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AI adoption is a marathon not a sprint race

Fri, 26 Apr 2024 08:23:19 GMT

Lessons learned and suggestions.

More than a year after the launch of ChatGPT, companies are still facing the same question when they first considered the technology: How do they actually go about putting it into business use? Many companies have simply discovered that generative AI tools like LLMs, while impressive, aren’t plug-and-play. Companies should consider a few suggestions when thinking of whether and how to onboard these tools:

1) choose performance over novelty,

2) combine GenAI with tools like vector databases,

3) never forget the human-in-the-loop,

4) trace your data,

5) have realistic expectations.

In the nearly year and a half since the release of ChatGPT 3.5, both businesses and individuals alike rushed to explore generative AI (GenAI) technologies. For many, there was a palpable fear of missing out on the next big thing, of being overtaken by competitors who were able to crack the code of using it to revolutionize their businesses, or being caught flat-footed by sweeping, industry-wide change. Report after report has touted the transformative power of GenAI across industries and its implications on the future of work. Adding more heat to the fire, media articles continuously reminded us that jobs would likely be lost at scale and speedily .

Today, the GenAI frenzy has seemingly calmed — at least marginally. Many companies are still facing the same questions they were a year ago: How can they take advantage of the promised cost savings and substantial efficiency gains that GenAI allegedly offers? How do they actually go about putting it into business use?

From our front-seat rows in helping companies to adopt and use AI, we see many struggling. There are a few reasons for this.

First, many businesses, large and small, are still grappling with how to integrate traditional AI — such as rule-based algorithm and machine learning — into their operations. At best, they are in an exploratory phase with traditional AL, and at worst they’re simply feeling lost. A recent study suggested that more than 70% of the large companies surveyed were still wondering how to reap the potential benefits that AI can offer.

Second, GenAI is far more complex and is geared to serve specific purposes. While it is able to write a 5,000-word report in no time, it cannot, for example, do a basic data entry task, like extracting and classifying driver’s license data, that traditional AI can do easily. As such, companies need to think deep and hard about which business cases might be appropriate to find the benefits of GenAI. Navigating through traditional AI is like steaming through choppy waters with a state-of-the-art but somewhat cumbersome vessel, and GenAI adds more tonnage, power, and an even more turbulent sea. A company still unsteady with the former will of course struggle with the latter.

Third, the longer-term implications of adopting GenAI — such as the long-term costs and the impacts of current and future regulation — are still uncertain. To us, the current situation throws us back to just before the millennium. While companies back then may have seen the need for setting up websites, few could clearly see the specific roles that the wider internet would play as an integral part of omnichannel strategies, let alone across devices and as phone apps.

Given all this, it makes sense that most companies are still searching for a path forward (even if it can feel like everyone else has it figured out). That doesn’t mean the search is folly. Here’s how companies can get their bearings and figure out what to do next.

The Market for GenAI

The first decision most companies have to make is which GenAI product they want to use. Right now, there are now a lot of GenAI suppliers — both industry titans such as Meta and Alphabet and newcomers like Hugging Face, Anthropic, and Stability.ai. This market is set to become even more crowded, with data-rich corporations such as Bloomberg and JPMorgan Chase signaling their intention to enter the fray , and an Apple working on its own offering, called Ajax. There are a few factors companies should consider.

For one, Open AI and its current rivals are now vying to be solution developers’ top choice of GenAI, and the latecomers may have already missed the boat. OpenAI’s recent introduction of a simple tool for creating ChatGPT-powered apps is likely an attempt to consolidate its position, as users who are accustomed to one system are likely to use it again in future endeavors. With the largest — and arguably the best — GenAI in the market, OpenAI is in the best position to establish an ecosystem.

That said, solution developers will likely not want to swear allegiance to any of the GenAI makers as to retain the option of being able to select GenAI for different projects. This in turn has given rise to toolkits such as LangChain, an open-source platform designed to enable users to work across different GenAI at the same time.

The competition at play between different GenAI companies somewhat resembles the early days of the duel between iOS and Android. A large ecosystem would enable OpenAI to remain the (money-making) market leader for a few years until the its rivals are able to join force. This does not necessarily mean that Apple and Google would unite to compete with Microsoft. More likely, we would see rivals agreeing on the same standard to collaborate on in order to stand against OpenAI’s dominance. We believe this is not dissimilar to the situation in 2015 in which Android’s supporters were finally able to establish a meaningful ecosystem to rival iOS. As the GenAI market consolidates, we can expect to see two to three large factions competing among themselves. Expect more companies, big tech and startup alike, to redouble their efforts to be at the heart of these systems.

Key Considerations to Take Advantage of GenAI

Given this current state of affairs, how could businesses onboard GenAI? Here, we would like to offer a few suggestions:

1) Choose performance over novelty.

In our long experience working with GenAI, its performance doesn’t stem from human-like text responses in a conversational manner or a model that is trained on a vast amount of data. To get the best out of GenAI, you must ask whether it’s the right technology for a particular task or goal.

For instance, while ChatGPT is (for the moment) better at handing words and languages, we have found that traditional deep-learning models deliver much better results at processing images. Another discovery: In one product we are building, we found that ChatGPT-4 is better at “understanding” users’ queries, while version 3.5 is faster and better at converting processed output into responses to users.

In other words, instead of unquestioningly embracing the latest AI technology, companies must understand the business problems that they are trying to solve and find the most suitable AI tool based on both the strengths and weaknesses of each of available options.

2) Combine GenAI with the power of vector database.

This is a new form of database that specializes in retrieving the closest matching records to best answer specific queries (as opposed to traditional databases that merely hold the records). Companies can use an GenAI such as ChatGPT to break down users’ queries, and then use a vector database to look for the best answers that match those parameters.

Consider an analogy: If you were interviewing for a job, ChatGPT and its competitors would offer the ability to “read the room,” analyzing the interviewers’ posture, facial expressions, word choices, and tones. Vector databases, on the other hand, would act like your memory and wisdom banks, constituting the capability to come up with the best things to say.

Put differently, GenAI by itself may not be sufficient. Depending on the problems to be tackled, it can only be half of the technology solution. The need for vector database to make GenAI truly useful means companies should expect to face even more complexity and long lead time when putting the solution together.

3) Never forget human-in-the-loop.

As ever, no matter how powerful AI technologies seem to be, their abilities are only as good as how much humans are involved. This is no different for GenAI. Humans play a critical role in guiding GenAI toward business goals, managing interactions within IT systems, designing the actions required for data going to and coming out of AI models as well as mitigating hallucinations — the made-up or outright false information produced by GenAI — that remains a major problem of GenAI today.

4) Trace your data.

While the problem of hallucinations remains rampant, it’s important to establish a clear trail from data source all the way to end-users. Traceability enables users to know the original source of data, which bolsters reliability and trustworthiness of the GenAI output, thereby creating a stronger foundation for informed decision-making.

Companies need to ensure that data lineage is a prominent feature in both their technology stacks as well as processes and workflows. Only in this way companies can be in full knowledge that they are using the right kind of data.

5) Have realistic expectations.

GenAI is a fast-traveling ship with a lot happening below deck. It is hard to know exactly what, how much, and how quick GenAI companies can realistically achieve. Believing with conviction that it can yield immediate results and outstanding financial returns will most likely lead to disappointments. Leaders must recognize that the exploratory and experimental journey of GenAI will likely be a long one.

The utilization of GenAI technologies in business operations transcends a mere technological investment; it’s fundamentally a business imperative. Hard as it is as an undertaking, to onboard GenAI in company operations is to understand the nuances of the current GenAI developments and have a keen awareness of the challenges presented. Yet, for those businesses that can successfully make use of GenAI to reach their business goals, the rewards can only be both promising and huge.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI trade-off: managing opportunities and risks

Fri, 01 Dec 2023 10:22:45 GMT

Generative AI is developing fast, and companies will have to balance pace and innovation with caution.

The board’s role is to constructively challenge the management team to ensure this happens, keeping the organization at the forefront of this latest technological development yet intensely mindful of the risks. The questions posed here are not, of course, exhaustive, and more will arise as the technology progresses. But they are a good place to start. Ultimately, board members hold responsibility for how generative AI is used in the companies they oversee, and the answers they receive should help them meet that responsibility wisely .

We recently provided a view of how CEOs might start preparing for what lies ahead . 1 But what is the role of the board? Many board members tell us they aren’t sure how to support their CEOs as they grapple with the changes that generative AI has unleashed, not least because the technology seems to be developing and getting adopted at lightning speed.

The early use cases are awe inspiring. A software developer can use generative AI to create entire lines of code. Law firms can answer complex questions from reams of documentation. Scientists can create novel protein sequences to accelerate drug discovery. But the technology still poses real risks, leaving companies caught between fear of getting left behind—which implies a need to rapidly integrate generative AI into their businesses—and an equal fear of getting things wrong. The question becomes how to unlock the value of generative AI while also managing its risks.

Board members can help their management teams move forward by asking the right questions. In this article, we provide four questions boards should consider asking company leaders, as well as a question for members to ask themselves.

Questions for management

Generative AI models—deep learning models trained on extremely large sets of unstructured data—have the potential to increase efficiency and productivity, reduce costs, and generate new growth. The power of these “foundation” models lies in the fact that, unlike previous deep learning models, they can perform not just one function but several, such as classifying, editing, summarizing, answering questions, and drafting new content. This enables companies to use them to launch multiple applications with relative ease, even if users lack deep AI and data science know-how.

Board members can equip their C-suite to harness this potential power thoughtfully but decisively by asking the following four broad questions.

How will generative AI affect our industry and company in the short and longer term?

Forming any sensible generative AI strategy will require an understanding of how the technology might affect an industry and the businesses within it in the short and longer term. Our research suggests that the first wave of applications will be in software engineering, marketing and sales, customer service, and product development. As a result, the early impact of generative AI will probably be in the industries that rely particularly heavily on these functions—for example, in media and entertainment, banking, consumer goods, telecommunications, life sciences, and technology companies.

Even so, companies in other industries should not delay in assessing the potential value at stake for their company. The technology and its adoption are moving too fast. Recall that the public-facing version of ChatGPT reached 100 million users in just two months, making it the fastest-growing app ever. And our research finds that generative AI can increase worker productivity across industries, adding up to $7.9 trillion in value globally from adoption of specific use cases and the myriad ways workers can use the technology in everyday activities. Each company will want to explore immediate opportunities to improve efficiency and effectiveness. Those that don’t may quickly find themselves trailing behind competitors that answer customer queries more accurately and faster or launch new digital products more rapidly because generative AI is helping write the code. They risk falling behind on the learning curve, too.

Simultaneously, companies will want to begin looking further out. No one can predict the full implications of generative AI, but considering them is important. How might the competitive environment change? How might the business benefit, and where does it look vulnerable? And are there ways to future-proof the strategy and business model?

Are we balancing value creation with adequate risk management?

An assessment of the new frontiers opened by generative AI will rightly make management teams eager to begin innovating and capturing its value. But that eagerness will need to be accompanied by caution, as generative AI, if not well managed, has the potential to destroy value and reputations. It poses the same—and more—risks as traditional AI.

Like traditional AI, generative AI raises privacy concerns and ethical risks, such as the potential to perpetuate bias hidden in training data. And it heightens the risk of a security breach by opening up more areas of attack and new forms of attack. For example, deepfakes simplify the impersonation of company leaders, raising reputation risks. There are also new risks, such as the risk of infringing copyrighted, trademarked, patented, or otherwise legally protected materials by using data collected by a generative AI model.

Generative AI also has a propensity to hallucinate—that is, generate inaccurate information, expressing it in a manner that appears so natural and authoritative that the inaccuracies are difficult to detect. This could prove dangerous not just for companies but for society at large. There is widespread concern that generative AI could stoke misinformation, and some industry experts have said it could be as dangerous to society as pandemics or nuclear war if not properly regulated. 4

Companies will therefore need to understand the value and the risks of each use case and determine how these align with the company’s risk tolerance and other objectives. For example, with regard to sustainability objectives, they might consider generative AI’s implications for the environment because it requires substantial computing capacity.

From there, boards need to be satisfied that the company has established legal and regulatory frameworks for the knowable generative AI risks assumed across the company and that AI activities within the company are continually reviewed, measured, and audited. They will also want to ensure mechanisms are in place to continually explore and assess risks and ethical concerns that are not yet well understood or even apparent. How, for example, will companies stand up processes to spot hallucination and mitigate the risk of wrong information eliciting incorrect or even harmful action? How will the technology affect employment? And what of the risks posed by third parties using the technology? A clear-eyed early view on where problems might lie is the key to addressing them.

The bottom line is that AI must always be subject to the effective oversight of those designing and using it. Support for the effort can come from government regulatory frameworks and guidance being developed on how to use and apply generative AI. It will be important for companies to keep abreast of these.

How should we organize for generative AI?

Many companies took an experimental approach to implementing previous generations of AI technology, with those keenest to explore its possibilities launching pilots in pockets of the organization. But given the speed of developments within generative AI and the risks it raises, companies will need a more coordinated approach. Getting stuck in pilot mode really isn’t an option. Indeed, the CEO of one multinational went as far as to ask each of his 50 business leaders to fully implement two use cases without delay, such was his conviction that generative AI would rapidly lend competitive advantage.

Company leaders should consider appointing a single senior executive to take responsibility for the oversight and control of all generative AI activities. A smart second step is to establish a cross-functional group of senior people representing data science, engineering, legal, cybersecurity, marketing, design, and other business functions. Such a team can collaborate to formulate and implement a strategy quickly and widely.

Bear in mind too that a foundation model can underpin multiple use cases across an organization, so board members will want to ask the appointed generative AI leader to ensure that the organization takes a coordinated approach. This will promote the prioritization of use cases that deliver fast, high-impact results. More complex use cases can be developed thereafter. Importantly, a coordinated approach will also help ensure a full view of any risks assumed.

The board will also want to check that there’s a strategy for establishing what is likely to be a wide range of partnerships and alliances—with providers that customize models for a specific sector, for example, or with infrastructure providers that offer capabilities such as scalable cloud computing. The right partnerships with the right experts will help companies move quickly to create value from generative AI, though they will want to take care to prevent vendor lock-in and oversee possible third-party risks.

Do we have the necessary capabilities?

To keep pace with generative AI, companies may need to review their organizational capabilities on three fronts.

Technology

The first front is technology. A modern data and tech stack will be the key to success in using generative AI. While foundation models can support a wide range of use cases, many of the most impactful models will be those fed with additional, often proprietary, data. Therefore, companies that have not yet found ways to harmonize and provide ready access to their data will be unable to unlock much of generative AI’s potentially transformative power. Equally important is the ability to design a scalable data architecture that includes data governance and security procedures. Depending on the use case, the existing computing and tooling infrastructure might also need upgrading. Is the management team clear about the computing resources, data systems, tools, and models required? And does it have a strategy for acquiring them?

Talent

The introduction of generative AI, like any change, also requires a reassessment of the organization’s talent. Companies are aware they need to reskill the workforce to compete in a world where data and AI play such a big role , though many are struggling to attract and retain the people they need. With generative AI, the challenge just got harder. Some roles will disappear, others will be radically different, and some will be new. Such changes will likely affect more people in more domains and faster than has been the case with AI to date.

The precise new skills required will vary by use case. For example, if the use case is relatively straightforward and can be supported by an off-the-shelf foundation model, a generalist may be able to lead the effort with the help of a data and software engineer. But with highly specialized data—as might be the case for drug development—the company may need to build a generative AI model from scratch. In that case, the company may need to hire PhD-level experts in machine learning.

The board will therefore want to query leadership as to whether it has a dynamic understanding of its AI hiring needs and a plan for fulfilling them. Also, the existing workforce will need to be trained to integrate generative AI into their day-to-day work and to equip some workers to take on new roles. But tech skills are not the only consideration, as generative AI arguably puts a premium on more advanced analytical and creative skills to supplement the technology’s capabilities. The talent model may therefore need to change—but with consideration of a caution raised recently at the World Economic Forum: using AI as a substitute for the work of junior-level talent could endanger the development of the next generation of creators, leaders, and managers. 5

Organizational culture

Finally, a company’s culture shapes how well it will succeed with generative AI. Companies that struggle with innovation and change will likely struggle to keep pace. It’s a big question, but does the company have the learning culture that will be a key to success? And does the company have a shared sense of responsibility and accountability? Without this shared sense, it is more likely to run afoul of the ethical risks associated with the technology.

Both questions involve cultural issues that boards should consider prompting their management teams to examine. Depending on what they find, reformulating a company’s culture could prove to be an urgent task.

A question for the board

As boards try to support their CEOs in creating value from generative AI and managing its risks, they will also want to direct a preliminary, fundamental question to themselves: Are we equipped to provide that support?

Unless board members understand generative AI and its implications, they will be unable to judge the likely impact of a company’s generative AI strategy and the related decisions regarding investments, risk, talent, technology, and more on the organization and its stakeholders. Yet, our conversations with board members revealed that many of them admit they lack this understanding. When that is the case, boards can consider three ways to improve matters.

The first option is to review the board’s composition and adjust it as necessary to ensure sufficient technological expertise is available. In the past, when companies have struggled to find technology experts with the broader business expertise required of a board member, some have obtained additional support by setting up technology advisory boards that include generative AI experts. However, generative AI will likely have an impact on every aspect of a company’s operations—risk, remuneration, talent, cybersecurity, finance, and strategy, for example. Arguably, therefore, AI expertise needs to be widespread so that the full board and all its committees can properly consider its implications.

Second, the board can improve its members’ understanding of generative AI . Training sessions run by the company’s own experts and by external experts on the front line of developments can give board members an understanding of how generative AI works, how it might be applied in the business, the potential value at stake, the risks, and the evolution of the technology.

Third, the board can incorporate generative AI into its own work processes. Hands-on experience in the boardroom can build familiarity with the technology and appreciation of its value and risks. Moreover, because generative AI can improve decision making, it would be remiss of boards not to explore its potential to help them perform their duties to the best of their ability. For example, they might use it to surface additional critical questions on strategic issues or to deliver an additional point of view to consider when making a decision.

Boards are responsible for how generative AI is used at the companies they oversee. Asking company leaders the right questions will help unlock the technology’s value while managing its risk.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

GenAI readiness, impact, and urgency

Fri, 22 Sep 2023 08:53:49 GMT

Don't forget, historically it has taken about five to seven years for a disruptive firm to change industry models.

Companies are struggling with where to start with generative AI. This framework can help leaders identify how vulnerable their business is to changes from this new technology and plan their response.

To best understand generative AI (GenAI), look at it through bifocal lenses. Through the top lens, one sees the long view of big, looming issues, such as accuracy, privacy, and bias, as well as GenAI’s potential impact on “knowledge workers” and even economy-wide job losses and societal risks . While this view is important, we have heard over and over that it’s not helpful to executives and board members because it’s difficult to translate from the generic insight that millions of jobs will be impacted to what it means for their businesses today.

That is why we also recommend looking at GenAI through the bottom lens, and taking in what’s directly ahead. Here, leaders can find the immediate opportunities and threats from GenAI that firms face today. In taking this view, however, leaders may also need to adjust what they’re looking for.

Our case studies, based on our growing global community of over 3,000 GenAI practitioners, point to a new category of work, more precise and actionable than “knowledge work.” We call it WINS Work: the places where tasks, functions, possibly your entire company or industry are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). Heart surgeons and chefs are knowledge workers but not WINS workers. Software programmers, accountants, and marketing professionals are WINS workers.

GenAI has the potential to be power tools for WINS work. It can generate new prose, computer code, images, narration, music, and videos as well as ingest and summarize, critique, improve, and reformat almost any manner of document or analysis. Every WINS task, subprocess, and end-to-end process within your enterprise (and in many cases the entire enterprise) should be evaluated for potential leverage with GenAI.

How Urgent Is It for My Firm to Pay Attention to GenAI?

We believe the easiest way for companies to proceed is to ask themselves two simple questions:

How much of our cost base is made up of WINS work?
How digitized are the WINS inputs today?

Plotting your company on a 2×2 matrix, ask yourself where it falls. The top-level categorization can be accomplished by looking at the cost base of the firm overall and then by function, and estimating the percentage of work that is WINS work. For example, software development, customer service, marketing, and R&D are just four areas with high levels of WINS work. Here’s a closer look at each competitive position.

In the Crucible

Industries with a high percentage of cost in WINS work and that are highly digitized are “In the Crucible” and must understand and embrace GenAI immediately. Software, entertainment, professional services, financial services, education, and others are In the Crucible, because competitors who adopt GenAI rapidly will be better, faster, and cheaper.

The tools of GenAI provide new, creative answers and expressions for everything from resumes to marketing. Think of it like the power of photography in portraiture. While portrait painting was previously available only to the wealthy — an artifact created slowly by highly trained craftspeople — photography expanded the market radically, transforming the entire idea of a portrait (think about a selfie) and its economics. In the hands of great talent, photography became a new form of art. Likewise, software development, script writing, film production, tax filing, accounting, etc., are likely to be under significant cost pressure — and in time will undergo wholesale transformation. These activities may or may not become totally automated, but just as you’d never hire an accountant today who doesn’t use Excel, having GenAI capability may become table stakes for many tasks.

Holding a Lever

Companies that are “Holding a Lever” are able to gain advantage in cost, time, and quality, even if their cost base is not heavily weighted toward WINS work and their customer end product or deliverable is also not WINS nor digitized. For example, Moderna has recently required all employees be trained in GenAI tools. They believe it is a fundamental skill to drive WINS worker productivity even though their product is a molecule or treatment intervention. In our GenAI learning community, we have found GenAI is excellent for supporting bid preparation when responding to an RFP. Speed of sale is a critical performance variable for all, even those firms with few WINS workers. Many SG&A functions, key aspects of R&D, and even the entire end-to-end product development and supply functions can leverage GenAI.

Next in Line

The category of “Next in Line” in our framework may provide an opportunity to take tasks that are not digitized today and digitize them to create opportunity. For example, many leading home décor companies are investing in what they are calling their “digital front door,” enabling customer engagement in the identification and purchase process. GenAI will enable new levels of customization to help customers take actions to envision home furnishings in much more realistic and imaginative ways, leading to a better experience, greater customer uptake, and far fewer returns for those companies that are in the forefront.

In the Balcony

For companies that are “In the Balcony,” we see low digitization and limited WINS work as characteristic of the value creation process today. These are often industries with high amounts of low-skilled labor or, when high skill is involved, the nature of the skill is more in the creation of a physical product or service. Figure 2 gives a sample of industries in each of the four quadrants.

Unlike much CapEx and technology spend that takes several years to see a return, GenAI, even at this early stage, can often be accretive to EBITDA within the year it is adopted, because the near-term productivity boost is so compelling. Over time, these initiatives may birth strategic investment opportunities to create defensible assets or competitive moats.

Legal and Risk Issues

When using these tools, it’s vital to have human review of important and high-risk decisions. Today’s GenAI models can sometimes “hallucinate” and give wrong answers. Therefore, for now, staff should use it to augment — not fully automate — high-risk tasks. In time, innovators will figure out ways to improve and augment the core models to improve accuracy. In addition, if you are using one of the open access models like ChatGPT, have clear policies as to when you will allow data and queries to be part of their knowledge base and when will you keep it private.

Where to Start

To get your GenAI initiative moving, we suggest the following approach:

1. Get fully educated on the entire suite of GenAI tools that can drive productivity, change, and innovation in your company and industry. This is not a classroom exercise. This is more akin to swimming. You cannot learn to swim by listening to a lecture or watching a video. Roll up your sleeves, dive into the pool, and swim laps. Learn how the tools work and what they can do.

2. The board/CEO should appoint a cross-functional team to start at the task, subprocess, and process levels for practical experiments and report on progress.

3. Have a cross-functional business/technology/finance team examine whether and where you could generalize the lessons of your early experimentation. Discover where you “hold the lever.”

4. Perform a strategic review of the cost drivers in the WINS category and current digitization to assess how urgent it is to invest broadly in GenAI. Find out if your company is “In the Crucible.”

5. If you discover you are “In the Crucible” or “Holding a Lever,” create a test-and-learn strategy linked to a six-to-24-month program of improvement. We believe that’s how much time you have before competitive intensity increases in your industry.

6. If you are “Next in Line,” get smart on GenAI and begin to move toward digitization of all WINS work so you are ahead of the curve. You are next to be “In the Crucible,” and if you don’t transform your industry, someone else will.

7. For those “In the Balcony,” continue to learn. While things are less imminent for you, GenAI tools will come along to make your business easier and faster, much like Excel and Word replaced calculators and typewriters.

Looking at GenAI Through Bifocal Lenses

As we said in the beginning, we advocate that all business leaders should put on their GenAI bifocals and look not only into the distance through that top lens, but perhaps more urgently and importantly, look through the bottom lens to see what’s near. Historically it has taken about five to seven years for a disruptive firm to change industry models. Five years after Uber’s 2009 founding, taxi medallion prices in NYC peaked at about $1,000,000 in 2014. By 2017 their value was $250,000 or less and continued down.

Firms with heavy reliance on WINS work need to act today to fend off stiffer competition and to overcome disruptive competitors within 36 to 60 months. Don’t be caught with high costs, old processes, a data disadvantage, fleeing talent, and expensive capital.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to find the strategic shareholders.

Sun, 03 Sep 2023 06:02:31 GMT

Don't approach investor relations as a marketing exercise.

Many companies go about investor relations all wrong, pitching their companies and plans to whatever audience they can and hoping some shareholders will buy in. This approach wastes time and valuable resources building relationships with the wrong shareholders who do not bring the right competencies, connections, and commitment to a business. Managers tasked with investor relations often believe their role is to “sell” the business — or the strategy the company pursues — with the sole goal of retaining and attracting as many shareholders as possible. Rather than selling a strategy in investor relations, managers need to think strategically about investor relations and the right shareholders they need for their business. By co-analyzing a company’s strategy with the shareholder landscape, managers can identify and attract strategic shareholders, who can help their business thrive. We have found this is best done with a five-step approach to strategic shareholder management.

Many managers today approach investor relations as a marketing exercise: pitching the company’s business and strategy to as broad of audience as possible in anticipation that some shareholders will purchase the company’s stock. They hope this will further boost the stock price and keep the company’s directors and other shareholders satisfied. In other words, investor relations often uses a dragnet, whereby managers cast nets into an ocean of shareholders in hopes that they catch some of them.

Companies should instead apply a targeted approach to investor relations with the aim of finding strategic shareholders. Strategic shareholders are those that stand to bring the greatest competitive edge to a business — not just their capital and contentment. To execute and support winning strategies, companies today need strategic shareholders more than ever.

Our research shows that having the right shareholders in a business can help a company achieve its strategic goals and amplify value creation to outpace its competitors. In essence, the right shareholders can in themselves create a competitive advantage for the companies in their portfolios. Given the current approach that many companies use for investor relations, this may seem hard to fathom, but it’s true, and we explain why and how winning companies do this.

What are Strategic Shareholders?

Well-crafted competitive strategies enable companies to outperform rivals in product markets. These strategies typically require three things.

First, companies must develop valuable and hard-to-imitate competencies, such as positive reputations, technological expertise, and logistics excellence, which provide sources of competitive advantage. Second, successful competitive strategies require companies to manage dependencies with key stakeholders, including regulators, activists, and the media. Third, companies must establish business connections with strategic partners that can support the strategy, such as an alliance partner in a foreign market. When done well, these three factors will facilitate a company’s competitive strategy and its dominance over competitors.

Some shareholders, we have found, can provide the three inputs necessary to build and execute a successful competitive strategy. That is, they provide the means to build valuable competencies, manage key dependencies, and facilitate the business connections companies need in competitive markets.

Competency builders

Some shareholders can help companies develop competencies. Private equity investors are known for building concentrated portfolios in companies in sectors in which they have deep expertise. To win over other shareholders, activist investors need to conduct rich research to thoroughly understand an industry, which managers can then tap into. From our many discussions with directors, it is evident that activists appoint some of the most knowledgeable and capable directors available. Even big investors are structured into groups that develop rich insights in a domain, which can be shared or transferred among firms.

By tapping into the insights of investors, companies can learn in advance about emerging industry trends, new technology opportunities, potential geopolitical shocks, and customer inputs. Companies can also access valuable human capital from investors. Some investors have deep networks and recruitment expertise that savvy companies in their portfolios may utilize to find new talent.

Dependency managers

Shareholders can also manage dependencies for companies. Companies are dependent on a host of stakeholders to maintain and build their business, including regulators, activists, rating agencies, politicians, the media, and so forth.

Using big data, our research shows that shareholders can help companies manage dependencies with these key stakeholders. For example, we have found that companies receive better more positive coverage from media outlets that their investors own. This results in these companies being cast in a better light — to both the companies’ and shareholders’ benefit. Similarly, we have found that companies receive more favorable ratings from rating agencies that their investors own.

The same could be argued for relationships with regulators, politicians, NGOs, and others: having investors that have some control over these stakeholders can benefit these investors’ portfolio companies. It is up to savvy managers to attract and retain such connected investors.

Connection makers

Finally, shareholders can facilitate new business connections for companies. Many shareholders today have broad portfolios that connect them to many different companies. Shareholders that understand these companies well can play a brokerage role that enables them to make different connections between companies in their portfolios.

One study finds that shareholders facilitate mergers and acquisitions (M&A) by helping their portfolio companies identify M&A targets in other areas of their portfolio. The same is true for strategic partnerships, licensing deals, and buyer/supplier contracts. Tapping into the network of investors can be especially beneficial for companies that are looking to enter new product or geographic markets, and for companies that are looking to collaborate to erect barriers that make it harder for new entrants to compete.

Some venture capital funds are especially good at keeping up with trends and identifying promising upstarts with innovative and sometimes disruptive business ideas. Leading companies we’ve worked with have developed relationships with venture capital partners to gain access to their innovation insights and broader portfolio of firms, which could lead to direct corporate investment.

A Limited Pool

Importantly, not all shareholders offer all three of these advantages — or even any advantage. This is precisely the problem with current “dragnet” approaches to investor relations: companies attract shareholders that fail to add strategic value to the unique business and strategy.

Adding a further challenge, when managers understand the value that some key shareholders can offer, those shareholders are going to be quickly sought after. And once these shareholders’ resources are exhausted, having been invested in competitors, some companies will find themselves far behind, forced to abandon the benefit of having strategic shareholders.

Though the pool is limited, managers should not lose hope. A critical opportunity comes from the fact that companies pursuing different strategies will have a different set of shareholders that can be considered strategic — the shareholders that add value to their unique business.

Targeting Strategic Shareholders

So how do companies identify these strategic shareholders and obtain the benefits they can provide?

The first step is to shift the mindset of investor relations programs. As we noted, managers will benefit by not approaching investor relations as an exercise to attract whatever shareholders can be sold on the company’s strategy. Moreover, the goal of investor relations is not to simply “placate” shareholders, but to tap into the expertise and connections that shareholders offer. Most companies use their investor relations department, if one exists at all, to field phone calls from shareholders, track shareholder transactions, and keep a pulse on the market to identify when an activist may come knocking. The focus is primarily on defense, with limited emphasis on offense. And the offense that does exist is mainly oriented to attracting what is popularly deemed “patient capital,” money from shareholders with long horizons who will sit back and not cause trouble. Sadly, many management teams hope their shareholders will not cause a stir or offer their opinions. This mindset creates a hurdle to attracting the most beneficial shareholders — the strategic ones — and tapping into the benefits they offer.

The second step is to ensure all investor relations personnel know in depth the company’s strategy needs. During conversations with investors, managers need to look for opportunities to tap into their expertise, and this can best be done when those managers know what to look for based on what their business needs — new competencies, certain dependencies managed, or new connections. This is typically easier for CEOs and senior strategy officers, and harder for some directors and investor relations personnel who are not deeply involved in the company’s strategy formulation process. Structurally, this can be overcome by moving investor relations closer to corporate development.

Third, managers need to identify the list of shareholders who will bring the necessary inputs for their business. This requires mapping the current shareholder base to identify gaps and regularly surveying the shareholder landscape to identify new shareholder prospects. For this, we have found big data can help to identify shareholders with unique portfolios (e.g., concentrated deeply in certain sectors) or positions (e.g., a large stake in a leading media company) that might be of use. From here, we encourage companies to develop detailed shareholder profiles of those that are deemed to be of strategic importance.

Fourth, once identified, shareholders with promising expertise or connections need to be engaged. Depending on what is being sought by management, it can help to move beyond meetings with the investor’s portfolio managers and seek more senior affiliates. Engagement can be tricky, and it helps when managers and directors choose the right forum, where both sides are comfortable but engaged. We have found face-to-face meetings in the company’s or investor’s office work best — rather than investor conference calls or roadshows. Like others, shareholders want to express their ideas and believe they can help. In some cases, when a shareholder is of sufficient strategic use, the nominating committee of the board may offer them a board seat.

It also helps to approach these engagements with care and a healthy amount of criticism. Currently, most managers approach shareholders as many job candidates do when seeking employment: putting all their concern on selling themselves and forgetting to ask questions of the employer to ensure there is a two-way fit. Managers need to learn about shareholders to gauge their richness and depth of expertise and what value they might bring to the business. Once that value is deemed positive, then it is time to go into selling mode and try to get the shareholder to buy into the company, turning them from a passive asset into a source of competitive advantage.

Avoid Pitfalls

Despite the importance of strategic shareholders to a business, there are two notable pitfalls that managers need to be mindful of in managing relationships with these shareholders.

First, managers should not rely solely on expertise and connections provided by strategic shareholders prior to making strategic decisions. In one study we found that if managers choose to diversify into a new industry without doing their own due diligence because their strategic shareholders have themselves invested heavily in the industry, their decision to diversify can compromise the companies’ competitive edge and destroy shareholder value. The lesson: blindly imitating strategic shareholders will not produce a sustainable competitive advantage.

Second, managers need to be aware that strategic shareholders’ incentives can sometimes misalign with the interests of their portfolio companies. This occurs because shareholders with broad holdings prioritize their overall portfolio returns, not the value of any single company. Some studies warn, for instance, that when shareholders hold ownership simultaneously in industry rivals, they may pass proprietary information from firms in which they have less ownership to firms in which they have greater ownership. To avoid this risk, managers need to extensively analyze strategic shareholders’ holdings and broader interests, paying special attention to where these shareholders jointly own industry rivals.

Though most investor relations programs are designed to attract the broadest and largest set of shareholders, companies will unlock the full potential of their shareholder base only when they apply a more strategic approach to investor relations.The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

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Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI adoption and Finance industry: canary in the coal mine.

Sat, 02 Sep 2023 18:27:23 GMT

It's all about soft vs. hard data.

What will artificial intelligence do to industries and jobs? For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology. The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. But it may not leave the overall system better off.

The meteoric rise of artificial intelligence (AI) in the public conscience has caused many people to question what an AI-dominated future looks like. Will AI transform industries? If so, will it democratize or consolidate them? Will it create better or worse outcomes? Outlines of answers can be found in the world of finance which has been transformed in the last decade by the same forces driving AI: the diffusion of ever more powerful computing and the profusion of data. The experience of finance is both encouraging and sobering for an AI-dominated future. It suggests that AI will transform some (but not all) industries, that it will benefit larger players most, and that just as it makes individual players smarter, it may make the world dumber.

The world of finance is an obvious laboratory for exploring the potential effects of AI because information processing is the central function of financial markets. Unsurprisingly, financial institutions of all types invest heavily in technology and data well ahead of other industries in order to compete most effectively. Of course, the experience of finance may not fully illuminate the scope of newer large language models that have so impressed the world in the last six months. But the changing competitive dynamics within finance over the last decade provide clues about what will happen across many industries when AI becomes cheaper and more widely available. And regardless of how these newer versions of artificial intelligence play out, finance will always to be the canary in the coal mine for the rest of the economy.

First, it appears clear that AI can disrupt industry dynamics very quickly. Consider the asset management industry. Over the last 15 years, we have witnessed two significant disruptions that can be traced to the growing dominance of technology and data. First, the mutual fund industry has seen the rise of passive fund managers (i.e., managers who invest in indices with no analysis) and the decline of active fund managers (i.e., stock pickers). This shift has occurred remarkably quickly as data and technology made passive investing more competitive and made it more difficult for active managers to attain informational edges. In the last eight years alone, the ratio of passively-managed assets to actively managed assets has risen from 0.6 to 1.2 — a dramatic shift in market share. The ability of active fund managers to extract large fees (upwards of one percentage point of assets under management) has been clobbered as passive fund managers demonstrated their ability to approximate many active fund management strategies at one-tenth of the cost.

Second, the hedge fund industry has been transformed by the growing dominance of quantitative investing over traditional, fundamentals-driven long-short strategies. The ability to analyze large amounts of data quickly and create relatively short-term strategies appears to be beating the slower and deeper analysis that traditionally led to long and short investment decisions. These trends in finance suggests that an AI-dominated future can create outsized winners and losers in very short order.

At the same time, the experience of the financial world suggests that not everything changes as quickly as people predict. While the high-frequency world of financial trading with its confluence of macroeconomic, sentiment, and company-specific information has changed rapidly, the lower frequency worlds of wealth management and lending have changed considerably less.

The much anticipated ability of robo-advisors to eclipse the massive financial advisory complex has appeared to stall and may be reversing . It appears that the client side of finance retains a preference for humans. Lending, similarly, has not been transformed by AI nearly as much as was predicted and AI-powered lenders have faced considerable problems . The incremental amount of additional data to be processed on individuals and business credit may just not be as large or as useful as in financial markets broadly.

The power of AI to disrupt industry dynamics appears to be tightly connected to the nature of the information problems being solved. Financial markets are a multi-dimensional information problem that requires massive amount of data and computing power. Fields with similar properties, like drug design, may be ripe for AI disruption. But many fields, including those in the services sector and manufacturing, simply may not have the same relevance for AI — they may be more like wealth management or lending. The experience of the finance industry suggests that human-facing services where data is not abundant and fast-changing can remain largely intact in a world of AI. To be clear, AI can still have a large impact by improving decision making but it is more likely to be incremental (as it has been in wealth management and lending) rather than transformational (as it has been in money management).

The world of finance can also help us understand if AI will be democratizing or consolidating. Here, it appears that the answer is less equivocal. Where AI has been pivotal (i.e., in financial markets), scale and speed appear to be the critical determinants of success. When technology and data come to dominate, winners keep winning and the ability to invest in technology and data is the key differentiator. A smaller quant fund has significant challenges in acquiring data feeds and computing power relative to established players. Similarly, fees for passive investing just continue to decrease as larger players share the benefits of scale with investors thereby boxing out upstarts. For sectors of the economy where AI is transformational, scale can be expected to be determinative and hopes for a great unleashing of smaller players that challenge established players appear to be overstated.

What can the finance industry’s experience tell us about whether AI is good for humans? Here, the experience of the world of finance is more sobering. The displacement of active managers who were charging large amounts for little excess performance seems like a positive development that is worth cheering. At the same time, it does not appear that financial markets are doing their central task — the processing of information — much better and it could be getting worse . The rise of investors that either willfully ignore information (passive investors) or obsess about fast-changing information (quant funds) means that the hard work of processing slow-moving, ambiguous, firm-specific information may be getting neglected. As data and computing come to dominate, industries may come to rely excessively on hard data that is fast-changing (e.g., stock price movements, real time credit card data on spending). Meanwhile, softer data (e.g., the future prospects of firms, the quality of management, the longer run consequences of pricing strategies) can be subordinated and diminished — even if it is what really matters for markets.

I fear this last lesson may generalize particularly well. The ability to analyze hard data in unstructured ways that are not directed by humans — the hallmark of AI — promises to transform the world in many ways, just as financial markets have been. But that transformation may be limited to settings where data is abundant and fast-changing. Moreover, the winners will be the largest firms able to invest in the computing power and data to create differentiated strategies. And the premium on the ability to consider softer data could fall in the short run even if, ultimately, it is what matters the most.

Can financial markets figure out how to capitalize on the wonders of AI and not neglect these more fundamental issues? The current equilibrium appears to be a financial market dominated by large players providing commodity services relatively cheaply but that neglects the processing of softer information. The challenge for the world of finance — and perhaps all of us — is to remember that the hardest questions facing managers and leaders are not entirely determined by hard data. What will allow my enterprise to succeed in 10 years? How can I deploy capital most effectively so that we innovate to create products and services that can serve our customers better? Hard data will inform these decisions but it is unlikely to be entirely dispositive. These decisions require acts of imagination and conviction. Just as the ability to use hard data cheapens and becomes more efficient via AI, it is these acts of judgment that will rise in importance. To acknowledge the primacy of these human questions does not diminish how much AI can help us — it simply reasserts that AI is merely a technology and that the greatest rewards for managers and investors rests in these fundamentally human endeavors.

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Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Orienting AI around customer love

Wed, 02 Aug 2023 14:48:50 GMT

Create value that’s hard to copy.

In a time of high inflation and tight economics, some managers may be tempted to use generative AI technology only to cut costs and improve efficiency. That would be misguided. While generative AI has the potential to bend the cost curve in many industries, the greatest value will come to those companies that focus on enriching the lives of their customers.

The surge of generative artificial intelligence (AI) applications is spurring exciting innovations and consumer experiments, but it also worries many people who are concerned about data privacy or only being able to communicate with a company through a bot. These concerns are especially acute in industries where customer interactions and data privacy are critical, such as banking or healthcare.

Some level of anxiety typically accompanies breakthrough technologies, and it’s natural to worry about a technology that mimics human intelligence. As this new class of large language models has emerged, however, most companies have put model risk, accuracy of the model’s output, and ethical use of data at the heart of their risk frameworks. They aim to ensure responsible uses of new AI technology.

Less appreciated is the risk that companies will cede the customer experience to models and bots designed to extract value in the short term, not to foster long-term customer loyalty. Companies might increasingly pair traditional AI and machine learning models with generative AI to deliver messages and offers to customers in more human-like ways. If we are not careful, profit-seeking bots, algorithms, and predictive models could indeed lead to dystopian experiences.

Even in the world of AI, customer love should lead the way. Traditional metrics of customer sentiment, such as Net Promoter Score (NPS), may start to look different, but one premise will endure: Every interaction enhances or diminishes a customer’s perception the company involved.

Informing each decision with the goal of enriching customers’ lives will lay down a reliable route to an AI-enabled future that creates more value for customers, employees, and shareholders. In fact, early published results from researchers at Stanford University and Massachusetts Institute of Technology show favorable effects from the rollout of an AI-based conversational assistant tool to 5,200 customer support agents in several countries. Not only did the tool raise agent productivity by 14% on average, but the AI-assisted interactions had higher average NPS, and monthly agent attrition dropped by 9%.

Make It Personal

Orienting AI around customer love requires a fundamental rethink of objective functions. Most existing algorithms optimize around ROI for a particular moment rather than around an entire experience. AI-enabled customer engagement holds the promise of a company learning more from each interaction and finding more ways to create value for customers.

That’s a good sign because customers increasingly expect more personalized, relevant experiences and are open to sharing their data in return. Bain & Company’s latest survey of almost 30,000 banking customers in 11 countries found that the respondents who agreed that their bank personalizes the experience are more likely to reward it with a higher NPS. There’s a 123-point difference in NPS between respondents who strongly agree that their bank interacts based on knowing who they are and those who strongly disagree.

One way AI refines personalization is through digital assistants for customers, as shown by emerging efforts in banking and payments. Royal Bank of Canada uses an AI-enabled assistant called NOMI to personalize digital money management for customers. Features include timely tips pushed to clients, personalized budgets, and savings recommendations based on spending behavior and cash flow. In the year following its launch, the results were promising, with 50% more digital interactions for NOMI customers relative to the entire customer base, 93% more time spent on financial accounts, and 2% attrition of NOMI customers vs. 8% for their peers.

Generative AI digital assistants are also helping employees to strengthen their customer connections, reinforcing the places where a human touch may be a source of differentiation. Morgan Stanley Wealth Management, for instance, is rolling out an AI assistant to help its thousands of financial advisers better support their clients in a personalized way. The assistant combines search and content creation so that financial advisers can quickly find and tailor the right information for each client at any moment.

Large language models will enable a new era of personalization. Machine learning techniques already turn each customer’s pattern of digital interactions into a unique behavioral “fingerprint,” and recent AI advances will now enable these fingerprints to include speech and text interactions.

Help employees help their customers

Companies should start with a few no-regrets cases to get the organization comfortable with generative AI technology. These typically use AI to help employees who deliver aspects of the customer experience so that humans can vet the model’s output. Examples include suggestions to relationship managers for the next conversation with a customer based on recent engagement or providing specific actions for handling collections with customers facing financial hardship.

The next wave of cases would feature AI embedded in standard operating procedures for employees. Promising cases include predictive routing of a customer’s inquiry to the agent best equipped to handle a particular issue or real-time script recommendations for relationship managers. The technology will listen to a customer call in real time and help agents know whether their interactions are creating a promoter or a detractor Other employee-supporting features in the near future could include devising a personalized offer with images and text that evoke a customer’s favorite hobby or reminding a relationship manager to call on a customer during key life stages.

In a few industries such as retail, a fully AI-enabled front line is starting to support automated engagement directly with customers. Over time, this digital front line could deliver service with the same thoughtful empathy as traditional human front lines. Bots will engage with customers and learn to serve up relevant products and information just as the best employees have always done. The best uses of AI may even completely reimagine the overall experience.

As companies learn to use generative AI to create value, there’s a risk that they take the wrong approach when applying the technology to the customer experience. In fact, research shows AI can help boost customer satisfaction when it’s used to offer customers more personalized solutions or to help human employees provide better service than they would without the technological assist. Some examples of companies experiencing early success with this are in the financial services industry.

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Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Value Creation and Digital Transformation

Wed, 02 Aug 2023 10:50:54 GMT

A digital and AI transformation cannot be done in “special project” mode. To pull this off, the entire organization must be able to deliver constant digital innovation, which requires a holistic set of capabilities. The effort is significant, but so is the reward.

While 89% of large companies globally have a digital and AI transformation underway, they have only captured 31% of the expected revenue lift and 25% of expected cost savings from the effort. Until business leaders are convinced of the value and confident in how to get it, they are unlikely to do the difficult, hands-in-the-dirt changes needed to improve their success rate. To see where digital transformation creates value, the authors used McKinsey’s Finalta benchmark, which tracked the performance of 80 global banks every year from 2018 to 2022 against a set of 50 normalized metrics, such as digital/mobile adoption, digital sales by banking product, number of people in contact centers, and number of branches. They found that digital leaders are creating much more shareholder value than laggards, often by creating value that’s hard to copy.

“Show me the money!” Cuba Gooding Jr., playing Rod Tidwell, made those words a cultural touchstone in the movie Jerry McGuire. He was not just voicing his concerns about committing to a sports agent, played by Tom Cruise in this case; he was also questioning Cruise’s commitment.

Business leaders, shareholders, and board members have increasingly been saying the same thing — albeit using different words — when it comes to their company’s digital and AI transformations. While 89% of large companies globally have a digital and AI transformation underway, they have only captured 31% of the expected revenue lift and 25% of expected cost savings from the effort.

That track record begs some tough questions: Is all this digital effort worth it? Do I really need to lead my industry or is being a fast-follower a smarter strategy? Can I create digital and AI capabilities that give me a lasting competitive advantage or is this just the price of doing business in the modern age?

Until business leaders are convinced of the value and confident in how to get it, they are unlikely to do the difficult, hands-in-the-dirt changes needed to improve their success rate, as we argue in our book Rewired: How to Outcompete in the Age of Digital and AI . But using proprietary data, we’ve found just how and where digital transformations create value — and what businesses can do to beat the competition.

Hard Evidence, Real Value

Hard evidence that directly ties digital and AI transformation to improvements in operational KPIs and financial performance is scant.

To redress this issue, we turned to banking, a sector that has enough of a history with digital transformations to produce meaningful findings and where we own a unique longitudinal dataset.

First, we used McKinsey’s Finalta benchmark, which tracked the performance of 80 global banks every year from 2018 to 2022 against a set of 50 normalized metrics, such as digital/mobile adoption, digital sales by banking product, number of people in contact centers, and number of branches. We then isolated performance in two metrics — the percentage of mobile adoption by their customer base and the percentage of sales originated in digital channels — to define 20 digital leaders and 20 digital laggards. These two metrics are broadly recognized in the industry as core indicators of a digital retail banking model.

Next, we combined this data with McKinsey’s Corporate Performance Analytics to see how banks ultimately perform against financial metrics (e.g., total shareholder return, growth, expenses). We then ran a blind assessment (i.e., the identity of the banks was hidden) of the maturity of digital and AI capabilities of the leading and lagging banks.

The findings have been striking: Digital leaders are creating much more shareholder value than laggards. Between 2018 and 2022, digital leaders achieved average annual total shareholder returns of 8.1% vs. 4.9% for laggards. Leaders also had significantly better return on pre-tax tangible equity (ROTE), growing it from 15.5% in 2018 to 19.3% in 2022, vs. a more modest growth from 13.6% to 15.3% for laggards.

This financial outperformance is a result of leaders’ success in growing revenue and better containing expense growth. Between 2018 and 2022, digital leaders have grown their active customer base at 0.5% and their retail revenues at 0.8% annually, while digital laggards saw zero growth in their active customer base and a decline of 1.4% per year on retail revenues. During the same period, leaders’ operating expenses grew at 1.3% per year, while laggards grew at almost twice that (2.3% per year). So, how are leaders able to outcompete so demonstrably?

Creating Value That’s Hard to Copy

Where does value come from? Let’s look under the “digital hood.” Both digital leaders and laggards are growing adoption of their mobile app at the same rate, with a gap of 14 to 15 percentage points between them staying constant over time. (See below chart.) This is not surprising. As soon as a bank introduces a new mobile app feature, others see it and follow suit relatively quickly. The mobile app is table stakes.

Turning to digital sales provides a much more insightful answer. Here, the gap between leaders and laggards is growing fast, with leaders almost doubling their advantage over laggards over the five-year period. In fact, digital leaders grew digital sales from 40% to 70%, while digital laggards grew from 8% to 17%.

The reason for this large differential is that to drive digital sales, leading banks go well beyond the mobile app to digitally transform what’s hard to see and hard to copy: the end-to-end process from origination to fulfillment to servicing. To do this, they must orchestrate hundreds of teams capable of developing digital and AI innovations, day-in, day-out, across all their customer journeys and core business processes.

At the front end of this process, for example, leading digital banks deploy personalization analytics and digital marketing campaigns to bring relevant offers to (potential) customers. In the middle of this process, they create an omnichannel experience where branch and contact center professionals have the tools and data to support customers at any stage of the sales journey, even if that journey was started online. These leading banks also provide customer approvals in real time, thanks to automated credit-risk decisioning. At the back end of the process, they drive customer self-servicing through well-designed digital workflows enabled by a modern data architecture.

The value of this approach to transformation is also revealed in contact center staffing. Laggards saw an increase of 20% over the past five years, as they were unable to contain inbound calls from customers that enter digital channels. In contrast, digital leaders were able to decrease contact center staffing by 11% as they benefited from their ability to fully fulfill customer demand online and provide effective self-servicing capabilities.

Knowing what to do is important, but executing on the “how” is what makes the difference. Let’s see how a U.S. bank did it for its secured lending business. Traditionally, the bank took about 45 days for a customer to secure a loan, on average. The process involved multiple documentation requests to customers (e.g., pay stubs, W2s, letters of explanation), and back-end processes (e.g., initial file review, file assignment, ad hoc reports) were highly manual.

To transform this journey, the bank’s leadership team reinvented the entire process. To speed pre-approvals, they developed a database of tens of millions of U.S. households combining credit, property and income attributes using internal and external data sources. This data allowed them to generate personalized pre-approved offers that customers could accept with one click. They built a mobile-first customer experience, where customers could personalize their offers based on real-time data and finalize a pre-filled application, either on mobile or with the assistance of a bank employee. They redesigned key processes (e.g., specialized loan “assembly lines”), automated key tasks (e.g., initial file scrub), and developed digital tools for operators to drive productivity (e.g., daily workflow management). And they modernized credit policy execution to enable greater use of data in underwriting (e.g., using direct deposit data for income), while maintaining or increasing risk controls for the bank.

To enable all these innovations, they implemented key technology and data capabilities, including a customer data platform, AI/ML models (e.g., propensity models), data products (e.g., income), a digital app for customers and a workflow tool for the fulfillment center, all deployed on a cloud-based platform-as-a-service infrastructure.

All in all, this transformation required more than a dozen use-cases across the entire journey and massive change management programs (e.g., training, retooling) for agents in branches, contact centers, and operations. But, only 18 months after the initial launch, the approval process was shortened from 28 to 7 days. This leap allowed the bank to become a leading secured lending originator and increase originations by 35%, while reducing origination cost by 20%.

The Capabilities Needed to Outcompete

A company that aspires to outperform needs to do the kind of end-to-end changes the bank above did across dozens of customer journeys and core business processes. That’s only possible when it is rewired with differentiated capabilities. Our study of more than 200 large-scale digital and AI transformations isolated the six core capabilities rewired companies develop:

Creating ambitious and focused transformation roadmaps . This requires business leaders to align their efforts on specific domains (e.g., journeys or processes) that matter to customers and generate significant value.
Building a quality digital talent bench . Leaders prioritize creating an environment that attracts top-notch engineers and allows them to thrive (e.g., tailored career tracks, autonomy).
An operating model where hundreds of small cross-functional “pods” made up of business, engineering, and resources from control functions are mobilized against priority solutions. A single journey (or product) owner responsible for the end-to-end experience.
A distributed technology environment and modern software engineering practices to allow the entire organization — not just IT — to develop digital and AI-based solutions.
Data products and modern data architecture that make it easy for different parts of the organization to consume data for their own applications.
Change management to ensures digital solutions are adopted and can scale by making them easy to use and reuse across the enterprise.

In our blind assessment of these capabilities for the leaders and laggards, we found that leaders stand out across the board on these capabilities. No single one explains their success. All are needed. With that baseline, the most differentiated capabilities are talent and operating model, not technology. Over time, these capabilities create ever-improving customer experiences and drive lower unit cost. Financial rewards follow.

While thye research has focused on banking, our experience reflects similar lessons and patterns in every industry, whether B2B or B2C, products, or services. A digital and AI transformation, however, cannot be done in “special project” mode. To pull this off, the entire organization must be able to deliver constant digital innovation, which requires a holistic set of capabilities. The effort is significant, but so is the reward.

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Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI is a catalyst

Wed, 02 Aug 2023 09:24:35 GMT

Like any business-planning exercise, think about your AI strategy in phases. Embrace agility and change, and keep a continuous learning mindset, calibrating and adjusting your gameplan as you go.

The AI roadmap will be different for every organization, and it looks different depending on whether you’re a tech company or not. Tech companies are more likely to have already implemented some form of intelligent agent into their software experiences, for example. But for everyone, the potential is massive, and the time to start is now.

Just like ride-sharing needed smartphones, there are as-yet conceived industries that will need AI to get off the ground. Most of us recognize that AI will be completely game-changing. We see the practical applications — not only for the tech landscape but for humanity, and that’s what’s truly profound about this.

The AI market is moving quickly, and the cycles in and around AI are faster than we’ve ever seen. Right now, there is tremendous opportunity for business leaders to embrace AI and adapt to the profound changes that are coming. There is exponentially greater opportunity for the businesses that use AI to lead and drive that change.

Recently, like millions of people, I used a ride-sharing app on my smartphone. It was pretty uneventful and not something I gave much thought. Ride-sharing is simple and convenient, and it’s now an $80+ billion industry. But it wasn’t that long ago that it didn’t even exist. We had cars, we had riders, and we had drivers; but to work, ride-sharing needed smartphones. When they arrived, so did an enormous variety of conveniences and new experiences — some that became entire industries — that we never could have imagined.

Artificial intelligence is a similar kind of catalyst; it’s the next wave of truly transformative technology with potential we cannot yet fully envision or appreciate. It is the defining technology of our time, changing the way we live and work. In my entire career in tech, I’ve never been more excited and optimistic than I am now. I have a colleague at Microsoft who talks about AI like this: You’ve got to use the “new thing” to do old things better. Then, you use the new thing to … do new things. He’s right.

Consider an example from health care. Paige is a software company using AI to change the way doctors identify, diagnose, and treat cancers. With properly trained and tuned models, AI can look at thousands of digital pathology images, pixel by pixel, and detect abnormalities faster and with more accuracy. Imagine what these tools can unlock not only for pathologists and doctors, but for patients, too. It means earlier disease detection, healthier lives, and more time with loved ones.

Right now every company, no matter the size or industry, should be thinking about AI. AI is moving from its auto-pilot phase, which was all about narrow, purpose-built tools that use machine learning models to make predictions, recommendations, and automate, to its copilot phase, where there’s tremendous opportunity to revolutionize how just about everything gets done. Leaders who embrace AI now and take action to understand it, experiment with it, and envision how it can solve hard problems are going to run companies that thrive in an AI world.

But where should they start? Nearly every day, I talk with business leaders who ask important questions about AI’s potential. No matter where you are in your AI journey, it’s incumbent upon every leader to embrace this unique time and take advantage of this powerful technology. If you feel unsure how to start, or how to move forward, you’re not alone. Like any business-planning exercise, think about your AI strategy in phases. Embrace agility and change, and keep a continuous learning mindset, calibrating and adjusting your gameplan as you go.

Start by Experimenting

The best way to learn about AI is to use it. It’s rare for new and disruptive technology to be immediately accessible. This is. Most of the leaders I talk with have tried popular AI applications like ChatGPT or the new Bing. There are many other options out there, but the point is to get curious.

Try applying it to whatever task is in front of you and see what it’s good at and what it’s not. Use it to generate interview questions, write a memo, research and summarize a topic you want to learn more about, or get thought starters for a document. I used Bing and ChatGPT to help me get ideas for a speech. I’ve used Microsoft 365 Copilot, the AI integration across Microsoft apps to generate slides, to find and summarize documents that share a topic, and to recap email exchanges with colleagues. By using and experimenting with AI, you’ll be in a better position to imagine how it could be used in your organization — and you likely know better than anyone where opportunities and potential exist.

Deploy for Productivity

When it comes to productivity, AI copilots — from Microsoft and from others — can be deployed or embedded in applications to assist or simplify certain tasks. Less than two years since its launch, GitHub’s Copilot is already writing 46% of the code on its repository and helps developers code up to 55% faster. Imagine what developers are doing with that extra time. Three out of four users say it helps them conserve mental energy and focus on more satisfying work. Said another way: Creating new things and solving new problems.

Consider the workflows and process-driven activities in your business: things like payroll, on-boarding, or IT help desk support. These are all repeatable, rules-based processes that can be streamlined with AI. This is the driver behind an entire new category of AI software that can handle manual tasks and reshape scores of business processes.

There’s another way to think about AI for productivity: time. If you’re in fraud detection, or you’re a security analyst, time can be your biggest asset or your biggest challenge. If you can shorten the amount of time it takes to comb through lots of data-rich, time-sensitive information, you’re already better and more effective at your job.

Transform Experiences

Today, AI is already impacting how businesses deliver experiences that are better, faster, more efficient, or entirely new, from predictive text on your phone to chatbots on websites to suggested searches when you open a browser.

As an example, PwC is using Azure OpenAI Service to expand and scale its own AI offerings while also helping clients in industries like insurance or healthcare reimagine their businesses by leveraging the power of generative AI. CarMax is using it to analyze hundreds of thousands of customer reviews and surface key takeaways for buyers about every make, model, and year of vehicle in its inventory.

Even in its early stages, AI is making employee experiences better too. A recent Microsoft survey found that 89% of employees and business decision makers with access to automation and AI-powered tools feel more fulfilled. They say it’s because they can spend time on work that truly matters. Nine out of 10 said they want the opportunity to apply AI solutions to even more tasks and activities.

I see that happening already in some of the organizations I work with. They’re pursuing more advanced AI in use cases like customer support, writing assistance, or data extraction and classification. The common thread in each of these involves using AI to leverage resources or information you already have to transform experiences for people.

Build New Things

The above steps are versions of using the “new thing” to do old things better, to borrow my colleague’s turn of phrase. But how can you use the new thing to actually do new things? What can you do that’s completely different? How can you delight customers and create new lines of business and, with them, new revenue?

This is the challenge before business leaders right now; and it’s a hard one. The answer starts with integrating AI into your organization and iterating from there. Because while AI will enable people and organizations to achieve more, we’re at the very beginning of defining what “more” looks like. But to move ahead, we need to put in place the conditions that we help us discover what comes next.

One of the really exciting things about using AI to be more efficient — whether that’s using generative AI to get ideas or to conduct research — is that it allows you to think more deeply about a concept or a problem you’re trying to solve. It’s not a far leap to imagine how this level of concentrated effort is going to enable companies to develop truly new and innovative solutions faster; and then take advantage of the snowball effect of that speed.

Throughout: Prioritize Security & Responsible AI

Alongside all of AI’s promise, one thing is certain. We will not realize AI’s full potential without safeguards. Technology has always been an accelerator and an enabler. AI is no different, but it does present potential risks that have to be managed.

For any company, the success of Responsible AI initiatives depends on at least three things. First, it takes committed and involved leadership. (Microsoft’s Vice Chair and President, Brad Smith, and our Chief Technology Officer, Kevin Scott, chair our Responsible AI Council.) Second, companies must build inclusive governance models and actionable guidelines , as we have done. Finally, they must also invest in responsible AI in the form of new engineering systems, incubations that are research-led, and in the people who will ensure that responsible AI principles are put into practice. At Microsoft there are hundreds of people working on this, and for many of them, it’s their full-time job. Beyond that, they’ve adopted the mindset that using AI responsibly is a responsibility they all share, no matter your role.

To Sum up

Artificial intelligence is a kind of catalyst; it’s the next wave of truly transformative technology with potential we cannot yet fully envision or appreciate. Companies will start by using this new technology to do “old things” before discovering the new opportunities it creates. So, how should they go about this process? They should: start by experimenting, deploy for productivity, transform experiences, and then try to build new things. Throughout this process, they should prioritize security and responsible use.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to percolate innovation in big companies

Mon, 03 Jul 2023 08:10:59 GMT

The business advantages of scale and scope are widely recognized, but large, global enterprises often fail to fully realize them when it comes to innovation.

All too often innovations — including new products, new HR policies to attract and retain talent, and new production processes —developed in one part of a business stay there. Other groups that could benefit from them don’t know they exist. This leads to lost revenues and higher costs, since teams around the world often end up duplicating (or triplicating, or quadruplicating) investments in solving common problems. This article identifies three common obstacles to scaling innovations and describes a way to overcome them.

The business advantages of scale and scope are widely recognized, but large, global enterprises often fail to fully realize them when it comes to innovation. Innovations that are developed in one geographic region or business unit — new products to delight customers, new HR policies to attract and retain talent, new production processes that drive efficiency gains — too often stay within them instead of being disseminated across the enterprise.

Consider the experience of a global consumer packaged goods company, and three of its regional business units:

Region 1 had developed a new brand targeted at younger consumers, who were increasingly seeking healthier products.
Region 2 had changed its production processes in ways that increased profitability.
Region 3 had developed new sustainability focused business models.

For reasons we’ve seen time and again, regions that could have imported these innovations dismissed them, or worse, were simply unaware of them, as they innovated in silos.

Not everything needs to scale globally. Local innovations for local markets matter. But there are meaningful downsides of not scaling the great many innovations that are born locally but do have significant global potential. These include lost revenues, since a new product originating in one region could be sold to customers in others, and higher costs, since teams around the world often end up duplicating (or triplicating, or quadruplicating) investments in solving common problems. The CEO of McDonald’s, Chris Kempczinski, highlighted this in a recent memo , in which he noted that insufficient collaboration across regions meant that “we are trying to solve the same problems multiple times, aren’t always sharing ideas, and can be slow to innovate.”

Other disadvantages arising from insufficient global scaling are less obvious but also really matter. When regional businesses that started out with similar product portfolios don’t share innovations and instead evolve in isolation of one another, those portfolios become more divergent over time. This leads to a proliferation of product lines, which makes it more difficult for the corporate center to optimally allocate resources, and for shareholders to value the company and understand its growth prospects.

There are three common obstacles to scaling innovations from one to many markets. To illustrate both the obstacles and the solutions for overcoming them we’ll use the case of a global retail bank.

1) Fallacies about Local Uniqueness

Though every region is different, local leaders tend to overestimate the singularity of their markets, their customers, and the conditions under which they operate. But often, new products and other innovations — and the capabilities and learnings that come from their development — are much more transferable than they assume, in particular when they are aligned with innovation priorities that are shared across regions. An innovation priority, or “strategic focus area,” is essentially a broad hunting ground for innovation. At the consumer goods company, for example, one innovation priority was packaging, which led to specific innovations in materials and size and format.

Let’s turn to the bank. One of its regional units had made significant investments in developing a digital mortgage-application platform that further automated its mortgage-application process to make it faster and more consumer friendly. Though mortgages were an important product line across regions, most were reluctant to consider adopting this innovation, contending that the distinct ways that mortgages were regulated within their individual markets made it unfeasible.

Regulatory frameworks do vary from country to country, of course, but not in ways that precluded this innovation from being widely adopted, since the implementation of the platform could be adapted to address differences in regulatory requirements in each market, much in the same way that existing mortgage-application processes were customized to local markets. What’s more, the platform was consistent with innovation priorities shared across regions, including digitizing and improving customer experiences across existing product journeys and increasing the profitability of mortgage lending.

To overcome this obstacle, the bank’s central corporate strategy team facilitated a process to identify common innovation priorities across regions. First, regions were asked to define their individual innovation priorities in a standard way (for example, “new ecommerce payment and financing solutions”), identify the associated target customer segment, the customer problems to be addressed, and the broad types of technologies and capabilities required. Each of the bank’s four regions defined between five and 10 innovation priorities.

The corporate strategy team then facilitated a series of discussions among senior leaders of each region to compare and identify commonalities in their innovation priorities. This surfaced multiple priorities that were shared across all four regions. In addition to those related to the mortgage-application platform, they included the development of new mobile-only products for millennial and Generation Z consumers and reduction of call center use and wait times.

Innovations aligned with those priorities could then be identified as sharable, no matter where they originated. Awareness of common innovation priorities among teams across regions also led communities of practice among designers and product developers to emerge organically. These convened regularly to exchange ideas, disseminate learnings, and co-create new innovations consistent with those shared priorities.

2) Misaligned Incentives and Culture

Various formal and informal disincentives are another obstacle to scaling innovations. For example, regional leaders’ bonuses are typically based on their own unit’s P&L performance, which can make them reluctant to spend time, energy, and resources on exporting an innovation that will benefit another region. Conversely, a “not invented here” mindset can make managers reluctant to import externally generated ideas.

The bank’s bonus plans significantly penalized local leaders who missed ambitious profitability targets. This made them reluctant to dedicate resources to exporting innovations. In addition, a “not invented here” mindset was widespread — from regional leaders who wanted to prove their ability to innovate independently to developers who preferred to write their own code rather than use the software developed by peers in other regions.

To address these issues, the bank offered P&L relief to regions on a case-by-case basis, allowing them to deviate from profitability targets without risking their bonuses so that they could invest their own resources into scaling innovations for the benefit of other regions. It also established a central fund and team to support global scaling efforts.

Regions that were developing innovations that could address shared priorities were invited to submit short “global scaling resource requests” that summarized the additional investments required to scale them to other regions beyond just their own. The request could be to cover the cost of building a product in a way that made it operable across global technology environments, for example, or the team hours required to train and support users in other regions and the potential in-year P&L tradeoffs or broader operational challenges that would result.

A global scaling committee, comprised of corporate C-suite leaders and central strategy and finance executives, decided which requests to approve. Most resource requests submitted to scale innovations that addressed shared priorities were approved.

The bank’s corporate leaders also tried to change attitudes by publicly celebrating both managers who shared their region’s innovations and those who adopted those created by others. They spotlighted global scaling initiatives in all-day quarterly business-review sessions attended by top regional executives, where exporting and importing regions jointly discussed the innovations and the process of scaling them. The bank’s leaders also highlighted such innovations and the teams involved in these efforts in company newsletters.

Finally, the bank’s leaders encouraged and frequently reminded regional managers to ask themselves a series of questions before dismissing potential innovations developed by other regions. They included:

Could we better serve the customers we have or seek by adopting this innovation?
If the innovation isn’t specific to one or more of our local circumstances — for example, if it has been developed in the context of a different technological, regulatory, or competitive environment, or requires a means of production or distribution that we lack — could it be adapted?
And might we be guilty of having a “not invented here” mindset if our first instinct is to dismiss this innovation?

3) Lack of Transparency

As we mentioned, one major reason that business units don’t share innovations and instead often duplicate efforts is a lack of awareness of what one another has developed or is undertaking. One way to address this is by creating a central mechanism that makes it easy for regions to share knowledge of their innovation pursuits from their earliest stages.

Consider one innovation priority shared by all the bank’s regions that we mentioned: developing mobile-only products for millennials. Innovations consistent with this priority across regions included distinct payment, savings, and investment products; new product features like roundup savings and cashback and rewards; innovations to streamline customer journeys within mobile-only apps, and integration with budgeting and bill-splitting tools that appealed to the millennial audience. But regions lacked awareness of each other’s efforts and were in some instances even duplicating spend on development of the same things.

The bank addressed this issue in two ways: by establishing the process for identifying common innovation priorities across regions that we previously described and by instituting an innovation-portfolio-management system to profile and track innovations. Regions were required to enter into the IT system a description of each innovation project, including the associated innovation priority, the target customer segment, its revenue potential, and anticipated investment requirements.

Beyond supporting global scaling, the system helped each region better understand and manage its own innovation pipeline. When it surfaced instances of regions unknowingly duplicating each other’s efforts, they were able to collaborate and pool their insights, development capabilities, and data assets.

Too many global enterprises don’t make the most of local innovations. By systematically overcoming the obstacles to scaling them, they can spread them much faster and more effectively and generate a much higher return on their innovation investments.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI impact on enterprise culture

Mon, 03 Jul 2023 07:30:32 GMT

Today we find ourselves in a place that’s all too familiar: the unfamiliar.

The platform shift to AI is well underway. And while it holds the promise of transforming work and giving organizations a competitive advantage, realizing those benefits isn’t possible without a culture that embraces curiosity, failure, and learning. Leaders are uniquely positioned to foster this culture within their organizations today in order to set their teams up for success in the future. When paired with the capabilities of AI, this kind of culture will unlock a better future of work for everyone.

As business leaders, today we find ourselves in a place that’s all too familiar: the unfamiliar . Just as we steered our teams through the shift to remote and flexible work, we’re now on the verge of another seismic shift: AI. And like the shift to flexible work, priming an organization to embrace AI will hinge first and foremost on culture.

The pace and volume of work have increased exponentially, and we’re all struggling under the weight of it. Leaders and employees are eager for AI to lift the burden. That’s the key takeaway from our 2023 Work Trend Index , which surveyed 31,000 people across 31 countries and analyzed trillions of aggregated productivity signals in Microsoft 365, along with labour market trends on LinkedIn.

Nearly two-thirds of employees surveyed told us they don’t have enough time or energy to do their job. The cause of this drain is something we identified in the report as digital debt: the influx of data, emails, and chats has outpaced our ability to keep up. Employees today spend nearly 60% of their time communicating, leaving only 40% of their time for creating and innovating. In a world where creativity is the new productivity, digital debt isn’t just an inconvenience — it’s a liability.

AI promises to address that liability by allowing employees to focus on the most meaningful work. Increasing productivity, streamlining repetitive tasks, and increasing employee well-being are the top three things leaders want from AI, according to our research. Notably, amid fears that AI will replace jobs, reducing headcount was last on the list.

Becoming an AI-powered organization will require us to work in entirely new ways. As leaders, there are three steps we can take today to get our cultures ready for an AI-powered future:

1) Choose curiosity over fear

AI marks a new interaction model between humans and computers. Until now, the way we’ve interacted with computers has been similar to how we interact with a calculator: We ask a question or give directions, and the computer provides an answer. But with AI, the computer will be more like a copilot. We’ll need to develop a new kind of chemistry together, learning when and how to ask questions and about the importance of fact-checking responses.

Fear is a natural reaction to change, so it’s understandable for employees to feel some uncertainty about what AI will mean for their work. Our research found that while 49% of employees are concerned AI will replace their jobs, the promise of AI outweighs the threat: 70% of employees are more than willing to delegate to AI to lighten their workloads.

We’re rarely served by operating from a place of fear. By fostering a culture of curiosity, we can empower our people to understand how AI works, including its capabilities and its shortcomings. This understanding starts with firsthand experience. Encourage employees to put curiosity into action by experimenting (safely and securely) with new AI tools , such as AI-powered search , intelligent writing assistance, or smart calendaring, to name just a few. Since every role and function will have different ways to use and benefit from AI, challenge them to rethink how AI could improve or transform processes as they get familiar with the tools. From there, employees can begin to unlock new ways of working.

2) Embrace failure

AI will change nearly every job, and nearly every work pattern can benefit from some degree of AI augmentation or automation. As leaders, now is the time to encourage our teams to bring creativity to reimagining work, adopting a test-and-learn strategy to find ways AI can best help meet the needs of the business.

AI won’t get it right every time, but even when it’s wrong, it’s usefully wrong. It moves you at least one step forward from a blank slate, so you can jump right into the critical thinking work of reviewing, editing, or augmenting. It will take time to learn these new patterns of work and identify which processes need to change and how. But if we create a culture where experimentation and learning are viewed as a prerequisite to progress, we’ll get there much faster.

As leaders, we have a responsibility to create the right environment for failure so that our people are empowered to experiment to uncover how AI can fit into their workflows. In my experience, that includes celebrating wins as well as sharing lessons learned in order to help keep each other from wasting time learning the same lesson twice. Both formally and informally, carve out space for people to share knowledge — for example, by crowdsourcing a prompt guidebook within your department or making AI tips a standing agenda item in your monthly all-staff meetings. Operating with agility will be a foundational tenet of AI-powered organizations.

3) Become a learn-it-all

I often hear concerns that AI will be a crutch, offering shortcuts and workarounds that ultimately diminish innovation and engagement. In my mind, the potential for AI is so much bigger than that, and it will become a competitive advantage for those who use it thoughtfully. Those will become your most engaged and innovative employees.

The value you get from AI is only as good as what you put in. Simple questions will result in simple answers. But sophisticated, thought-provoking questions will result in more complex analysis and bigger ideas. The value will shift from employees who have all the right answers to employees who know how to ask the right questions. Organizations of the future will place a premium on analytical thinkers and problem-solvers who can effectively reason over AI-generated content.

We believe a learn-it-all mentality will get us much farther than a know-it-all one. And while the learning curve of using AI can be daunting, it’s a muscle that has to be built over time — and that we should start strengthening today. When I talk to leaders about how to achieve this across their companies and teams, I tell them three things:

Establish guardrails to help people experiment safely and responsibly. Which tools do you encourage employees to use, and what data is — and isn’t — appropriate to input. What guidelines do they need to follow around fact-checking, reviewing, and editing?
Learning to work with AI will need to be a continuous process, not a one-time training. Infuse learning opportunities into your rhythm of business and keep employees up to date with the latest resources. For example, one team might block off Friday afternoons for learning, while another has monthly “office hours” for AI Q&A and troubleshooting. And think beyond traditional courses or resources. How can peer-to-peer knowledge sharing, such as lunch and learns or a digital hotline, play a role so people can learn from each other?
Embrace the need for change management. Being intentional and programmatic will be crucial for successfully adopting AI. Identify goals and metrics for success, and select AI champions or pilot program leads to help bring the vision to life. Different functions and disciplines will have different needs and challenges when it comes to AI, but one shared need will be for structure and support as we all transition to a new way of working.

The platform shift to AI is well underway. And while it holds the promise of transforming work and giving organizations a competitive advantage, realizing those benefits isn’t possible without a culture that embraces curiosity, failure, and learning. As leaders, we’re uniquely positioned to foster this culture within our organizations today in order to set our teams up for success in the future. When paired with the capabilities of AI, this kind of culture will unlock a better future of work for everyone.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The accuracy-explainability tradeoff in AI: Black box vs. White Box.

Thu, 01 Jun 2023 16:13:59 GMT

Historically, tech leaders have assumed that the better a human can understand an algorithm, the less accurate it will be.

There is no one-size-fits-all solution to AI implementation. All new technology comes with risks, and the choice of how to balance those risks with the potential rewards will depend on the specific business context and data. But our research demonstrates that in many cases, simple, interpretable AI models perform just as well as black box alternatives — without sacrificing the trust of users or allowing hidden biases to drive decisions.

Historically, tech leaders have assumed that the better a human can understand an algorithm, the less accurate it will be. But is there always a tradeoff between accuracy and explainability? The authors tested a wide array of AI models on nearly 100 representative datasets, and they found that 70% of the time, a more-explainable model could be used without sacrificing accuracy. Moreover, in many applications, opaque models come with substantial downsides related to bias, equity, and user trust. As such, the authors argue that organizations should think carefully before integrating unexplainable, “black box” AI tools into their operations, and take steps to help determine whether these models are really worth the risk before moving forward.

In 2019, Apple’s credit card business came under fire for offering a woman one twentieth the credit limit offered to her husband. When she complained, Apple representatives reportedly told her, “I don’t know why, but I swear we’re not discriminating. It’s just the algorithm.”

Today, more and more decisions are made by opaque, unexplainable algorithms like this — often with similarly problematic results. From credit approvals to customized product or promotion recommendations to resume readers to fault detection for infrastructure maintenance, organizations across a wide range of industries are investing in automated tools whose decisions are often acted upon with little to no insight into how they are made.

This approach creates real risk. Research has shown that a lack of explainability is both one of executives’ most common concerns related to AI and has a substantial impact on users’ trust in and willingness to use AI products — not to mention their safety.

And yet, despite the downsides, many organizations continue to invest in these systems, because decision-makers assume that unexplainable algorithms are intrinsically superior to simpler, explainable ones. This perception is known as the accuracy-explainability tradeoff: Tech leaders have historically assumed that the better a human can understand an algorithm, the less accurate it will be.

White Box vs. Black Box

Specifically, data scientists draw a distinction between so-called black-box and white-box AI models: White-box models typically include just a few simple rules, presented for example as a decision tree or a simple linear model with limited parameters. Because of the small number of rules or parameters, the processes behind these algorithms can typically be understood by humans.

In contrast, black-box models use hundreds or even thousands of decision trees (known as “random forests”), or billions of parameters (as deep learning models do), to inform their outputs. Cognitive load theory has shown that humans can only comprehend models with up to about seven rules or nodes, making it functionally impossible for observers to explain the decisions made by black-box systems. But does their complexity necessarily make black-box models more accurate?

Debunking the Accuracy-Explainability Tradeoff

To explore this question, we conducted a rigorous, large-scale analysis of how black and white-box models performed on a broad array of nearly 100 representative datasets (known as benchmark classification datasets), spanning domains such as pricing, medical diagnosis, bankruptcy prediction, and purchasing behavior. We found that for almost 70% of the datasets, the black box and white box models produced similarly accurate results. In other words, more often than not, there was no tradeoff between accuracy and explainability: A more-explainable model could be used without sacrificing accuracy.

This is consistent with other emerging research exploring the potential of explainable AI models, as well as our own experience working on case studies and projects with companies across diverse industries, geographies, and use cases. For example, it has been repeatedly demonstrated that COMPAS, the complicated black box tool that’s widely used in the U.S. justice system for predicting likelihood of future arrests, is no more accurate than a simple predictive model that only looks at age and criminal history. Similarly, a research team created a model to predict likelihood of defaulting on a loan that was simple enough that average banking customers could easily understand it, and the researchers found that their model was less than 1% less accurate than an equivalent black box model (a difference that was within the margin of error).

Of course, there are some cases in which black-box models are still beneficial. But in light of the downsides, our research suggests several steps companies should take before adopting a black-box approach:

1. Default to white box.

As a rule of thumb, white-box models should be used as benchmarks to assess whether black-box models are necessary. Before choosing a type of model, organizations should test both — and if the difference in performance is insignificant, the white-box option should be selected.

2. Know your data.

One of the main factors that will determine whether a black-box model is necessary is the data involved. First, the decision depends on the quality of the data. When data is noisy (i.e., when it includes a lot of erroneous or meaningless information), relatively simple white-box methods tend to be effective. For example, we spoke with analysts at Morgan Stanley who found that for their highly noisy financial datasets, simple trading rules such as “buy stock if company is undervalued, underperformed recently, and is not too large” worked well.

Second, the type of data also affects the decision. For applications that involve multimedia data such as images, audio, and video, black-box models may offer superior performance. For instance, we worked with a company that was developing AI models to help airport staff predict security risk based on images of air cargo. They found that black-box models had a higher chance of detecting high-risk cargo items that could pose a security threat than equivalent white-box models did. These black-box tools enabled inspection teams to save thousands of hours by focusing more on high-risk cargo, substantially boosting the organization’s performance on security metrics. In similarly complex applications such as face-detection for cameras, vision systems in autonomous vehicles, facial recognition, image-based medical diagnostic devices, illegal/toxic content detection, and most recently, generative AI tools like ChatGPT and DALL-E, a black box approach may be advantageous or even the only feasible option.

3. Know your users.

Transparency is always important to build and maintain trust — but it’s especially critical for particularly sensitive use cases. In situations where a fair decision-making process is of utmost importance to your users, or in which some form of procedural justice is a requirement, it may make sense to prioritize explainability even if your data might otherwise lend itself to a black box approach, or if you’ve found that less-explainable models are slightly more accurate.

For instance, in domains such as hiring, allocation of organs for transplant, and legal decisions, opting for a simple, rule-based, white-box AI system will reduce risk to both the organization and its users. Many leaders have discovered these risks the hard way: In 2015, Amazon found that its automated candidate screening system was biased against female software developers, while a Dutch AI welfare fraud detection tool was shut down in 2018 after critics decried it as a “large and non-transparent black hole.”

4. Know your organization.

An organization’s choice between white or black-box AI also depends on its own level of AI readiness. For organizations that are less digitally developed, in which employees tend to have less trust in or understanding of AI, it may be best to start with simpler models before progressing to more complex solutions. That typically means implementing a white-box model that everyone can easily understand, and only exploring black-box options once teams have become more accustomed to using these tools.

For example, we worked with a global beverage company that launched a simple white-box AI system to help employees optimize their daily workflows. The system offered limited recommendations, such as which products should be promoted and how much of different products should be restocked. Then, as the organization matured in its use of and trust in AI, managers began to test out whether more complex, black-box alternatives might offer advantages in any of these applications.

5. Know your regulations.

In certain domains, explainability might be a legal requirement, not a nice-to-have. For instance, in the U.S., the Equal Credit Opportunity Act requires financial institutions to be able to explain the reasons why credit has been denied to a loan applicant. Similarly, Europe’s General Data Protection Regulation (GDPR) suggests that employers should be able to explain how candidates’ data has been used to inform hiring decisions. When organizations are required by law to be able to explain the decisions made by their AI models, white-box models are the only option.

6. Explain the unexplainable.

Finally, there are of course contexts in which black-box models are both undeniably more accurate (as was the case in 30% of the datasets we tested in our study) and acceptable with respect to regulatory, organizational, or user-specific concerns. For example, applications such as computer vision for medical diagnoses, fraud detection, and cargo management all benefit greatly from black-box models, and the legal or logistical hurdles they pose tend to be more manageable. In cases like these, if an organization does decide to implement an opaque AI model, it should take steps to address the trust and safety risks associated with a lack of explainability.

In some cases, it is possible to develop an explainable white-box proxy to clarify, in approximate terms, how a black-box model has reached a decision. Even if this explanation isn’t fully accurate or complete, it can go a long way to build trust, reduce biases, and increase adoption. In addition, a greater (if imperfect) understanding of the model can help developers further refine it, adding more value to these businesses and their end users.

In other cases, organizations may truly have very limited insight into why a model makes the decisions it does. If an approximate explanation isn’t possible, leaders can still prioritize transparency in how they talk about the model both internally and externally, openly acknowledging the risks and working to address them.

***

Ultimately, there is no one-size-fits-all solution to AI implementation. All new technology comes with risks, and the choice of how to balance those risks with the potential rewards will depend on the specific business context and data. But our research demonstrates that in many cases, simple, interpretable AI models perform just as well as black box alternatives — without sacrificing the trust of users or allowing hidden biases to drive decisions.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium .

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How Machine Learning can improve customer experience

Sat, 01 Apr 2023 18:13:29 GMT

The world largely focuses on how advanced and impressive the core technology is, which distracts from focusing intensely on its tangible value proposition, the precise ways in which it can render business processes more effective.

Machine learning is a promising technology for improving the customer experience. Why? It’s simple: because it can predict customer behaviors. Prediction as a capability is the Holy Grail for foreseeing each customer need and personalizing products and services accordingly. From the consumer’s perspective, when ML’s ethical pitfalls are avoided, prediction can be the ultimate antidote to the information overload that we all face every day. By deploying ML to predict which content is most relevant for each individual, customers can receive better recommendations, less junk mail, very little inbox spam, and higher quality search results, among many other things. These improvements to customer experience aren’t only a nice-to-have, pleasant side-effect of profit-driven ML deployments. They pursue the raison d’etre of any company — to serve customers — and will ultimately translate into further benefits for the business. After all, a happier customer is a more loyal customer, and a higher customer retention rate means a higher customer growth rate.

Machine learning (ML) — technology that learns from experience (data) to predict the behavior of each individual — is well known for improving the bottom line by running major operations more effectively . But did you know that it can also measurably improve the customer experience?

ML generates actionable predictions for individual customers, and those predictions can drive how each customer is served. In this way, ML can target a marketing campaign to customers who are more likely to respond, or disallow credit card transactions that are likely to be fraudulent. It can move likely spam out of the email inbox, or display the property (Airbnb), search result (Google), product (Amazon and Netflix), or romantic partner (Match.com) that’s most likely to be of interest to a customer.

Despite these clear value propositions, ML isn’t yet deployed nearly as widely and seamlessly as it could be. The problem is that the world largely focuses on how advanced and impressive the core technology is, which distracts from focusing intensely on its tangible value proposition — the precise ways in which it can render business processes more effective. As a result, most ML projects fail to deploy , never realizing their intended business value. But as decision makers increasingly recognize that ML can have a huge impact on the customer experience — in addition to the bottom line — companies will begin to shift their focus to generating concrete value with ML, ultimately accelerating and expanding its use.

How ML Helps to Improve the Customer Experience

Why is ML such a promising technology for improving the customer experience? It’s simple: It can predict customer behaviors. Prediction as a capability is the Holy Grail for foreseeing each customer need and personalizing products and services accordingly . From the consumer’s perspective, when ML’s ethical pitfalls are avoided , prediction can be the ultimate antidote to the information overload that we all face every day. By deploying ML to predict which content is most relevant for each individual, customers can receive better recommendations, less junk mail, very little inbox spam, and higher quality search results, among other things.

This has far-reaching potential. ML’s predictions can enhance the customer experience across lines of business and across industries. By way of illustration, here are seven established business applications of ML, each delivering an impact to the bottom line (the leftmost column) — as well as an impact to the customer experience (the rightmost column):

Customers Clamor for Fraud Detection

In one of these arenas — fraud detection — customers already clamor for ML’s predictions. In fact, they complain loudly when prediction fails them. Failure comes in two flavors. For one, if you as a customer see an unexpected charge on your credit card bill, you’ll probably get a bit irritated. And yet, when using your credit card, if a charge won’t go through because your bank’s system thinks it could be unauthorized, you might get irritated just the same.

The only way to maximize the customer experience is to minimize those two kinds of mispredictions — and that’s where ML comes in . ML is the science of improving prediction by way of learning from data. That’s its very definition.

In the prevention of card fraud, FICO is the leader . Their Falcon product, used by 9,000 banks, screens all of the transactions made with most of the world’s credit and ATM cards — 2.6 billion cards globally. By detecting fraud with ML, a medium-sized bank could save about $16 million and, at the same time, improve the customer experience by decreasing the fraud its cardholders experience by about 60,000 cases (see the back-of-a-napkin arithmetic here ). I consider Falcon one of the world’s most successful and widely impactful commercial deployments of ML.

This operation mostly goes unseen, but such unseen efficiencies often do more for the customer experience than the predictive operations that garner the most attention. FICO Falcon affects each consumer much more frequently than the most famous ML system, one that’s commonly known among consumers: the FICO Credit Score, a household name and a major factor in your power to borrow. Many understandably feel that their FICO Score is an important part of their identity as a consumer. Meanwhile, although Falcon’s fraud detection is normally invisible to consumers, it affects their experience much more often: every time they use their card. FICO evaluates financial power by day and fights financial crime by night.

Help Me to Help You: Creating a Virtuous Cycle

Plenty of other proven ML applications that serve the bottom line also serve the customer experience, including the use of ML to route customer service calls, streamline support ticket flow, and detect other kinds of malicious behavior beyond fraud, including phishing, misinformation, and offensive content.

Of course, by helping the customer, companies also help themselves. These improvements to customer experience aren’t only a nice-to-have, pleasant side-effect of profit-driven ML deployments. They pursue the raison d’etre of the company — to serve customers — and will ultimately translate into further benefits for the business. After all, a happier customer is a more loyal customer, and a higher customer retention rate means a higher customer growth rate. The sooner you deploy ML to serve these dual purposes, improving both the bottom line and the customer experience, the sooner your firm can begin to capitalize on this virtuous cycle.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Be prepared for a period of higher cost of capital

Sat, 01 Apr 2023 17:58:10 GMT

Productivity is the new priority and it replaces the growth strategy.

With interest rates rising in real terms, the value of growth strategies has declined. Managers need to look carefully at where they allocate capital, focusing more on investing in productivity. Nor should they treat strategy-making as a periodic exercise but rather as a dynamic, continuous process.

For most of the last 15 years, capital has been cheap. Since 2009, the after-tax cost of borrowing for some large companies has been below the rate of inflation, making their debt in real terms cost-free. And for much of this time, the stock market moved steadily upward, consistent with historically low costs of equity. We estimate that in early 2022, the weighted-average cost of capital (WACC) for the average company in the S&P 500 hovered below 6%.

All that changed in March 2022, when the world’s central banks began raising rates to curb rising inflation. Over the next 12 months, the U.S. Federal Reserve increased its benchmark Fed Funds rate f rom 0.25% to 4.75% and the yield on 10-year treasury notes climbed from 1.75% to almost 4% today. With rates on risk-free assets rising, the cost of debt for most large corporations also increased — from an average of less than 2.3% for most Baa-rated corporate debt in early 2022 to nearly 5.75% today . And, finally, the cost of equity capital also rose for most large companies over this period — from less than 7% to approximately 10%, according to research conducted by Aswath Damondaran at the Stern School of Business at NYU. All in, the WACC increased by 50% or more in just 12 months — from less 6% a year ago to nearly 9% today.

In the face of expensive capital, companies need to reexamine how they allocate their resources and communicate their strategies. In the brave new world of dearer capital this will involve:

1. Re-evaluating growth investments

We all know the obvious ways that growth can destroy value, like pursuing unprofitable business models or pushing products with negative gross margins. But profitable growth can destroy value too. This occurs when the capital that is invested in growth initiatives generates an accounting profit but does not produce a return on capital that exceeds the business’s cost of capital. In today’s world, leaders should be much more discerning about growth investments — doubling-down on investments that are likely to produce attractive returns and putting low-return growth investments on the backburner.

To elaborate, as capital costs rise, the value of growth relative to the value of margin improvement changes significantly (see the exhibit in this article ). When the cost of capital is low, strategies that accelerate growth create far more value than those tied to boosting margins. This partially explains why so many companies pursued growth strategies over the last decade with considerable fanfare from investors — Adobe, Alphabet, Dell Technologies, even John Deere.

But as capital costs approach 9% or so (the current average for the S&P 500), the value of growth diminishes and strategies that increase profitability create more value. Many companies have already reached this tipping point. This is why the tech-heavy NASDAQ, comprised largely of growth stocks, declined so significantly last year — from a high of more than 14,000 in early 2022 to a low of 10,500 at the beginning of 2023 — and companies like Meta saw their market values collapse.

One company known for its focus on growth has begun describing its strategy and performance very differently in lieu of higher capital costs. The tech high-flier Netflix recently announced that it would end the company’s focus on “net subscriber adds” as a key performance metric. Indeed, despite adding more than 10 million new subscribers in 2022, Netflix is placing much greater emphasis on revenue, operating income, and operating margin as the company’s primary performance measures. The message to investors is clear: In assessing the value of Netflix, you must consider the profitability of streaming customers, not just their number. Other growth companies are likely to follow Netflix’s lead with this kind of messaging.

2. Investing in productivity

In anticipation of a slowing economy, more than 3,150 companies announced layoffs in 2022, according to data compiled by AI-powered market intelligence platform Intellizence . Amazon, Microsoft, Salesforce, Disney, and Philips led the way. In most cases, these layoffs were undertaken to increase efficiency, enabling each company to do the same, with less. In our experience, headcount actions can boost margins (at least temporarily), but they often make reigniting growth more challenging later — particularly if satisfying new demand requires significant rehiring. But the best companies find ways to increase productivity during slowdowns, not merely cut costs.

Boosting productivity requires that leaders identify the factors preventing their people from getting things done. In some instances, the problem is organizational complexity — that is, a combination of complex processes, organizational structures, and ways of working. Unless this underlying complexity is removed or otherwise dealt with, then any margin improvement will be short-lived. As one client executive recently remarked after overseeing her company’s third major workforce reduction in as many years: “We should have learned this by now. If you take the people out, but you don’t take the work out — or don’t change the way work gets done — the people will invariably come back.” Efficiency measures alone rarely create sustained improvements in profitability.

Enhancing productivity often requires investment — the substitution of capital or technology for labor. Some new technologies offer the potential to dramatically improve productivity, with modest investment. For example, AI-applications can enable many routine tasks to be automated. Advertising copy can be generated by machine rather than armies of marketing professionals; call centers can be manned by optimized language models, trained to respond to customers, suppliers, and other parties in a conversational way . Other technologies — most notably, factory automation — can require significant investment. But done right, technology investments reduce costs and increase productivity, allowing a company to improve margins in the short-term without sacrificing growth in the medium- to longer-term.

3. Dynamically reviewing capital spending plans

Most companies conduct capital planning on an annual basis. But the pace of change is necessitating a shift. CFOs need formal mechanisms to track changes in assumptions, risks, and opportunities continuously throughout the year. A plan to deploy capital midyear may need to be delayed, revised, or scrapped altogether , based on what takes place during the first half of the year.

Dynamic capital budgeting requires that organizations think about signposts, trigger points and related metrics . Signposts refer to the critical market and competitive factors that most influence a company’s decision to deploy capital. For example, a critical assumption behind General Motor’s decision to invest heavily behind Battery Electric Vehicles (BEVs) was the rate of penetration of BEVs in the market. That number is driven by a few specific signposts — such as, the cost of batteries, changes in government mandates, availability of charging stations, competitor pricing actions, etc.

Each of these signposts has important trigger points that should impact the assumptions built into the capital plan. For example, if the number of charging stations in major metropolitan areas does not grow as anticipated this year, then the assumptions in GM’s capital plan would most probably need to change. By continuously monitoring signposts — relative to trigger points — companies can determine if (and when) they need to modify their capital plans to stay ahead of the competition.

The age of cheap money is over. In the face of rising capital costs, leaders should rethink their approach to resource allocation and capital planning. These processes must become more disciplined and dynamic. Once “obvious” investments in growth may need to be reconsidered. And actions to improve margins must be mindful of productivity, not just efficiency. Capital — and its costs — must be measured and carefully managed. Otherwise, companies risk over-investing in the wrong opportunities and under-investing in the right ones, undermining future profitability, growth, and value creation.

As this table shows, while the impact on value (y axis) of a 1% increase in the operating margin generated by improved productivity remains constant (at 5% of company value) no matter what the cost of capital is, (x axis) the impact on company value of a 1% increase in the company’s growth is highly sensitive to the cost of capital. Put simply, the higher the cost of capital is, the less valuable is an increase in revenues, and when the cost of capital exceeds 9%, investments in productivity become more valuable than investments in growth.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How AI affects the organization design

Fri, 03 Mar 2023 15:52:53 GMT

Firms that understand how to use it as a new tool in the broader context of process reengineering and structure redesigning will arguably get the most from AI in the long run.

The idea of business process reengineering is making a comeback, this time driven by artificial intelligence (AI). In the 1990s, the implementation of enterprise resource planning systems and the internet allowed companies to make changes to broad business processes, but the expectations of the radical changes hoped for were often unfulfilled. However, AI enables better, faster and more automated decisions, allowing companies to improve efficiency and produce better outcomes. Companies — from banks to industrial firms — are already using AI to transform their processes.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering’s academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products.

But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes — Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development — all without any assistance from technology.

Now, a version of this idea is making a comeback in some companies, and we expect to see it in more. It will require not only an appreciation and understanding of AI, but also a renewed appreciation of business processes as a structure for improving work. As AI emerges as a universally applicable, general-purpose technology, it appears increasingly possible that it can enable the kind of radical redesign of business processes originally envisaged by reengineering’s proponents.

Updating Reengineering

The technologies enabling reengineering in the 90s were primarily transactional and communications-based. They enabled efficient data capture and transfer within and across organizations. AI, on the other hand, enables better, faster, and more automated decisions. In essence, most AI deployments in large organizations involve learning from large datasets to make a prediction or classification, which in turn helps business with making a better operational decision. Better operational decisions, in turn, enhance efficiency by producing better outcomes. A key difference is that present AI systems are a true general-purpose technology, and have brought about dramatic changes not just in production planning and control, but also visual image recognition and inspection, autonomous operations, and generating new content.

While the methods fueling this growth of AI have been around for decades, the cost of implementing them has dropped precipitously. Previously the domain of data scientists only, modern AI-based solutions are now mature enough to be offered “off the shelf,” greatly lowering the technical barriers to entry. Falling computing costs — driven by the wide availability of the cloud, the growth of low-cost bandwidth, and reduced cost of sensors — have drastically lowered the price of model-driven prediction. AI-based decisions can also be included within the wider context of automation. Technologies such as robotic process automation (RPA) help to structure the flow of work and automate information-intensive back-office processes. RPA is rule-based, which limits its ability to employ data-based decisions. But combined with machine learning as “intelligent process automation,” it can deal with much a greater variation of tasks.

This AI-driven reengineering is already taking place. Banks are using it to transform wealth management advice for clients. Insurance companies are using AI to make client onboarding and underwriting much easier, and automating claims estimates for auto and home damage with deep learning analysis of photos taken by the insured. Industrial firms are reshaping maintenance and engineering processes. Even in health care, where there is considerable research on AI but much less clinical adoption, diagnosis and treatment are being reshaped by AI-based telemedicine in some countries.

All of this has important ramifications for the way we use AI, how work is being performed, and how companies are being organized. To take advantage of these potential benefits, companies need to bring back an end-to-end process perspective on their businesses, and think carefully about how AI can transform them. In essence, companies need to explore where they are generating sufficient data to extract patterns that could be used to support operational decisions.

AI Drives Process Reengineering

As AI brings new capabilities to a business process, companies need to rethink what tasks are needed, in what frequency, and who does them. When AI is accompanied by partial automation, companies also need to decide what humans will do and what machines will do in their processes. Most AI applications to date seek to improve a given task. But this is missing the larger picture; smart companies are viewing the introduction of AI as the rationale for a new look at end-to-end processes.

At its most basic level, process analysis often involves a mix of constraints and opportunities. For example, at DBS Bank in Singapore, the manager of transaction surveillance (anti-money laundering and fraud detection) said in an interview they were frustrated at the high false positive levels identified by the rule-based system required by banking regulators. That is an unavoidable constraint on the process, but he saw an opportunity to use AI to predict and score the fraud risk of each positive result using machine learning. Transactions with low fraud probabilities could simply be put in a “cooler” for several months to see if they recurred with the same customer. Machine-learning based AI systems to detect outliers are well-established in the fraud detection arena. But when the machine learning system was combined with a new workflow platform and a relationship network analytics system (to identify fraud network members), the productivity of surveillance analysts increased by a third.

Another good example is at Shell. Shell has long been a process-focused company, and is presently engaged in a major AI initiative in areas like supply chain, operations, and maintenance. As part of this, Shell is reengineering its work processes.

For example, consider the work of monitoring and inspection at energy and chemical plants, pipelines, offshore facilities, and wind and solar farms. This work used to be exclusively done in person by inspectors and maintenance technicians, but AI can ease that constraint. Now many low value-adding inspection tasks can instead be done remotely by robots and drones. Some Shell facilities are so large that it would previously have taken years to inspect everything manually — now drones and robots are being introduced to automate these processes and help shorten the cycle time.

As a result of these changes, inspectors and maintenance technicians can now rethink their day-to-day work. They can focus on higher-value activities such as prioritizing projects or, if they’re on site, performing more advanced verification. At the same time, new tasks are emerging, such as annotation for images to improve inspection algorithms or managing the training processes for the thousands of machine learning models now working in production. What were previously physical work processes are now managed by multidisciplinary teams doing largely digital tasks.

This shift involved some resistance. At first, it was hard to convince the inspectors, but gradually they are being persuaded as they’re shown that the processing of the images provides similar accuracy in much less time. Furthermore, Shell is engaging these engineers in rethinking their work processes with the remote surveillance centers, empowering them to drive the change.

Shell is finding that this process of AI-enabled reengineering is becoming a permanent way of operating. Each individual project may take only a year or two, but the more they use digital, data, and AI to redesign processes, the more they see opportunities for going further. This is particularly important as the company is transforming to become a net-zero emissions energy company.

Who Should Lead AI-Enabled Process Change?

Process improvement has traditionally solely been the domain of operations managers. For that reason it has been somewhat rare for organizations to have an explicit reengineering initiative in conjunction with their AI projects. To truly leverage the power of AI, process design and improvement activities should be incorporated within the AI initiative. The most successful such initiatives are increasingly being orchestrated by “product managers” who have the successful deployment of the system, including the needed business changes, as their objective. Shell designates a product owner to manage the business change, and a product manager who is responsible for technical delivery. Some organizations also engage in “design thinking” exercises, which overlap in part with reengineering-style analyses of how workflows and activities need to be redesigned to meet customer or internal needs.

While we have seen multiple instances in which reengineering takes place alongside AI development, not enough organizations yet recognize the need for process change. A more explicit callout of the reengineering role and activities — including high-level design, detailed process flows, measurement of before and after costs and cycle times, and analysis of needed skills and training — would be helpful, whether it is called “reengineering” or not. The activities are too important to the success of AI projects to leave them to chance or an astute manager who remembers the reengineering movement.

Because automation-focused projects have a direct impact on process flows and are more likely than other forms of AI to involve only incremental change, they are more likely to include a formal set of process improvement steps. At Voya Financial , for example, the process improvement group has an automation center of excellence within it, and no automation project takes place without a process improvement effort first. The group’s head told us that automation at the company is as much a process-oriented engagement as a technical one. We’ve found several other companies that combine process improvement and automation, but we’d like to see both more aggressive process change and the more powerful AI technologies like machine learning in combination as well.

AI is a means to an end, not an end in itself.

AI is fast becoming a ubiquitous technology. Once the hype recedes it will become as standard as ERP systems, statistical packages, or even spreadsheets. AI platforms can be used by a much larger pool of companies to reengineer their processes. Firms that understand how to use it as a new tool in the broader context of process reengineering will arguably get the most from AI in the long run.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Technology and Customer-Centric Vision in Financial Services

Thu, 02 Mar 2023 10:49:52 GMT

Innosight's POV: to achieve sustained growth, incumbents must do more than push old products through new digital channels. They must create truly customer-driven innovations that are centered on new value propositions.

In the previous articles , we have highlighted the impacts of enabling technologies and related operative models in the financial services industry (e.g., advanced analytics, and AI). But how to build a holistic vision? Financial services incumbents are under pressure to meet the fintech challenge by delivering more innovation faster. As companies like the payment provider Stripe and NuBank, a consumer banking services company, have shown, disruptive startups can scale quickly and become major competitors to established players. Some tools used for example by Innosight can hugely support the effort.

The challenge for incumbents is how to compete against insurgent new entrants. Financial services is a mature industry with well-established business models and many entrenched advantages for its incumbents, including established customer bases, customer stickiness, and well-recognized brands. Many players can stave off disruption for a time with “fast follow” strategies, quickly reproducing the novel products and services the fintechs pioneered, and leveraging their established distribution channels to drive volume and adoption.

The approach is not foolproof, however. Startups Venmo and Cash App have sustained their market position despite the introduction of Zelle, a solution backed by some of the largest banks in the world. To achieve sustained growth, incumbents must do more than push old products through new digital channels. They must create truly customer-driven innovations that are centered on new value propositions.

In our work with clients, we have identified three common barriers to innovation success. Fortunately, there are powerful tools that can be used to overcome them.

Barrier 1: A Lack of Clarity on Who the Customer is and What Need is Being Addressed

Many organizations tailor their innovation efforts to a small set of high-level personas that match the socio-economic and demographic attributes of target customers. They might even give those personas names, to make them recognizable and relatable. But too often, they are overly broadly defined and “averaged out,” resulting in products that struggle to appeal to or create value for any real customers.

Recommended Tool: Market Maps

In our view, successful innovation begins with a deep understanding of a customer's job to be done, meaning a problem or goal that customers are trying to solve or achieve in a particular circumstance. A broadly held job in financial services might be “buy a home for my growing family,” or “make sure I don’t spend beyond my means.” Using a customer jobs framework provides insights at a level of granularity that standard personas can’t match. But while jobs to be done are permanent, their relevance varies across individuals depending on their specific circumstances, and on the solutions that are available to them in the market.

A tool called Market Maps can help executives visualize this problem space. It's designed by Innosight.

Market Maps are created by laying out customer circumstances on one axis and jobs to be done on the other. Each resulting cell on the matrix can then be evaluated to see if an innovation opportunity arises for that combination of job to be done and circumstance. We ask three questions when we are evaluating them:

Is the job to be done prevalent? Do a lot of customers have this job to be done in their circumstances?
Is the job to be done unsatisfied? Do existing solutions in the market have significant shortcomings?
Is the job to be done high value? Would customers pay to have this job solved?

If the answer to all three questions is “yes,” then the innovation opportunity is likely worth exploring.

Barrier 2: Spreading Investment Dollars too Thinly to Have a True Impact

When innovation efforts are not clearly directed, and when teams across the organization are not aligned on a small number of priorities, money, people, and management attention can be too diffused to make much difference. This challenge of alignment and focus is particularly acute in agile organizations, where priority-setting and decision-making is widely distributed.

Furthermore, organizations can struggle to see the benefits they receive from their investments in innovation, especially when they come in the form of “customer experience” improvements, which are difficult to quantify and attribute directly to financial performance.

Recommended Tool: Strategic Focus Areas

Strategic Focus Areas help codify opportunities by defining the customer, their job to be done, and the kinds of solutions that can be developed to address it. Each Strategic Focus Area acts as a hunting ground for innovation, delineating the general area that teams should explore, but also leaving room for them to test and learn their way to solutions.

SFAs can be evaluated across two dimensions to determine the quality of fit for an organization and its priority. Those dimensions are:

Attractiveness—for example, addressable market, potential customer base, margin potential in the category—;
Feasibility, meaning how well it fits the organization’s capabilities and aligns with its strategy.

To drive focus, organizations should focus on a small number of Strategic Focus Areas, perhaps as few as three to five. This also ensures that sufficient resources can be invested to drive growth once a concept is sufficiently proven.

Alasdair Trotter , "We advised a consumer banking organization that was weighing the idea of creating products or services tailored to the specific needs of gig workers. Using a Market Map, we identified key combinations of jobs to be done and customer circumstances for that segment of workers. One revelation was that freelance accountants and other highly compensated professionals have the job to be done of “access credit for a major purchase,” which can be difficult due to their irregular incomes. Armed with this insight, we defined an SFA around alternative loan products and underwriting services."

Barrier 3: Bringing Undifferentiated Solutions to the Market

When developing new products, many organizations rightly focus on the user experience, product design, and pricing. However, they often forget to ask a crucial question: “How are we going to solve this problem better than any other company?” and thus miss the opportunity to create differentiation.

The lack of a clear point of view, backed up by evidence, of why a customer should choose one company’s product over another’s often leads to promising products that nonetheless fizzle when they are introduced.

Recommended Tool: Performance Maps

A Performance Map can help executives visualize the performance of competitors’ products from their customers’ perspectives. To evaluate them, they must consider which features customers value – this often includes price, convenience, ease of use, and other factors – and then chart them against each other and against the customers’ definition of quality.

"One of our clients, a wealth management firm, was seeking to create new products that would appeal to younger investors. After evaluating several financial planning tools from leading competitors, we found that a trade-off was embedded in all of their designs. On the one hand, they were very sophisticated, outputting highly personalized projections and providing customized advice. However, they were also very complex to use, requiring a high degree of engagement with the subject, as well as a good deal of time to gather information. Our analysis suggested that these tools were likely “overshooting” the needs of beginner investors, who might appreciate a less sophisticated but easier to use product."

Many of our clients have sophisticated approaches to innovation, from agile principles to a commitment to testing and learning. But unless these three barriers are overcome, companies can waste time and money developing new products that don’t actually address their customers’ needs, or that are insufficiently different from what already exists in the market to attract new customers. Or, they can create a perfectly sound product but fail to support it enough to allow it to break through in an extremely competitive marketplace.

By utilizing Market Maps, Strategic Focus Areas, and Performance Maps to generate high-quality hypotheses at the beginning of the innovation process, organizations can avoid those and other pitfalls.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI, Accountability and Value Creation

Thu, 02 Feb 2023 10:22:13 GMT

There is a clear need for CDOs to focus on adding visible value to their organizations.

The CDO role is poorly understood, and incumbents of the job have often met with diffuse expectations and short tenures. There is a clear need for CDOs to focus on adding visible value to their organizations. We suggest 8 strategies for CDOs to create — and show — value for their companies:

Assume responsibility for analytics and AI;
Focus on data products;
Measure and document results;
Build relationships with peers and business leaders who get it;
Focus on data governance
Work on creating a data-driven culture even though it’s difficult to show value quickly;
Build analytics and data infrastructure;
Focus on a few key projects of value to stakeholders.

The chief data officer (CDO) role was only established in 2002, but it has grown enormously since then. In one recent survey of large companies, 83% reported having a CDO. This isn’t surprising: Data and approaches to understanding it (analytics and AI) are incredibly important in contemporary organizations. What is eyebrow-raising, however, is that the CDO job is terribly ill-defined. Sixty-two percent of CDOs surveyed in the research we describe below reported that the CDO role is poorly understood, and incumbents of the job have often met with diffuse expectations and short tenures . There is a clear need for CDOs to focus on adding visible value to their organizations.

Part of the problem is that traditional data management approaches are unlikely to provide visible value in themselves. Many nontechnical executives don’t really understand the CDO’s work and struggle to recognize when it’s being done well. CDOs are often asked to focus on preventing data problems ( defense-oriented initiatives) and such data management projects as improving data architectures, data governance, and data quality. But data will never be perfect, meaning executives will always be somewhat frustrated with their organization’s data situation. While improvements in data management may be difficult to recognize or measure, major problems such as hacks, breaches, lost or inaccessible data, or poor quality are much easier to recognize than improvements.

So how can CDOs demonstrate that they’re creating value? The primary ways that data adds value to companies is through enabling them to understand and predict business performance and customer behavior, and embedding it into products and services — all offense-oriented initiatives. CDOs, then, must be able to help companies achieve value through better data usage and consumption.

That is a primary focus of a recent research project sponsored by Amazon Web Services. It included a large survey of 250 CDOs who attend the MIT Chief Data Officer/Information Quality Symposium, as well as in-depth interviews with 25 prominent incumbents of the role. Of the CDOs surveyed, 41% said they define success by achieving business objectives — significantly more than those who measured success in terms of change management or culture shift (19%), technical accomplishments (5%), prevention of serious data problems (2%), or an equal combination of these factors (32%).

Based on the insights in this research, we’ll describe several value-creating steps below. We’ll start with some approaches that work for every type of organization, and then describe some that depend on the analytical and data management maturity of the company employing the CDO.

How CDOs Can Create Value.

Assume responsibility for analytics and AI. These initiatives are viewed as delivering the most value: 35% of the CDOs surveyed believe in focusing on a small set of key analytics or AI projects to deliver the most value. A majority of CDOs (64%) are also spending their time on enabling new business initiatives based on data, analytics, or AI. That makes them — either officially or unofficially — chief data and analytics officers, which is a fast-growing variant of the CDO title. Several CDOs commented in interviews that the combination of managing both the supply of and demand for data is effective at providing value. At lower levels of maturity, focus on a few key projects of value to stakeholders. If your organization is early in its data and analytical journey, pick a few analytics and AI use cases to develop based on consultations with key stakeholders. Ensure that those few projects get successfully deployed. And don’t boil the ocean — modernize the data environment only as particular analytics applications or AI use cases are being developed. Then business leaders can see the connection between data modernization and the business value it enables. Focus on data products. Data products are combinations of data and analytics/AI to achieve a specified result for a customer or employee. An example might be a new simulation model to determine whether wealth management customers will outlive their savings, or an attrition model to predict employee departures. Adopting an analytics-based data product focus, which encompasses all activities from ideation to deployment and ongoing maintenance, is a good way to ensure value creation. The product focus ensures that data scientists, data engineers, and other members of a data product team don’t just create algorithms, but rather collaborate in deploying entire business-critical applications. Thirty-nine percent reported that they “adopt a data product management orientation with product managers .” This is a relatively new concept, so for that many to have already adopted a data product focus is surprising.Manav Misra, the chief analytics and data officer at Regions Bank, ensures that each of the data products his team develops are successfully deployed and the value to the company carefully measured. For each data product they have quarterly steering committee meetings, at which the business team — the leaders of the business or functional unit that sponsored development of the data product — does the reporting, and Misra’s team attends the meeting.

Measure and document results.

Carefully measuring the results and value of key projects, sometimes in collaboration with the finance organization, helps CDOs demonstrate and publicize value. Sebastian Klapdor, CDO of printing and design service company Vista, is also a strong advocate of data products, and ensures that all of Vista’s data products have impact by assessing them quarterly with a sign-off on any monetary benefits from the finance organization. In only a couple of years his CDO organization has documented $90 million in incremental profits — an impressive number for a company with $1.5 billion in 2021 revenues. Some CDOs have also created online dashboards to describe their organization’s achievements and value with respect to data and data-driven business outcomes.

Build relationships with peers and business leaders who get it.

Successful CDOs find business leaders — and parts of the business — who already appreciate data to a substantial degree, and who can be partners in providing data-driven value. Data, analytics, and AI initiatives require substantial change not only in technical areas, but also in processes, culture, skills, and customer/supplier relationships. They can’t be done successfully without strong senior executive support. CDOs need close and trust-based relationships with those senior executives.

Strategies for Advanced Companies.

Some other approaches to providing value depend on the sophistication of the company involved in analytics, AI, and their data management underpinnings.

Highly sophisticated companies can focus on data governance. Data governance is a top priority for CDO activity in our survey, but it’s a difficult way to achieve value. It involves behavior change and asking data users to take on data management activities that are not part of their defined jobs. Given the difficulty of effective data governance, only those CDOs who have established value through other means may want to take it on as a priority. Some CDOs are attempting to establish “governance by design,” in which systems and data structures enforce the proper use of data through data architectures and reusable data assets. However, it is still early days for this approach, and it also requires a high level of data management sophistication.

Advanced companies should work on creating a data-driven culture, even though it’s difficult to show value quickly. A hefty (69%) percentage of CDOs spend a substantial fraction of their time on data-driven culture initiatives, and it’s clear why: 55% view a lack of a data-driven culture as a top challenge to meeting business objectives. Cultural initiatives typically involve data literacy programs and attempts to inculcate data-driven decisions across the organization. However, these cultural activities also involve behavioral change and may be slow to come to fruition. Therefore, CDOs should take on cultural change only in a measured fashion if they have not already brought about substantial value through other means.

Build analytics and data infrastructure if your organization is sophisticated. Some CDOs in relatively advanced analytics and AI companies emphasized that completing key projects alone is not enough. They felt that CDOs eventually need to build an infrastructure to accelerate the use of data, analytics, and AI throughout the company.Todd James, who leads data and AI for 84.51°, the data science subsidiary of The Kroger Co., said that: “A set of strategic use cases is not enough. That creates a set of point solutions. You’ve got to be able to scale by having a set of reusable analytical capabilities…We’re trying to create a composable [built from modular components] set of analytics and AI applications that are accessed through APIs.” Similarly, one leading bank’s head of enterprise data and machine learning is focused heavily on scale and infrastructure development for machine learning. He noted in an interview: “With ML, we are moving toward platforms that everybody can take advantage of, with both standardization and automation. We want to root out arbitrary uniqueness, and get rid of temporary ML platforms.” The bank is also building a feature store: a repository of reusable variables for ML models.

There is little doubt that organizations need chief data officers and that the job is here to stay as long as its incumbents add value. Some are clearly doing so. The job may have short average tenures, but 30% of the CDOs in our survey had already occupied their jobs for more than six years. If CDOs adopt these and related approaches to producing tangible value with data, analytics, and AI, they will be instrumental in transforming their organizations into more digital and data-driven competitors. As Bill Groves, a veteran CDO who held the role at Walmart, Honeywell, and Dun & Bradstreet put it, “This [the CDO function] is not a service organization; it’s a transformation organization.”

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to create business value through AI

Wed, 01 Feb 2023 09:52:23 GMT

Companies have every opportunity to use data, analytics, and AI to transform their businesses. Now is the moment to rethink how these investments are being made. It is time for data leaders to deliver transformative business outcomes. This is the moment to move forward and learn from the lessons of the recent past.

Companies need to rethink how they’re investing in data, analytics, and AI — and whether these investments are creating real business value. Based on a recent survey of senior data and analytics leaders of Fortune 1000 companies, we offer four recommendations:

Focus on culture change and its business impact;
Start small;
Build strong business partners and sponsors at every stage;
Pay attention to data ethics.

It’s time for Fortune 1000 companies to rethink their investments in data, analytics, and AI. Of course, companies should be investing in these critical business capabilities and differentiators. What they need to take a hard look at is how they’re investing, and whether these investments are leading to the kinds of gains and the levels of business value that companies are aspiring to achieve.

Responses to a recently released survey of Fortune 1000 and global data and business leaders show that data, analytics, and AI efforts have stalled — or even backslid. Since 2012, when the survey was launched to investigate organizations’ investments in data initiatives, the survey has expanded into related topics such as analytics, AI and machine learning, the role of the Chief Data Officer, and data ethics. This year, the survey captured the perspectives of chief data officers (CDO), chief data and analytics officers (CDAO), and other senior data and business leaders from 116 Fortune 1000 companies and global leaders, across financial services, retail, consumer packaged goods, health care, life sciences, and more. The responses revealed troubling trends.

Consider the following findings and implications from the 2023 survey:

Just 59.5% of executives reported that their companies were driving business innovation with data, compared to 59.5% four years ago — no change.
A disappointing 40.8% of executives reported that their companies were competing on data and analytics, a decrease(!) from 47.6% four years ago.
An unsatisfactory 39.5% of executives reported that their companies were managing data as a business asset, a decline from 46.9% four years ago.
Just 23.9% — under one quarter — of executives reported that their companies have created a data-driven organization, down from 31% four years ago.
Finally, and most discouraging, a meagre 20.6% of executives — barely one in five — reported that a data culture had been established within their companies, a nearly 50% decrease from the 28.3% of companies reporting having established a data culture back in 2019. Regression, not progress.

These findings are not great news. Consider that 87.8% of executives reported that their companies had increased investments in data, analytics, and AI during 2022, and 83.9% expected this investment trend to continue in 2023. While 91.9% of respondents said that this investment is creating measurable business value, it’s apparently not enough to move the needle on these key metrics of organizational transformation.

What should companies be doing differently to achieve a different outcome? What are successful outlier firms doing differently? Given the economic headwinds on the horizon, companies need to be smarter about how they invest in data, analytics, and AI, and track their investments to sustainable business progress.

Having been a firsthand observer of the growth and adoption of data, analytics, and AI in the corporate world for four-plus decades, here are some recommendations for any company that aspires to leverage data, analytics, and AI to transform their businesses and reposition themselves for the long haul.

4 Drivers

1) Focus on Cultural Change and its Business Impact

If you want your investments in tech to pay off, you also need to invest in your culture. This, however, is often overlooked. It should come as little surprise that 79.8% of the executives surveyed identified cultural impediments, not technology, as the greatest barriers to becoming data-driven companies. While companies pointed to investments in laudable technology initiatives such as data modernization, data products, and AI/ML initiatives, only 1.6% of executives highlighted data literacy as their top investment priority.

Cultural barriers might stem from education, communication, business processes, organizational alignment, skills development, training, or all of the above. Change and transformation are never easy for a large organization, but perhaps it’s time for companies to invest more time and attention — and funding — to change thinking, mindsets, and the ways in which companies use data, analytics, and AI if they are truly serious and committed to transforming their business and not just following the pack.

2) Instead of Boiling the Ocean, Start Small

Too many companies undertake massive technology infrastructure investments intended to improve access to data — data warehousing, master data management, cloud migration — that fail to deliver commensurate business value. Experience suggests that companies that start small, with a focus on delivering immediate business value and establishing a foundation one step at a time, have been most successful in building data-driven organizations for the long haul.

Investing in modern data environments can be wise from a long-term infrastructure and platform perspective, but if companies cannot show business value from their data investments at every step along the way, data leaders run the risk of losing business confidence, commitment, and trust. This has been a recurring pattern for many organizations and a contributing factor to the short and unstable tenures of corporate chief data officers. Data leaders cannot afford to make unforced errors.

3) Build Strong Business Partnerships and Sponsorship at Every Stage

Like any area of professional expertise, data, analytics, and AI has acquired a specialized language of their own, with terms like “data mesh” and “data fabrics.” Regardless of the potential value of such approaches, too often these technical terms ring of impenetrable jargon that may put off other business leaders or result in a lack of trust. This is particularly true if investments in these areas do not produce clear business value in the intermediate term. Without a foundation of credibility that is built on delivering business results, initiatives lose momentum and their proponents lose organizational support. This is a pattern that too often repeats itself.

Successful data leaders blend into the organization, communicating in clear, concise, simple, and benefits-oriented language. By speaking in terms of business results, successful outcomes, and customer satisfaction — the language of business leaders — they build the trust of their business colleagues. This helps them identify and collaborate with strong business sponsors. Together, they work hand-in-hand to deliver data, analytics, and AI capabilities that produce very specific and measurable business outcomes — more customers, happier customers, successful new products, and entry into new markets — which are directly attributable to data, analytics, and AI capabilities. These CDOs and CDAOs successfully embed themselves within the business fabric of the company.

4) Don’t Forget About Data Ethics — Your Customers Won’t!

Lastly, companies would be wise to seriously invest in establishing well-understood policies and practices that ensure the ethical use of data by their organizations. With just 40.2% of executives reporting that their companies have well-established data ethics policies in place, and just 23.8% saying that the industry has done enough, a growing critical mass of pundits are calling this out as an area for urgent attention.

To sum up

Companies have every opportunity to use data, analytics, and AI to transform their businesses. Now is the moment to rethink how these investments are being made. It is time for data leaders to deliver transformative business outcomes. This is the moment to move forward and learn from the lessons of the recent past.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI business models

Mon, 30 Jan 2023 14:22:18 GMT

Generative artificial intelligence products have many hurdles to overcome before fulfilling the wildest hopes and fears that they’ve inspired since OpenAI introduced ChatGPT in November.

The enormous processing capacity needed to run modern AI systems is shaping their technical development and business model. OpenAI Needs Billions to Keep ChatGPT Running.

To a user firing up OpenAI ’s chatbot hoping to generate automated haikus about the American Revolution or recipes for Spam casserole, the product’s basic interface and instantaneous answers can seem simple, even magical.

On the other side of those queries, though, an immense amount of work is going on. OpenAI’s ChatGPT chatbot requires far more computing power to answer a question than Google takes to respond to a web search. The startup’s current offering is good enough to inspire speculation about a world in which it and programs like it take over some disruptive proportion of the work that only humans can do today. But even if that’s where the economy is headed, getting there is beyond the average startup’s capacity.

Generative artificial intelligence products have many hurdles to overcome before fulfilling the wildest hopes and fears that they’ve inspired since OpenAI introduced ChatGPT in November. The service has suffered regular outages; the problem stems from the technical challenges that come with running any suddenly popular website, rather than with the computing power needed to run its AI models, according to OpenAI. ChatGPT also has the potential to give incorrect information , and in its current form it doesn’t have sufficient information to answer questions about recent events. These are all thorny issues that it and its competitors will likely be grappling with for years.

But the challenge of computing power in particular is likely to shape the development of the field, and potentially the products themselves. As organizations such as OpenAI seek to turn a profit, they may have to start charging for services that are now free. Some companies could look for ways to make more targeted products with computing needs that aren’t as intensive. And the cost of computing is already influencing which entities will have influence over the AI products that seem set to shape the future of the internet.

On Jan. 23, Microsoft Corp. announced a multiyear investment in OpenAI. A person familiar with the deal, requesting anonymity to give non-public business details, put its value at $10 billion. Much of that value lies in Microsoft gaining the right to almost half of OpenAI’s financial returns in exchange for giving OpenAI access to computing power on Microsoft’s cloud network, Azure. Other general-use AI systems are similarly tied to one of the large cloud computing companies, even when the organizations building the models are independent.

Clement Delangue, chief executive officer of Hugging Face Inc., which runs a repository of open source AI models popular with startups, says the industry runs the risk of “cloud money laundering.” This term describes what happens when startups have access to enough money and subsidized computing power to sidestep assessments of whether it makes sense to use certain techniques to solve particular problems. This dynamic should be avoided, he says, “because it creates kind of unsustainable use cases for machine learning.” He says it’s important to have precise calculations of what generative products actually cost to run.

One thing is clear: These systems are monumentally expensive. The first step to building one is sucking up huge volumes of data—text, photos or art—from across the internet. After that, such data is used to train an AI model. For the biggest models, this process runs into the millions of dollars even before considering the cost of specialized engineers, such as language experts, says Rowan Curran, an analyst at Forrester Research. And the more data a system is built on, the more computing power it likely needs to answer a query. Each new question has to run through a model that includes tens of billions of parameters, or variables, that the AI system has learned through its training and retraining.

The GPT-3 system that ChatGPT is built on uses 175 billion parameters, which expands its versatility while also making it especially power hungry. Many of the most popular models on Hugging Face have about 10 billion parameters, Delangue says. Stability AI’s Stable Diffusion, an open source rival to DALL-E , OpenAI’s image generator, has about 1 billion. But subsequent versions could be larger, says Tom Mason, Stability AI’s chief technical officer. “I think there’s a trend this year that the models are getting bigger,” he says. At the same time, he says that people in the field are all working on improving the efficiency of the underlying technology in ways that could offset this increase.

In December, OpenAI Chief Executive Officer Sam Altman tweeted that ChatGPT’s average cost per query was “probably single-digit cents per chat.” A Morgan Stanley analysis put it at 2¢. That’s about seven times the average cost of a Google search query, according to Morgan Stanley, and it can quickly add up in products that operate on such a large scale.

A spokesperson for OpenAI says it’s making progress on improving its efficiency, and it continues to seek a balance between distributing its technology as widely as possible and finding a path to commercial viability.

If everyone uses massive, general models rather than smaller, more specific ones, the computing demands are probably not sustainable right now, Delangue says. But some companies are already looking for an opening by creating models to serve a specific market. “One way for startups to go is to identify their area of specialization and focus their training models only on the relevant data,” says Preeti Rathi, general partner at Icon Ventures Inc. in Palo Alto. Icon has invested in Aisera , a company creating a system specifically targeted at helping resolve customer service tickets.

Particularly in the short term, other startups will build products using general models made by OpenAI, Alphabet Inc. ’s Google or Stability AI, then customize them or add domain-specific data to target specific markets, says Navrina Singh, CEO at startup Credo AI , which is working on governance systems for new AI applications. Others are looking to design products that won’t rely on the biggest tech companies.

Large cloud companies are eager to work with startups that are hungry for computing power, in part because they have the potential to become long-term customers. Amazon’s cloud unit in November unveiled a partnership with Stability AI. Google has a ChatGPT-like system called Lamda that it hasn’t released publicly, and the Wall Street Journal has reported that it has had talks to invest $200 million in Cohere, an AI startup that creates language software developers can use for things such as chatbots. “There’s a somewhat of a proxy war going on between the big cloud companies,” says Matt McIlwain, managing director at Seattle’s Madrona Venture Group LLC, which invests in AI startups. “They are really the only ones that can afford to build the really big ones with gazillions of parameters.”

After an extended period of technological innovation during which a handful of companies consolidated their dominance of the internet, some people see AI developing in a way that will only strengthen their grip. There have been calls for regulation of the emerging field, and some countries or universities are setting up publicly owned supercomputers as alternatives. Bloom, the largest open source rival to GPT, was trained on a French public supercomputer called Jean Zay. Delangue has said that a French research tax credit should be expanded to cover the costs of computers for machine learning research, and he urged other countries to take similar action.

The tech titans have positioned themselves well to avoid being disrupted, says Forrester’s Curran. “We’re in the beginning stages of what could possibly be a huge explosion of ideas and creativity around business creation,” he says. “But the big players are all doing lots of work here. So the chance that they would be totally blindsided by a startup isn’t huge.”

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Retention strategies for a downturn period

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 01 Jan 2023 16:26:58 GMT

Know your customer cohorts and their health.

Mitigating panic churn during a downturn can minimize the disruption of economic uncertainty. Key to doing this is by focusing on at-risk customer cohorts. Not only is retention less vulnerable than acquisition to the short-term swings of a bad economy, but the rule of thumb that it costs five times more to win a new customer than to keep a current one becomes even more extreme in a downturn.

The markets are down, currencies are down, and inflation is up. While every downturn is different, these signals are familiar and they suggest we’re nearing a recession. New sales leads will start to slow, win rates will narrow, deals will take longer to close, and customers will start to churn. It can be a scary time, but we’ve been here before — and there’s a playbook for how to weather these conditions.

It's very inspiring to see how companies that focused relentlessly on existing customers were able to ride through the uncertainty with the least disruption.

Here are three practices I saw that worked.

Know your customer cohorts and their health.

To mitigate short-term or panic churn, you need to understand the nature of the downturn and which customer verticals will be affected. “Customer Success” teams, whose role requires them to understand why customers churn, renew, and grow, are generally already looking at this data. Give this team the mandate, people, and purpose-built technology to group your customers into cohorts, with particular attention paid to the industries or groups most affected by the crisis.

We saw this work when Untappd, a geosocial networking platform for beer aficionados, spotted a pattern of distress affecting their smallest customers. As lockdowns took effect, these micro-businesses in the restaurant and bar industry were trying to cut every cost. Knowing which customers were feeling the most pain was key to how Untappd was ultimately able to retain them.

Early in the pandemic they catagorized their own customers into five new cohorts, ranging from “business as usual” to “engage immediately” and “extreme risk,” using a matrix of data points that included each customer’s industry, company size, sentiment, account tenure, and product usage.

They then began by engaging with their customers in the most at risk cohorts. They studied how other customers in their industry were dealing with its challenges and decided to be as flexible as they could with them. If the conversation resulted in them asking for different things, they gave them the benefit of the doubt. If they asked for a pause in payment, the answer was yes.

Create repeatable plays to stop churn.

Once you identify the customers most at risk of churn, your job is to find ways to help them. And while finding individualized solutions can make a difference, you may find that there are similarities across what cohorts need. In this case, finding and implementing repeatable solutions can make a lot of sense.

For example, Untappd targeted their most at-risk customers with a “pause program” that mitigated churn by giving them the option to pause their subscriptions until their venues reopened. Once the lockdowns lifted, paused customers found it easy to restart their subscription and take advantage of new features like touchless menus, developed by Untappd during the interim.

Monitor product usage with an eye towards long-term adoption.

Even if you’ve created strong cohorts of customers, there may still be surprises and a customer who you didn’t expect to be suffering may start to slip. Reaching these customers is just as crucial as reaching your most at-risk cohorts.

To do this, you’ll want to monitor your product usage data closely and reach out to any customers whose usage dips. Your Customer Success team should ask them how they’re being impacted by the downturn, and offer any resources and expertise.

Even though you may be focusing on the short-term aspects of retaining at-risk customers, you don’t want to lose sight of adoption as your long-term goal. Making sure all clients are receiving the maximum benefit of your product should be a core part of navigating a downturn.

Is your Customer Success team equipped to succeed?

Facing today’s uncertainty, the question CEOs should be asking themselves is whether their Customer Service teams have the resources they need to fight for and retain customers. Our research suggests that most do not.

Operationalizing a Customer Success team requires money and people with operational and data skills. In a booming market, these resources are plentiful; in a downturn, they’re finite and hotly contested. Still, I’ve seen evidence that strengthening retention works. Not only is retention less vulnerable than acquisition to the short-term swings of a bad economy, but the rule of thumb that it costs five times more to win a new customer than to keep a current one becomes even more extreme in a downturn. As new business slows, and new leads are harder to come by, the multiple expands.

Sometimes, to focus on retention, it’s necessary to shift resources away from other units, even sales. And yes, it’s scary to shift resources away from new business. Your Customer Success team, meanwhile, should accept a tradeoff too: the expectation to deliver higher retention with their increased resources. Set them a higher target, hold them accountable — and watch your ROI accrue.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

A new approach to Data-Driven Performance Management

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 01 Jan 2023 16:09:35 GMT

Meritocracy matters.

Reliable, accurate, and bias-free measures of employees’ job performance are notoriously elusive. And while companies are awash with data about their employees, their ability to translate them into trustworthy markers of performance is at best a work in progress. Research shows that self-ratings and supervisory ratings of job performance overlap by merely 4%. While true meritocracy in organizations is impossible and performance won’t ever be entirely measurable, organizations can become more merit-based, fairer, and overall more successful if they identify KPIs about performance within their organizations and use the right data to measure it.

Meritocracy matters. A company that rewards talent, effort, and achievement can be expected to outperform those where nepotism, systemic biases, toxic politics, and sheer incompetence are prevalent; it’s just a matter of time. The rise of people analytics, innovations in the field of HR technologies, as well as the inevitable integration of AI and machine learning algorithms into talent management practices, are all signals of the same underlying phenomenon: a relentless quest for a more rational, fair, and evidence-based approach to managing workers, and unlocking human potential at work.

Yet reliable, accurate, and bias-free measures of employees’ job performance , the key to managing performance more fairly, remain notoriously elusive. Indeed, despite ubiquitous tech tools, not to mention avalanches of fancy data, visualizations, and dashboards, the reliable quantification of workers’ value creation remains as distant from real-world management realities today as it was 40 years ago.

To be sure, large companies are awash with data from internal communication systems, project management software, survey platforms, and even sensors, but their ability to translate them into trustworthy markers of human performance is at best a work in progress. Despite clear advancements on the tech side , and some undeniable achievements in data science, there is still a large gap between workers’ output, performance, or value generation and their career success, status, or seniority. Ask any organization to identify their top employees, including managers and leaders, and to prove their selection with hard evidence or data, and they will look at you perplexed. In most organizations, success is more likely a reflection of winning a popularity contest , or harnessing a strong reputation, than actually contributing to the organization’s success.

To overcome this problem, one first needs to identify what constitutes real, meaningful performance, tying to the high-level goals of the organization. This means creating a continuum of performance metrics from individual employees to teams, divisions, and the entire company.

Quantifying the Theoretical

A common reason for this organizational deficit is that job performance, defined as an employee’s contribution to organizational effectiveness (including not just their ability to effectively carry out valuable tasks, but also good citizenship and the absence of detrimental or counterproductive work b ehaviours), cannot be directly measured, especially by relying on spontaneous or intuitive observation. Job performance is a theoretical construct, just like happiness, integrity, or narcissism. We can at best observe its indicators or manifestations, but to observe these accurately requires the right incentives, a model, expertise, effort, and reliable evaluation tools that need to be refined and internally validated. That is: not our instincts.

As decades of academic research in organizational psychology indicate, the most common approach to quantifying someone’s job performance is to rely on subjective ratings, whether by the employee (self-rating of performance) or their manager (supervisory ratings). The typical correlation between self-ratings and supervisory ratings of job performance is merely 0.22 , which translates to a trivial 4% overlap between the two. In other words, 96% of the variability in employees’ self-rated job performance is unrelated to how their managers’ view their performance. Unsurprisingly, employees are often surprised when they discover what their bosses think of their performance, a feeling that managers reciprocate when they discover what employees think of their own performance.

Much of this tension stems from organizations not putting in the effort to define quantitative measures of performance across different roles and levels of the organization. This will only ever capture some of what could be considered the true, holistic performance of the group or individual. Without agreed upon, quantitative KPIs, performance evaluation becomes even more political, emotional, and prone to biases.

While employees are generally too generous in their self-evaluations of performance, there is not much evidence for the superior accuracy of supervisory ratings in measuring workers’ true contributed value or output, though aggregating ratings of different managers or sources, including peers, will significantly boost reliability . Needless to say, it is not just possible, but also desirable, to improve how others see us through factors unrelated to our actual job performance. For example, if you are a boss, being friendly with your employees, giving them freedom and flexibility, and ensuring that they have a good time at work, may all translate into positive 360-degree feedback ratings of your reputation, without actually boosting your team’s performance. Same goes for employee engagement, which only correlates with team performance at 0.3 (that’s merely a 9% overlap).

Creating Your Organization’s Hierarchy of Needs

After individual performance metrics are identified, the next challenging problem is building the connective KPI tissue to top-level business such as financial KPIs, revenues, profits, turnover, growth, and innovation.

This requires organizations to define a hierarchy of quantitative and qualitative KPIs. The KPI definition process needs to include nearly everyone in the organization, from the CEO to frontline employees, identifying what metrics matter for their role/team/division and how that relates to outcomes defined as important in related groups. These definitions then need to be validated by an analytics team (at least for the quantitative KPIs). Tooling can then be put in place to continuously measure these metrics and provide feedback across the organization. As the predictive power of these KPIs inevitably changes with business conditions, the definition process needs to be repeated.

It is also clear that people don’t enjoy having their workplace data microanalyzed, even if they live in a world in which their data has been extensively traded and commoditized by apps, phones, and wearables. Some surveillance algorithms are in fact Orwellian . Not getting any feedback about what data is being scraped and analyzed undermines trust. And getting feedback – for instance, an automated email that generalizes “you’re in too many meetings” — may trivialize the tech being used, or confirm suspicions that the tech is intrusive. As a result, employees will likely regard the underlying analytics as either creepy or crappy. This is why the KPI definition process has to be transparent and inclusive. Executives shouldn’t think that they can design metrics in a vacuum, or that just because a metric is predictive that it’s ethical to use. Creating ethics committees with independent members, engaging in regular discussions with stakeholders across the organization, and admitting when certain metrics are a best guess rather than the absolute “right” metric are essential for building a fairer, faster, and more data-driven organization.

The ever-increasing complexity of work has also cemented the importance of effective, large-scale collaboration for organizational success. The success or failure of large projects is less about the performance of a single individual and more about how people work together. Organizational success is fundamentally not an individual phenomenon. So assessing and rewarding performance at the individual level, while necessary because individuals are ultimately the ones who get paid and promoted, is scientifically at odds with what is actually important in modern organizations: working with others, cooperating effectively, and making others better.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to “democratize” data science and AI.

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 01 Jan 2023 15:16:00 GMT

The multidimensional impacts of the Citizen Data Scientists.

New tools are enabling organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks.

Organizations considering implementing these tools should take five steps:

Provide ongoing education,
Provide visibility into similar use cases throughout the organization,
Create an expert mentor program,
Have all projects verified by AI experts,
Provide resources for inspiration outside your organization.

Until recently, the prevailing understanding of artificial intelligence (AI) and its subset machine learning (ML) was that expert data scientists and AI engineers were the only people that could push AI strategy and implementation forward. That was a reasonable view. After all, data science generally, and AI in particular, is a technical field requiring, among other things, expertise that requires many years of education and training to obtain.

Fast forward to today, however, and the conventional wisdom is rapidly changing. The advent of “auto-ML” — software that provides methods and processes for creating machine learning code — has led to calls to “democratize” data science and AI. The idea is that these tools enable organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts.

In theory, making data science and AI more accessible to non-data scientists (including technologists who are not data scientists) can make a lot of business sense. Centralized and siloed data science units can fail to appreciate the vast array of data the organization has and the business problems that it can solve, particularly with multinational organizations with hundreds or thousands of business units distributed across several continents. Moreover, those in the weeds of business units know the data they have, the problems they’re trying to solve, and can, with training, see how that data can be leveraged to solve those problems. The opportunities are significant.

In short, with great business insight, augmented with auto-ML, can come great analytic responsibility. At the same time, we cannot forget that data science and AI are, in fact, very difficult, and there’s a very long journey from having data to solving a problem. In this article, we’ll lay out the pros and cons of integrating citizen data scientists into your AI strategy and suggest methods for optimizing success and minimizing risks.

The Risks of Democratizing AI in Your Organization

Putting your AI strategy in the hands of novices comes with at least three risks.

First, auto-ML does not solve for gaps in expertise, training, and experience, thus increasing the probability of failure. When used by trained data scientists, auto-ML tools can help a great deal with efficiency, e.g. by writing code quickly that a data scientist can validate. But there are all sorts of ways an AI can go technically or functionally sideways, and non-data scientists with auto-ML may run straight into those pitfalls. For instance, one of the issues in ensuring a successful AI project is the ability to appropriately handle unbalanced training data sets. A data set of transactions that contain few instances of suspicious transactions — let’s say 1% — must be sampled very carefully for it to be usable as training data. Auto-ML, however, is an efficiency tool. It cannot tell you how to solve for that problem by, for instance, subsampling, oversampling, or tailoring the data sampling given domain knowledge. Furthermore, this is not something your director of marketing knows how to handle. Instead, it sits squarely in the expertise of the experienced data scientist.

Other risks of failure in this area also loom large, particularly those that pertain to creating a model that is ultimately useless. For instance, the model is built with inputs that aren’t available at run time, or the model overfits or underfits the data, or the model was tested against the wrong benchmark. And so on.

Second, AI infamously courts various ethical, reputational, regulatory, and legal risks with which AI experts, let alone AI novices, are not familiar. What’s more, even if they are aware of those risks, the AI novice will certainly not know how to identify those risks and devise appropriate risk-mitigation strategies and tactics. In other words, citizen data scientists will increase these risks, and brands are putting their reputations in the hands of amateurs with potentially serious implications on the organization’s clients, customers, and partners.

Moreover, the guardrails companies have built to mitigate this risk were built with traditional data scientists in mind. While many organizations are creating AI ethical risk or “Responsible AI” governance structures, processes, and policies — and others will soon join suit in response to new regulations in the European Union ( The EU AI Act ) and Canada ( The AI and Data Act ) roll out in the coming years — they’ll need to extend that governance to include AI created by non-data scientists. Given that spotting these risks takes not only technical expertise but also ethical, reputational, and regulatory expertise, this is no easy feat.

Third, related to both of the above, having AI novices spend time developing AI can lead to wasted efforts and internal resources on projects better left on the cutting room floor. And potentially worse than that, faulty models that get used may lead to significant unforeseen negative impacts.

How to Prepare Your Organization for Democratized AI

All AI should be vetted for technical, ethical, reputational, regulatory, and legal risks before going to production, without exception. While citizen data scientist-created models carry more risks, that doesn’t mean that the auto-ML approach cannot work. Rather, for those organizations that determine it is an effective part of their AI strategy, the key is to create, maintain, and scale appropriate oversight and guidance. Here are five things those organizations can do to increase the likelihood of success.

Provide ongoing education.

Published best practices and guidelines enable citizen data scientist to find answers to their questions and continue to learn. For instance, there are best practices that pertain to the issues referenced above: unbalanced data sets, over and underfitting models, etc. Those best practices should be readily available internally and searchable by anyone and everyone building a model. This can be delivered in various forms, including an internal wiki or similar application.

Provide visibility into similar use cases within the organization.

One of the most powerful educational tools you can provide to your non-data scientists is examples or case studies they can use as templates for their own projects. In fact, those other projects may have resources that the team can use, e.g., NLP models that are plug and play, a model methodology used to solve a problem, and so on. This has the added benefit of speeding up time-to-value and avoiding the duplication of work and thus a waste of resources. In fact, more and more companies are investing in inventory tools to search and reuse various AI assets, including models, features, and novel machine learning methods (e.g., a specific type of clustering method).

Create an expert mentor program for AI novices.

This should be tailored to the project so that it provides problem-specific guidance. This also includes the ability to get an AI idea vetted by an expert early on in the project discovery phase, so as to avoid common pitfalls or unrealistic expectations for what AI can provide. Perhaps most important here is determining whether the data the organization or business unit has is sufficient for training an effective and relevant model. If not, a mentor can help determine how difficult it would be to acquire the needed data from either another business unit (that may store data in a way that makes it difficult to extract and use) or from a third party.

Ideally, mentors are involved throughout the AI product lifecycle, from the concept phase all the way through to model maintenance. At earlier stages, mentors can help teams avoid significant pitfalls and ensure a robust roadmap is developed. In later stages, they can play a more tactical role, like when the team needs guidance with a deployed model that isn’t performing as well as anticipated. Indeed, this function can also be very useful for experienced data scientists. Novice and expert data scientists alike can benefit from having an expert sounding board. It’s important to stress here that potentially two kinds of mentors are needed: one to solve for technical and business risks, the other to ensure compliance with the AI ethics or a Responsible AI program.

Verify all projects by experts before AI is puintoin production.

Mentorship can play a crucial role, but at the end of the day, all models, and the solutions in which they’re embedded, need to be assessed and approved for deployment by experts. Ideally, this should be performed by two distinct review boards. One board should be comprised of technologists. The other board should also include technologists, but should primarily consist of people from risk, compliance, legal, and ethics.

Provide resources for education and inspiration outside your organization.

Any group in any organization can suffer from groupthink or simply a lack of imagination. One powerful way out of that is to encourage and provide resources for everyone who builds AI models to attend AI conferences and summits, where the creativity using AI across all industries and business units is on full display. They may see a solution they want to procure, but more importantly, they may see a solution that inspires them to create something similar internally.

Conclusions

AI is in its infancy. Organizations are continuously trying to determine how and whether to use AI, particularly against a backdrop of doubting its trustworthiness. Whether you trust AI novices with your AI strategy or not, following these steps will ensure a disciplined approach to AI, will maximize the benefits that AI can bring, and will minimize potential risks. Put simply, following these five steps should be a part of basic AI hygiene. Whether to democratize or not democratize AI is up to you.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to leverage on AI in your organization

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 01 Jan 2023 14:47:00 GMT

One misconception that exists in the minds of many managers is that AI is a decision maker that provides end-all answers and the functionality will cut costs or reduce the need for costly labor. The reality, however, is that AI remains more of a decision-support mechanism.

Different forms of AI can improve performance through prediction; automation of routines; and identification of images, keywords, and patterns in voice and text.

However, organizations often struggle with knowing where investments in AI will really pay off. Companies need to:

Ask whether they really need AI,
Pick a task to start with, not a project
Identify what data and complimentary systems it will require,
Adjust expectations around accuracy accordingly,
Not rush to deploy it enterprise-wide,
Ask whether they have the necessary skills to maintain an AI,
Decide whether the returns will outweigh the costs.

In recent years, AI has become more powerful and its applications to business have increased dramatically. As a result, companies that hadn’t seriously considered using AI are taking a fresh look. The appeal is obvious: different forms of AI can enhance performance through, prediction, automation of routines, identification of images essential to operational activities, or the identification of keywords, phrases and patterns in voice and text for information management.

Where organizations often struggle is in knowing where to invest in an AI project that will really pay off. But if AI hasn’t been a part of your company before, it can be hard to know where the real potential — and risks — lie. While AI may promise valuable gains, those gains come with a price tag, and leaders should feel confident that they’re picking the right project before they commit.

If your company is thinking about adopting AI, you should consider how it might create value, what good first projects might be, and whether you have the right talent on staff for your efforts to succeed in the long run. A first AI project can be daunting, but knowing which factors to focus on will bring the project down to earth — and clarify whether it’s worth the investment at all.

Will AI create value for your company?

Does your company really need AI?

“Why do we think this investment will be worth it?” is one of the first questions you’ll need to answer. That means knowing which source of operational pain (e.g., redundancy in particular tasks or bottlenecks in operational flow) you’re trying to address or where you’re aiming to improve efficiency and innovative competitive advantage (e.g., smart products and smart retailing). AI projects should address processes that significantly impact cost, revenue streams or resource allocations, where the ultimate result can be a noteworthy impact on the bottom line.

Good candidates for AI to provide value include:

Activities that are very time-consuming and labour-intensive (e.g., reading extensive documentation to categorize its actionable items).
Workflow activities that require intensive scanning of images.
Processes that can be augmented with voice analysis (e.g., customer support routing).
Enhancing predictive accuracy in areas such as customer behaviour or general forecasting, which applies to a multitude of industries such as insurance, finance, marketing, and even agriculture.

Be prepared to accept that AI might not actually be the answer you’re looking for. Even if you find a promising candidate for an AI project, be realistic about where the human element is critical (e.g., validating AI output or red flag verification) and AI may not provide much value. If you plan on making an investment in AI, you want to be sure it will make a difference.

Pick a task, not a project, as your entry point.

AI is generally task oriented. For the first AI experiment at your company, aim to pick a high-value task that is data-driven.

Consider an example from the healthcare sector: identifying patients who may be in a “high probability of falling” category. This is a high-value task because a fall can mean injury to the individual, the need for more complex care, and even legal action. Being able to identify which patients are at risk of falling and adding extra precautionary procedural methods to mitigate the risk of falling can offer real value. It’s a clearly defined task, with a vast amount of data that can be used to train a predictive AI capable of flagging patients as they are admitted for in-patient care.

Know what data and complementary systems you need.

Good data is the lifeblood of a successful AI project. Before committing to a project, you need to formally investigate the types and amount of data required to do it well, whether there are any restrictions on using that data (such as privacy regulations), and whether it’s in a reasonably accessible format. This investigation is not limited to internal data of an organization but data that may exist in external sources. A data engineer can be extraordinarily helpful here. If your data resources are not in order, you may need to focus on data first and pursue AI later.

Once you’ve identified that adequate data is available for AI to process, you’ll need to ensure that it’s possible to integrate an AI’s output into the target task. In other words, can it seamlessly plug into automated operations that depend on its recommendations? If your model is built with Python, will that be compatible with your systems? This is where IT experts play a vital role. It’s a rough awakening when you’ve gone through all the work of creating effective models, only to realize that using the output introduces yet another lengthy and cumbersome project.

Adjust your expectations of accuracy.

AI is a powerful tool, but it’s not magic. The type of AI method you’re deploying, the data you have available, and the task you are looking to focus on can all dictate the rate of accuracy — and the return on investment. Understanding what affects accuracy — and why — can help you set reasonable expectations for what the success of the project looks like. For example, image recognition/computer vision tends to be more reliably accurate than predictive forecasting applications.

Simply put, it’s key to understand the type of AI you are deploying and what the outcomes will be used for in order to estimate the impact on your bottom line.

Don’t rush to deploy enterprise-wide.

Just because an AI works well for one task doesn’t mean it will for others. In other words, consider the task of deploying AI to enhance compliance issues in your organization (e.g. identify sources of non-complaint activities). Models will be effective in identifying red flags according to a particular functional area (booking trades in a fin-tech setting). However, that model will not bplug-and-playplay to other areas to address compliance. AI projects will have to be conducted according to functional procedures and corresponding data that drive them.

Be realistic about whether you have the skills to maintain AI.

Just as processes change, the data that was essential to driving a model one month may be less important a few months later, and this can alter the effectiveness/accuracy of AI. New and more recent data emerges, process drivers change and with that, AI deployments need to be re-optimized. This requires the incorporation of data engineers, data scientists, and IT personnel to provide support for the maintenance of the system to ensure consistently effective AI. This goes to the type of organization. Larger companies already have the infrastructure (e.g. IT and data engineers). The addition of practising data scientists may be enough to maintain internal projects. Of course, the option to engage outside vendors is always a viable play and may be a good way to break into AI deployment and learn.

One notion to keep in mind: if you believe your company has the characteristics that will benefit from AI deployment, it may be worth investing in the personnel to make it an ongoing integral part of your operations.

Will returns outweigh the costs?

One misconception that exists in the minds of many managers is that AI is a decision maker that provides end-all answers and the functionality will cut costs or reduce the need for costly labor. The reality, however, is that AI remains more of a decision-support mechanism. Even with accurate image recognition or effective natural language processing (NLP), an AI initiative will typically enhance stages of a process, and not change the ultimate outcome. For example, NLP can enhance the ability to categorize documents and reduce the need to deploy time and labour-intensive resources to accomplish the task. However, the end result will most likely be a reduction in resources deployed to this task with the excess personnel being deployed to moreknowledge-intensive tasks in the organization. The result is definitely positive, but the ROI is not clear-cut.

AI Projects in Reality

Consider how one Global 500 industrial products company deployed AI to optimize their customer service process regarding customer email interactions.

The need: The company received a large volume of customer service emails. Responding to them involved repetitive processes, which made the task a promising candidate for AI.

The method: The initial project would use natural language processing (NLP) and custom-built classifiers to determine how to route customer emails. This would eliminate the time-consuming and error-prone task of assigning emails to the correct department by staff. It was expected that this would speed up response time by as much as 50% and increase the number of inquiries each customer service agent could handle. In order to move forward, collaboration was established between data scientists, IT personnel and data engineers.

The data: This was a natural entry point as the company had amassed tens of thousands of human-classified emails. This formed a robust training/testing data set that had high accuracy which was a crucial element for this type of project and is often overlooked.

The integration and outcomes: This narrow scope of focus allowed for relatively good accuracy of the classifier; 80% or better. This was selected as the benchmark for classifying inbound emails into one of 10 main categories and then up to 30 subcategories. At this rate, it would be more efficient than human routing and save considerable time and resources. In order to roll the model out into production, data scientists had to implement additional code to integrate the Python model into the existing email-based systems in the organization.

The ultimate result of the project was the demonstration of how AI can impact a time-consuming and labor-intensive business process, which ultimately drove other applications in the organization.

The results of starting AI the “right way.”

The key to a successful implementation of any new strategic technology is due diligence. In the case of AI, which can be highly complex, companies need to know the capabilities of AI methods and consider its deployment to the right processes, ones where it can make a difference. More specifically, due diligence with AI requires collaborative brainstorming among data engineers, data scientists, internal process SMEs and executives. A few days of focused deliberation can enhance the likelihood of successful roll-outs that optimize resources and drive competitive advantage or can avoid the painful purgatory scenario of new technologies remaining in the perpetual state of testing.

If AI is a fit, companies can let the technology do the heavy lifting of routine-based processes or identify seemingly unknown patterns in vast data resources. But in order to get there, you need to find the approach that works for your company. Once you do, you may see new opportunities opening up all around you.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The key ingredients for an innovation driven organization

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 06 Dec 2022 13:48:24 GMT

Many organizations seek innovation, but very few know how to find it.

Every company strives to be innovative, but most are missing key ingredients. How can you identify which ingredients your organization needs — and which employee styles can fill in the gaps? We distill four key innovation styles that can lead to success — generators, conceptualizers, optimizers, and implementors — and explains how common they are across sectors. Then, they outline a four-part framework for ensuring your team or organization has all four styles represented.

How do you identify and empower innovators in your company?

Collectively, we have spent over 40 years researching this question. Our research on innovation styles identifies and examines the different preferences and roles people take on when pursuing innovation. By understanding this concept, organizations can better identify where specific people are needed and who should work together to generate new breakthrough ideas.

Our latest study relies on data collected between October 2006 and January 2021, across as many people in as many organizations as possible.

Each respondent told us about what they like to do and what they do well when they solve problems (and what they do not like or do not do well). These answers revealed an individual’s preference for one of four unique innovation styles , each of which maps onto a distinct phase of a four-stage innovation process. Each style has a role to play in your organization, starting with finding new problems (generators), thoroughly defining problems (conceptualizers), evaluating ideas and selecting solutions (optimizers), and implementing selected solutions (implementers).

All four styles are necessary for innovation. Understanding which employees fall into which style enables an organization to manage their innovation efforts more effectively. However, in our experience, most organizations are lacking in some innovation styles — particularly generators — and we will be providing steps to help overcome this deficiency.

The Four Innovation Styles, Defined

Generators: Generators find new problems and ideate based on their own direct experience. For them, physical contact with, and involvement in, the real-world alerts them to unresolved gaps and inconsistencies — problems that might be worth addressing as opportunities and possibilities. However, generators only find these problems at a high level; they do not necessarily gravitate towards articulating a clear understanding of a problem’s specifics or its potential solutions. Across all organizational levels, generators are rare. Overall, just 17% of our sample were generators: 19% of executive managers, 18% of middle managers, 15% of supervisors, and 16% of non-managers. This means that, unless leaders are deliberate about including generators on teams, they may not be represented at all. Generators are perceptive of the world around them, and initiate and proliferate opportunities. So, a lack of generators makes it more likely that an organization will miss opportunities for valuable change. Given the importance of cognitive diversity in groups , this is a potential detriment to innovation performance. That said, there are some occupations where generators are more common than others. School teachers (56%), academics (38%), and artists (34%) are the occupations with the greatest proportion of generators; engineering (8%), strategic planning (9%), and manufacturing (9%) have the lowest proportion. This means a lack of generators may be acutely felt on certain teams in certain fields. For instance, there is a 71% chance a four-person team from a strategic planning department has no generators at all.
Conceptualizers. Conceptualizers define the problem and prefer to understand it through abstract analysis rather than through direct experience. Like generators, they like to ideate; but in contrast they prefer to model the problem clearly — integrating the various parts, relationships, and insights together — which can then be used as the basis for one or more solutions. Conceptualizers are the second rarest innovation style, making up only 19% of the sample. They are relatively evenly represented across most occupational levels, with 17%, 18%, and 17% of non-managers, supervisors, and middle managers as conceptualizers, respectively. But more executives — 25% percent — are conceptualizers. This likely reflects the specific cognitive demands for that role: executive managers must strategically plan for more distant goals, rather than execute more tactical tasks. Conceptualizers are most common in jobs where understanding the definition of the problem is vital, such as organization development (61%), strategic planning (57%), and market research (52%). Conversely, conceptualizers are rarest in operations (7%), technical support (11%), and project management (13%).
Optimizers. Optimizers evaluate ideas and suggest solutions. They prefer to systematically examine all possible alternatives in order to implement the best solution among the known options. Optimizers are most common among lower occupational levels (27% of non-managers) and decrease with a rise in occupational levels (23% of supervisors, 22% of middle managers, and 20% of executives). Because most solutions are implemented at lower levels of hierarchy, it makes sense that occupations at these levels are more likely to engage in optimization. Optimizers are also most common in positions where practical, precise, and detailed plans, processes, and solutions are sought. Engineering (43%), manufacturing (38%), and finance (36%) had the highest proportion of optimizers. Product developer (9%), academic (10%), and school teacher (11%) had the lowest proportion.
Implementers. Implementers put solutions to work. They enthusiastically (and sometimes impatiently) take action, experimenting with new solutions before mentally testing them and then make adjustments based on the outcome of these experiments. Implementers are the most common innovation style, representing 41% of our survey respondents. Thirty six percent of executive managers are implementers, but are about as common among non-managers (41%), supervisors (44%), and middle managers (43%). Individuals working in positions that require achievement of short-term results, such as IT operations (64%), customer relations (51%), and administration (50%) favor the implementer style. Artists (6%), strategic planners (7%), and designers (10%) are least likely to be implementers.

Challenges for Organizations

Two findings should stand out to managers. First, innovation styles are, generally, not evenly distributed. It is striking that only about 17% of individuals in our study were found to be generators while 41% were implementers. Second, people tend to sort into different occupational roles and levels of management based on their innovation style. For instance, generators are predominantly found in non-industrial occupations and conceptualizers are most common in strategic planning and organizational development.

These two findings contribute to the same problem: the organizations and teams you are working with are likely to lack the right balance of styles and be insufficiently cognitively diverse. If cognitive differences are unevenly distributed (e.g., there are more implementers and fewer generators) — and if people will choose roles and organizations based on their innovative style preference (e.g., generators are more likely to become artists and teachers, not executives and engineers) — we would expect most organizations and teams to lack the ideal cognitive diversity for innovation.

SMRT Innovation Framework

To innovate more effectively, organizations need to do two things: specifically, cultivate more generators who can find problems; and more generally, ensure skillful representation of all four innovation styles. To do both, we propose a four-step method for innovation that we refer to as the “SMRT” framework:

Structure : Achieving the right ratio of innovation styles.
Model : Demonstrating importance of an innovation style top-down.
Reward : Creating incentives for problem-finding.
Train : Creating opportunities to learn about all styles.

The best-in-class organizations we identified exemplify these tactics. While it was rare that any one company in our research deployed more than one of these four, we argue that these tactics are complementary and that using all four will supercharging innovation at your firm.

1) Structure.

Managers and their teams tend to get stuck when attempting to solve complex, ill-defined problems because there is a wide divergence in potential solutions. If managers can identify and isolate the exact stage of the innovation process where they are facing this divergence, they might have some sense of the talent they need to converge towards a solution. But since they are rarely able to do this, it means that managers do not have the right team structure for these situations in the first place.

To improve innovation, managers will first want to ask: During which stage of the innovation process do our teams get stuck? Next, managers will need to identify and amplify the missing innovation style that’s needed at that stage. For instance, during a field experiment in a Google hackathon, we found that agile practices stymied innovation because they focused teams on the implementation phase of the innovation process, rather than the idea generation phase. In this type of situation, an organization might benefit from intentionally identifying generators and deliberately amplifying their contributions. If no generators are available, other team members may be asked to role-play as generators instead.

Even if your team struggles with optimization or implementation, this doesn’t have to prevent you from innovating. Take the experiences of a strategy development team for a large American health insurance company. Tasked with recommending a new corporate strategy to senior management, the team was unable to agree on a final recommendation and was struggling with paralysis by analysis. Each time they thought they had a recommendation to forward, someone insisted on revisions to take new information into account or to make the strategy more comprehensive. When we analyzed their innovation profile, we found the team to be composed entirely of conceptualizers, except for one implementer — the administrative assistant. Rather than try to reach a perfect understanding, the team diversified its membership by including people with an affinity for optimization and implementation to help them reach a strategy acceptable to management.

2) Model.

Our research finds that because organizations tend to incentivize and select people with specific innovation styles. For example, organizations that need to get to market hire implementers and incentivize implementation behaviors; organizations that need product improvements hire optimizers and incentivize optimization behaviors; and so on. Yet, the innovation process requires all four innovation styles, or organizations risk succeeding in one area of innovation while failing on another.

Senior leaders, therefore, have a challenge (and an opportunity) to demonstrate the importance of the needed-at-the moment style — top down — to their entire organization. This is possible because an innovation style is a cognitive state and not a fixed personality trait and can be learned from training. In fact, a leader’s specific style is less important than their ability to shift, as needed, during the flow of the innovation process.

Consider Elon Musk and the role he plays at SpaceX and Tesla. At SpaceX, Elon Musk has largely exhibited a generator style: he is well known for blowing up rockets in order to find out how they work (and why they do not). He has even normalized the activity by referring to rocket explosions as “rapid unscheduled disassembles” ( RUDs ). By modeling the importance of generator behavior, he encourages and allows his SpaceX team to do the same. Yet, which innovation style is mission critical can change over time. At his other company Tesla, the challenge today has become innovating on how to manufacture at volume. This innovation requires an optimization mindset. Thus, Musk demonstrates the optimizer style at Tesla: when Model 3 production was drastically behind schedule, Musk was public about sleeping on the factory floor and handling direct oversight over Model 3 production .

Some leaders in our sample demonstrated an exceptional capacity to shift during the innovation process, resulting in substantial organizational performance improvements. For example, like air traffic controllers, many firefighters intensely err towards implementation. In their daily problem-solving activities, they confront situations that need their immediate action towards saving lives or resolving dangerous situations.

But in one department we studied, the new fire chief and his lieutenants felt they had antiquated views and lacked a vision or strategy for the future. As mostly implementers, the team was struggling to create a new strategy — a task that tends to be conceptualizer work. Through modeling behavior by the new fire chief, however, the team established its vision of becoming an elite department. This started by the new fire chief developing a survey questionnaire to distribute to department members as a fact-finding tool (modeling generator behavior). Leading a two-day workshop, the new fire chief and his lieutenants worked with department members to develop a long-term vision supported by six action pillars (modeling conceptualization and optimization). Committees were then created to drive each action forward (modeling implementation).

Cycling through these behaviors allowed the new fire chief to work methodically through the process with his employees to create a strategy they all bought into. As a result, the department aligned funding to increase their staffing and implemented a dual career system to reward/support individuals wanting to specialize in core competencies like hazmat emergencies, medical response, and other core specialties. This allowed them to build higher mastery level versus other departments and gain such recognition from peers.

3) Reward.

Because employees are rewarded for doing their job well, they tend to go out of their way to avoid problems which are outside of their job description. This also means they go out of their way to avoid finding new problems, particularly problems that are more complex, require them to do more work, or require them to work with different departments. This behavior is so prevalent that some researchers have deemed the activity of problem-finding “extra-role behavior” — one that requires individuals to go beyond the boundaries of their jobs.

Our field studies suggest that there is a clear solution to this limitation: companies should make problem-finding attractive for employees by offering rewards for this activity, beyond and in addition to just providing them with the freedom to do it. In 2020, The Wall Street Journal called 3M’s 15% rule, which invited employees to spend 15% of their time working on pet projects, as “ Corporate America’s Most Underrated Innovation Strategy .” In addition, 3M makes problem-finding a component of every employee’s job description by delegating responsibility, encouraging considerable tolerance of mistakes, and ensuring that at least 30% of each division’s revenues comes from products introduced from the last four years .

Having given employees that level of freedom, 3M can then explicitly reward problem-finding in a number of ways. Their “Duel Ladder System” of advancement allows employees to choose one of two parallel ladders on which to progress their careers — both with equal pay and benefits — one side of which is responsible for advancing science and products and the other side for managing people. This incentivizes employees to direct their talent where they can do the most good and removes disincentives bright researchers may have about stepping away from science (and it avoids bright researchers from turning into bad managers). 3M’s prestigious “ Carlton Society ” honors, often called the “3M Nobel Prize,” also recognizes those who have radically reshaped an industry. Importantly, nominations come from co-workers, and not from company management as would be typical in most firms. Problem finding is so rare in our study, that we suggest all companies create incentives that encourage employees to take part in problem finding.

Another example, from our research, involved a large engineering company serving the airline industry that was having difficulty finding new products and markets to grow. During training sessions to familiarize employees with the innovation process, we determined that the majority of employees were implementers — and not a single person was a generator. This was reflected in the corporate motto “We’re on It,” which rewarded taking action to fix short term problems. To rectify the situation, the company instituted a brand-new reward system that encouraged employees to create new product or market ideas. In this system, 100% of all projects that fell under these categories were funded by the head office; prior to this, business units had to use their own budgets.

4) Train.

Most business (and business school) training steers future corporate leaders towards a preference for optimization and implementation. Why? Because they tend to present future leaders with problems that we’ve already solved (the frameworks are retroactively fitted to problem-solution combinations). Dating back to 1973, management thought leader Henry Mintzberg showed that managers spend most of their time doing short-term tasks. In other words, and as our own research found, most managers are implementers. But that can change.

For instance, one way to train people is by exposing them to problem-dense environments. In a study of Japanese companies, we found that in the best performing organizations engineers and scientists hired into the R&D department began their careers in sales, not the R&D department. When we asked why, they said, “We don’t want them to think that we are going to give them problems to solve. We want them to learn the problems of the customer.” These companies also developed effective employee suggestion systems by training their employees “to be dissatisfied” with their workflow and the current way of doing this. The problems they identified in these areas were then termed “golden eggs” — opportunities for innovation and improvement — for teams to solve. The results were tracked and celebrated on a regular basis.

Another group of participants in our study benefited from an unusual type of on-the-job training. A team of managers at a fast food chain included 17 implementers, four optimizers, two conceptualizers, and two generators. Once they realized they were trying solutions without slowing down to find and define the right problem, the team changed their behavior. They relaxed their deadline for solutions and spent more time on finding facts and forming a big picture—conceptualizer and generator behaviors. While there was a number of ways to shift the team mindset from implementation to conceptualization and generation, in this case managers did this by taking shifts serving customers. Solutions were found after previously unknown problems were discovered and defined, resulting in implementation in less time than provided by the original deadline. In effect, the team created conditions — serving customers — that enabled them to think more like hands-on generators and insightful conceptualizers.

Finding Innovation

Many organizations seek innovation, but very few know how to find it. We have proposed a blueprint that leaders can follow by implementing structures, rewards, and training, as well as modeling the power of the different styles of the innovation process.

While each innovation style is critical, it is important for leaders to recognize, protect, encourage, and reward employees who are generators. But perhaps the most important factor in finding innovation is a change in mindset. Rather than viewing problems as negative obstacles, leaders can help employees see problems as opportunities for innovation — and see themselves as possessing four key behaviors that can lead to success.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Scale-Up strategies for a downturn period

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 06 Dec 2022 13:03:47 GMT

Recessions are a natural part of the business cycles and companies of all sizes must weather them or wither. Startups face a unique challenge because until they become profitable, they rely on outside capital to fund their growth and evolution to maturity. To make it through and emerge even stronger, conserve cash, and pay close attention to your customers, investors, employees, and culture.

Startups face unique challenges during economic downturns. They typically aren’t yet profitable and so are reliant on outside funding—and therefore are especially exposed when macroeconomic conditions change. To make it through a recession, startup CEOs should hit the road and talk to customers. They should also focus on preserving their company culture and retaining top employees. And they need to do whatever they can to extend their runways—including taking on a line of credit.

With stocks down 20% from their highs, we are officially in a bear market. Many economists predict we will enter a recession in the next few quarters if we’re not in one already. What strategies and tactics should startup CEOs use to prepare for and survive a recession?

I’ve spent the last two decades in the software industry, including two stints as CEO as well as serving on the boards of 10 private companies and as an advisor to many others. I’ve led or advised companies through the dotcom bubble bursting, the 2008 financial crisis, and the Covid recession. While every downturn is different, in my experience there are some essential steps that startups should take when the economic environment deteriorates.

Take steps to extend your runway. Now.

When a recession hits, it gets a lot harder to raise capital. You need to extend your runway or your “cash out date,” so plan to survive on the capital you have. Only spend money to make your product or service better or to drive new sales. No more “nice to have” expenses: Scale back on new initiatives, prioritizing only those that have a near-term chance of success.

In recessions “cash is king,” so you need to make sure you have enough to get through to the eventual expansion. Take on a line of credit to augment your equity capital. Interest rates are still reasonable and cheaper than new equity funding, even with rates rising.

Proactively embrace your best customers.

A recession is a perfect opportunity for you as CEO to strengthen your relationships with your biggest and most important customers. Remember they are feeling the threat of recession as well. Customers always want to meet the CEO of the company they have purchased from so this is an opportunity for you to hit the road, visit customers, and spend time with your salespeople. If you cannot have an in-person meeting, meet on Zoom. If you are uncomfortable selling, get over it. I recently spoke to a founder/CEO with a technical background who told me he “learned to appreciate sales” even though he was uncomfortable selling at first. If you’ve historically thought your time was best spent on product, it’s time to reconsider: In a downturn, your best use of time is talking to customers and making sales.

Remember that it is easier and cheaper to sell more to existing customers than to land new customers. This is especially true in a recession as everyone is taking a second look at all expenses. If you are in a B2B business, visiting customers also gives you real insight into how happy your customers are and whether you are at risk of customer churn. If you run a B2C business, invest in rewards programs and other initiatives to make sure your best customers feel appreciated. Churn risk increases during recessions as companies prioritize their spending and pull back on new initiatives. High churn rates have a direct impact on company valuations. As a CEO you are in the unique position to lead by example and your employees will recognize your effort.

Stay close to your venture investors.

2020 and 2021 were frothy years for venture capital and many venture firms bid up start up valuations to unsustainable levels. Those same investors must now decide which of their portfolio companies to prioritize and support as the economy slows. Investors will need to reserve capital for subsequent fund-raising rounds for portfolio companies to see them through to success.

In 2022 down rounds are becoming more common. As a CEO, admitting that your company has a lower valuation can be very difficult. It’s important for you to communicate often with your venture investors to make sure they see your long-term potential.

Embrace your best employees.

Recessions force employees to re-think their career choices. If employees start to doubt the viability of the company, they will take the calls from larger firms in the market — regardless of their equity upside — that can pay more in current income, bonuses, and benefits.

Get ahead of this. Spend time with your best employees making sure you understand their mindset. Employees always assume their equity stake is based on the last round of funding, so down rounds create employee angst. Losing top talent will have a very negative impact on your company. Managing and maintaining your momentum is critical both in terms of retaining your top talent as well as recruiting new talent.

Several times in my career I got ahead of this issue by offering additional stock option grants to top employees to make sure they did not even take the recruitment calls. It works. It’s far easier to get ahead of retaining top talent than it is to try to counter-offer once your employees are entertaining other options.

Emphasize and rally around your unique culture.

In my experience as a CEO, culture was by far the most important determinant of employee retention. Employees know their market value, and most stay with you if they are compensated and happy and feel they are making a difference. Focus on culture and communicate your company’s uniqueness and value proposition.

At Black Duck Software, an enterprise security startup, they created an equity and learning culture. Every employee was a shareholder and viewed the company as their own. We created learning and education opportunities and employees felt they continued to learn and grow by being part of the company.

Unique and identifiable culture is critical to motivate your entire team ready to fight through adversity. It may seem counterintuitive to both reduce expenses and focus on culture. It’s possible because funding unique cultural events is not expensive. It really is the thought behind the gatherings that count and that have an impact on employee morale. At Black Duck they held a Star Wars lego building competition for our software developers. The event was widely popular as the developers were able to publicly display their creativity and have fun, and it did not cost much to pull off.

Every company’s culture is different, but now is the time to double down on it. A good culture will help retain talent and ensure that you’re able to make it through tough times.

Recessions are a natural part of the business cycles and companies of all sizes must weather them or wither. Startups face a unique challenge because until they become profitable, they rely on outside capital to fund their growth and evolution to maturity. To make it through and emerge even stronger, conserve cash, and pay close attention to your customers, investors, employees, and culture.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to position your organization on the strategic factors

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 06 Dec 2022 11:39:20 GMT

How can you create the conditions for a competitive advantage? Start from the insight generation process.

Gathering insight is extremely important to your strategic success. Why? Because insight creates competitive advantage, which produces results, financial and otherwise, above those of your competitors. And don’t confine the notion of insight just to your customer or client needs. As I’ve demonstrated here, bring insight to the relationships you have with all your key stakeholders.

***

Strategies only work if you can figure out how to position your organization on the strategic factors most relevant to your organization’s key stakeholders . And doing that requires what we call insight, a recognition that no-one else has had about what your stakeholders really want. This article identifies four techniques to develop the insight you need to crack open your competitive advantage: introspection, looking at other perspectives, observing actual behavior, and looking at what happens in other domains.

Achieving advantage over competitors is what strategy is about. All organizations have competitors, no matter whether they’re business, government, or not-for-profit entities. Some compete for customers, some for funding. And they all compete for employees.

But strategies only work if you can figure out how to position your organization on the strategic factors most relevant to your organization’s key stakeholders . And doing that requires what we call insight, a recognition that no-one else has had about what your stakeholders really want. It’s that aha moment when someone explains something to you and for the first time you “get it.” Or it’s when you put “two and two together.” Or when suddenly in your mind “the lights go on.” We have many metaphors for an incoming insight.

But how do you create the conditions for getting an insight? Here are four techniques to develop the insight you need to crack open your competitive advantage.

Introspection

If you visit the Massachusetts Institute of Technology (MIT) you’ll see Charles Darwin’s name inscribed with other great scientists in the stone frieze in the forecourt of the Great Dome. He is regarded by many as the greatest of all scientists because his insights on evolution and adaptation changed how we think about life on Earth. Darwin was a great walker . And on these walks, usually around a rectangular track called the “sandwalk” near his home, he’d toss ideas around in his head speculating on connections between concepts and phenomena.

For Tom, the CEO of a diversified public company, insight comes not from walking, but from gardening. “When I’m at work my day is chock-a-block with meetings and issues, unscheduled events that I must deal with. So, there’s no time to think about strategy. I think about strategy when I’m gardening, which I enjoy in my spare time. It’s my time to decompress. It’s when my mind is free and in a relaxed mode.”

Charles Darwin illustrates that gathering insights via introspection takes time. Tom demonstrates that your mind needs to be in a relaxed and creative state. Under these conditions you can propose hypotheticals for yourself playing “what if?” I dare say Jeff Bezos would have done the same prior to the launch of Amazon in 1994, “what if we didn’t need bricks-and-mortar retail stores at all?”

See Things as Others See Them

Introspection is a wonderful starting point. But it’s limited by your view of events. Your organization’s stakeholders look at your performance through completely different sets of lenses. Tapping into that is a golden opportunity to capture insight too. This is best done through conversations around, for example, customer needs, product use, or servicing requirements.

Gordon is the CEO of a fruit cooperative that takes oranges from its grower-members, grades them, packs them and sells them to retailers. He explained to me the moment when the “penny dropped” on a fundamental aspect of the coop’s relationship with its growers. “Until we went out and interviewed several of our members, we didn’t realize just how critical prompt payment for supplied fruit was. Some growers run on a very tight budget.”

This insight had passed the office staff by as they weren’t farmers and were used to paying creditors on extended terms. Improving the cashflow of its growers not only boosted the coop’s relationship with its current members, but it also provided the coop with an advantage in attracting new members.

Observe Stakeholder Behavior

While having a series of conversations with customers and other stakeholders is a highly effective way to get “I-didn’t-know-that” insight, it’s not without its shortcomings.

Customers can be poor at recalling past behavior. In surveys, for example, some respondents remember buying a certain brand when in fact they didn’t. Their memory is unreliable because the most popular brand is often uppermost in their minds thanks to advertising.

Employees can provide false responses too because of what’s called “socially desirable responding.” This happens when an interviewee gives his or her opinion according to socially approved behavior. When employees are asked to rank the importance of pay from a list of potential motivators it typically comes in around fifth. Yet when it comes to actual employee behavior research has shown that it’s typically much higher among motivators. Why the discrepancy? One explanation is that, when interviewed, employees don’t want to be seen as mercenary.

This problem emerges most strongly when an individual is asked to rank a set of strategic factors — not so much in nominating the strategic factors as a group. Because of this, and in certain situations, consider gathering strategy insight from actual behavior.

Look at Other Industries

Organizations in the same industry tend to play off the strategies of others in the same industry. This isn’t restricted to the business sector. It occurs in the government and not-for profit sectors as well. An industry mindset emerges, and a consistency of process and strategy outcome become commonplace. Businesses in this situation become bereft of genuine insight.

To break this bind, companies have sought to gather strategy insight by looking at other industries. Examples I’ve documented include an international cosmetics business launching a men’s grooming range. They examined how the energy drink company, Red Bull , attracted young male customers. Another was a social enterprise organization, which delivered coffee, tea and snacks to offices and factories. It turned to Toyota for customer-service inspiration to improve accuracy and speed in filling orders.

Another was a health insurance company which reviewed how large banking organizations employed automation to improve customer experience via an app. As Felix, the executive in charge of the project explained it, “we had to escape the industry way of developing strategy if we were going to achieve genuine breakthrough.”

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Business Analytics is changing the Financial Industry

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 05 Dec 2022 17:46:51 GMT

Analytics is influencing the field of finance, bringing new efficiencies while creating new challenges.

The next big financial fraud may eclipse the recent collapse of cryptocurrency exchange FTX, which at last count had liabilities estimated at $8 billion . “You’re going to have larger frauds, and there might be more frauds,” Wharton accounting professor Daniel Taylor said during a panel discussion titled “The Analytics of Finance” held earlier this month. Stricter regulatory checks and audits may have averted the FTX scandal, he added, noting that it was the outcome of weak internal controls.

In the future, if regulators fail to embrace analytics as aggressively as sophisticated fraudsters do, “the frauds will get bigger, persist for longer, and be more devastating,” Taylor said. According to him, the largest frauds occur due to complexity, and the financial markets are getting increasingly complex.

The discussion was part of Wharton’s Beyond Business series, which explores some of the most complex and pressing issues affecting organizations and individuals around the world. An expansion of Wharton’s Tarnopol Dean’s Lecture Series, Beyond Business is streamed live on Wharton’s LinkedIn page .

This year’s three-part Beyond Business series “shines a light on how analytics, artificial intelligence, and machine learning are providing viable pathways for solutions in every domain,” said Wharton Dean Erika James . She led the discussion with Wharton finance professor Michael Roberts and Taylor, who is also director of the Wharton Forensic Analytics Lab .

Investing in technologies such as analytics is one of the surest ways to combat financial crimes, Roberts said. He noted that compared to the Securities and Exchange Commission’s 2022 budget of $2.7 billion, JP Morgan alone spends $12 billion annually on technology, and the entire financial sector may have a combined annual outlay of $100 billion. “The only hope, since [regulators] can’t compete on scale, is efficiency. And that means embracing data and analytics to try and ferret out any potential wrongdoing,” he said.

Taylor noted that as financial crimes are becoming increasingly sophisticated and difficult to detect, regulators are recognizing the power of analytics and “are starting to tool up.” Awareness is also filtering into education, with law schools including classes in data and quantitative analysis, he added.

Growth and New Efficiencies

For sure, regulators have used analytics to good effect in many settings. James pointed out that the public gets to hear of financial scandals that have reached a certain stage. “I would imagine there’s a ballast on the other side of that, with all of the [financial crimes] that [regulators] have been able to prevent,” she said.

The use of analytics in finance has also brought other gains, such as growth and new efficiencies to markets, and expanded household access to financial products, thereby leveling the playing field for them in many ways.

Analytics has “a critical and growing role” beyond its traditional domain of capital markets. “On Main Street, we see more effective capital budgeting because of data integration and exploitation via data science initiatives,” said Roberts. “Among households, we’re seeing improvements in budgeting, retirement savings, asset allocation, and credit usage, due largely to the democratization of data and technological innovation from fintech.”

Within the capital markets, analytics is finding new uses such as machine learning to develop predictive business models in valuations of businesses, Roberts said. Of late, data and predictive models are being used for due diligence in mergers and acquisitions, buyouts, and capital-raising activities, he added. He noted that practitioners are already seeing “impressive results” in terms of improved predictive accuracy and increased benefits to shareholders and other claim holders at firms.

According to Taylor, analytics could also help track corporate citizenry and adherence to ESG values. “Analytics with satellite monitoring could help distill greenwashing versus what’s called ‘true ESG’ or ‘true green’ investments,” while technologies such as infrared imaging could track and verify carbon emissions at specific units, he noted. “Those will help investors more efficiently allocate their capital to corporations who embrace their values.”

Depending on how analytics is used in finance, it could bring either good or bad outcomes. “We need to think about data and analytics as investments,” Roberts said in response to James’s question about whether it could create “a new class of haves and have-nots.” He pointed out that first movers have to be incentivized to take on the attendant investment risks. “But over time, competition will erode those returns and we’ll see a diffusion of technology and knowledge to the broader population — something we’re already seeing with inexpensive and widely available robo-advisors and electronic payment systems,” he added.

“If humans have been replaced by algorithms, you conceptually could imagine a world in which the algorithms don’t have those human biases that are detrimental to prices or to market efficiency,” said Taylor. But some algorithms focused on correlations between two stocks or between various indices are vulnerable to occurrences outside their programming, which could create “large distortions, suddenly, in markets,” he added. “[With analytics] we’ve removed some of the human biases from prices, but we may have introduced other biases. It’s an open question — which we might prefer, and which is better for society.”

Challenges and Opportunities Ahead

Looking ahead five or 10 years, Roberts identified the goalposts for analytics. The top challenge for analytics is around data ownership, where people have control over their personal data. On the other hand, analytics will bring “ cutting edge, customized financial planning to households at a negligible cost” to help them make more efficient decisions. Businesses, including those in the small and medium sectors, will also find expanding uses for analytics in areas like capital budgeting processes and resource allocation.

In an evolving field such as analytics, it’s critical to distinguish between technological failures and human failures, Roberts said. “First, better-informed participants that don’t blindly rely on technology, data, and analytics, will make a big difference. There has to be personal agency, because it’s going to help discipline behavior and ensure robustness. Second, markets themselves must install and enforce trip wires and guard rails that can handle the new technological reality.”

Finding the right approaches to avoid inequities will be an ongoing challenge. For instance, high-frequency traders such as hedge funds with sophisticated models might gain an advantage over retail investors, Taylor said. Alternatively, analytics is often used “to democratize finance and help spur retail investor participation,” he added. “You’re going to see that sort itself out based on those that can make profits from analytics by adopting it — and those that potentially can’t, not doing so.”

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Selling a startup

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 05 Dec 2022 16:23:55 GMT

Selling your company is the tip of the iceberg, and the more you know about post-acquisition life before you start negotiating, the happier you and your employees will be for the next two-to-three years. There are enormous psychological and operational changes ahead, and you can influence many of them by using this model to know when and where to negotiate.

In acquisitions, there are two types of leverage. The first is negotiating leverage, which determines who wins on deal-breaker points. The second is knowledge leverage, predicated on knowing what issues you can win on without jeopardizing the deal. There’s little you can do to change your negotiating leverage — you either have a competitive acquisition process or you don’t. However, you can change your knowledge leverage. Contrary to what the acquirer might say, most points are not deal breakers. You just need to know what to ask for — you might be surprised at how much the acquirer will agree to, but only if you ask.

The vast majority of startup exits occur via acquisition . And while the internet is full of advice for pre-exit founders, remarkably little content exists to help guide them through post-acquisition life — even though they and the employees they recruited will often spend two-to-three years toiling away with the acquirer. An acquisition is an exciting occasion, to be sure, but it is hardly the happily-ever-after ending that the “founder’s journey” story might suggest.

Throughout my career, I have experienced 10 different acquisitions from multiple perspectives: as a founder, an investor, and a Board member. I recently went on a listening tour to compare my experience with the post-acquisition stories of a wide range of acquired founders. While I’m not at liberty to name names or dive into specific deals (as a rule, founders do not tell bad stories about their new employer), I can aggregate the honest perspectives I heard and combine them with my own experiences to produce an overall guide to the acquisitions process.

The psychological shift from founder to employee can be difficult, and the years that follow can be deflating compared to startup life. You will have pixie dust on you for a while — “the founders that built X and sold it for $Y” — but you’ll soon be judged on how well you work with others and drive success for your new employer. You might also face resentment from your new peers, who have also worked hard for 10 years and don’t have an acquisition to show for it. You’ll be tempted to feel that everything the acquirer does differently is inferior — but resist this urge. You sold for a reason. Be graceful about the differences and learn from the experience. Find something that you can only learn or accomplish as part of this bigger company, then do it with purpose.

The most common theme for these conversations was simply: “I wish I had known then what I know now.” Knowing your leverage, the type of acquisition you’re in, and the important points to push on will help you maximize success and employee happiness in the long run. You owe it to yourself — and the employees who followed you — to be prepared.

How Much Can You Shape the Outcome?

Far more than you think.

There’s little you can do to change your negotiating leverage — you either have a competitive acquisition process or you don’t. However, you can change your knowledge leverage. Contrary to what the acquirer might say, most points are not deal breakers. You just need to know what to ask for — you might be surprised at how much the acquirer will agree to, but only if you ask.

KYA: Know Your Acquirer

Assessing your acquirer will help you and your employees prepare for what lies ahead.

Incumbent vs. Startup : Obviously the bigger and older the acquirer, the more cognitive and cultural dissonance you will experience. You cannot change this, but you can lead your team with emotional intelligence. The acquirer got big for a reason. On the other hand, being acquired by a startup can feel quite natural from a cultural perspective, and you’ll find similarities on everything from tech tools to HR policies.
Handling post-acquisition integrations : we supported clients in more than 20 acquisitions. Know that some acquirers are pros; some are not. Either way, make sure you know what happens “the day after.” Force the buyer to detail their plan, because it will raise numerous issues that will matter to you, your employees, and your customers.
Acquirer’s culture: You might feel that two or three years will go by quickly, but it won’t. It matters if your employees are entering a culture where they feel at home. You will get swept up in the acquisition momentum, so remember to ask yourself whether this is a company that reflects enough of your values. Talk to more than just the acquisition team and the deal sponsor — ask to speak to the CEO of a startup they’ve previously acquired.

Know Why You’re Being Acquired

There are five types of acquisitions, and understanding which model you fit with will inform your approach:

New product and new customer base: You know more than the acquirer and they could easily mess up what you have built, so you should fight for business unit independence. These acquisitions fail as often as they succeed. Examples include Goldman Sachs and GreenSky , Facebook and Oculus , Amazon and One Medical , and Mastercard and RiskRecon .
New product or service, but same customer base: Most acquisitions fall under this category. Founders should give in to faster integration, because it ultimately leads to more success for both sides. Integration does complicate earnouts — but your first priority is to avoid earnouts. Famous examples include Adobe and Figma , Google and YouTube , and Salesforce and Slack .
New customer base, but same product category: In this category, you know the customer and the buyer does not. Maintaining a higher degree of independence in the short term is important to the success of this acquisition. Be ready to share knowledge and eventual integration. Examples include PayPal and iZettle , JPMorgan and InstaMed , and Marriott and Starwood .
Same product and same customer base: The buyer wants your customer base and possibly to eliminate you as a competitor. You will be fully integrated into the acquirer by function, and quickly lose your independent identity. Examples include Plaid and Quovo , Vantiv and Worldpay , and ICE/Ellie Mae and BlackKnight .
Acqui-hires: You’ve built a team so good that another company is willing to buy the company to hire them en masse. Be realistic — this is a graceful exit for you, and a non-essential purchase for the acquirer. In this category, there are too many examples to count.

What to Ask For

During an acquisition, it’s easy to focus on transaction points like valuation, working capital adjustments, escrow, and indemnification. You need to get those right, but your experience through the next two-to-three years will depend more on how things operate post-acquisition. In rushed transactions, acquirers will tell you not to worry about these points — but you should. Here are the key non-deal points you should consider:

Employee Compensation: You should adjust employee compensation ahead of the acquisition because it will be very hard for the acquirer to change them later. Your employees earn startup salaries, which should be higher when the equity upside is removed. Be aware that the transaction may yet fall apart, so do the compensation benchmarking work and then wait to implement until you are highly certain the transaction will close.
Employee Titles: You will need to map your employees onto the acquirer’s titles and compensation bands. As a startup, you likely focused on equity and options, but the acquirer focuses on cash compensation and other benefits. Learn the differences among the titles before mapping, as big companies often base everything from bonus ranges and benefits access to participation in leadership meetings on them. Advocate hard for your employees — you have the Knowledge Leverage about them, so use it.
Retention: Acquirers want to retain key startup employees, and you have the power to decide who is in the retention bucket. However, it’s a double-edged sword because your employees must stick around to earn the extra compensation. Strive to keep that period under two years, as three will feel way too long. Rather than expand the retention pool up front, you should negotiate for a second discretionary retention bucket which you can use to retain key employees who might want to leave soon after the acquisition.
Pre-agreed Budgets and Hiring Plans: You thought raising money from investors was tough, but just wait for corporate budgeting. Most large companies use budgets and headcount as their control mechanisms, so negotiate both for your first year. You will want the freedom to execute, and you shouldn’t spend time advocating for every new hire — most likely with new stakeholders who weren’t part of the initial acquisition.
Governance: Who will you report to? Your new manager’s seniority and authority are the most important factors. You won’t escape company-wide budget processes, but it’s better to only have one person to convince. If you’re a standalone business unit, negotiate for a Board of senior leaders from the acquirer. It’s a novel structure for buyers, but it’s a smart way for you to match form with function. Finally, avoid matrix reporting at all costs, especially if you have an earnout.
Earnouts: Buyers prefer them because they align price with performance, but your job is to avoid them. This is easier said than done, but you’ll never be as free to execute post-acquisition as you were pre-acquisition, and unanticipated forces will disrupt the best-laid plans. You could crush it on revenue and miss gross margin, or hit all your targets, 12 months late. It will be your call, but if you have the chance to earn 25% more with an earnout or settle for 10–15% more upfront, I would take the smaller amount up front.

Engaging Your Board

Most acquisitions start with an unsolicited expression of interest, and CEOs have a duty to share them with the Board. Some are easy to dismiss, but others trigger the awkward dance: Do you want to sell? Don’t you want to go long? At what price would you sell?

This is where you will see your investors’ true personalities. Everyone understands that the Series B investors at the $125 million valuation will not relish a $200 million sale. However, the real task is to find the best risk-adjusted outcome for the company, considering founders, employees, and common shareholders. This is where you will be glad that you selected genuine partners as the investors in your boardroom, and independent Board members can provide an especially valuable voice.

If you decide to engage with the acquirer, then CEOs with M&A experience can take it from there. If you’re not that CEO, get help. You don’t want the entire Board involved, so get them to appoint one or two members to an M&A Committee and put them on speed dial. You will avoid many small mistakes — and have at least a couple of Board members already convinced when you return with a Letter of Intent.

Selling your company is the tip of the iceberg, and the more you know about post-acquisition life before you start negotiating, the happier you and your employees will be for the next two-to-three years. There are enormous psychological and operational changes ahead, and you can influence many of them by using this model to know when and where to negotiate.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to boost the company's valuation leveraging on the digital drivers

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 02 Nov 2022 11:46:29 GMT

How to create and capture value with digital.

In the digital era, how firms create and capture value has changed profoundly. But with digital transformation, many firms are leaving substantial value on the table, getting caught up in “doing” digital transformation rather than staying focused on how they will create and capture value with digital. To do this, first companies need to understand the three different types of digital value:

Value from customers (cross-selling, increased loyalty, great customer experience);
Value from operations (increased efficiency, modularity and reuse of components, automating processes);
Value from ecosystems (leveraging partners for both access to more customers and range of products and services).

With these types of value in mind, firms can then take action to create digital value by:

Identifying domain opportunities;
Building mutually-reinforcing capabilities;
Tracking digital value with a dashboard;
Recruiting digital partners;
Investing in digital savviness of everyone at the firm.

Companies that do this will become truly “future ready.”

A global financial services firm we worked with really seemed to get the digital message. They hired a chief digital officer who led many locally successful projects to improve the customer experience. These included making it easier to move from in-person to online for certain tasks, plus targeted offers based on customer data. They felt confident they were creating great customer value. But there was a problem. Those local innovations ended up adding more complexity to the existing fragmented business processes, systems, and data. Although the customer experience often improved — and in some cases, revenue increased — the rise in the cost-to-serve eclipsed the gains and added other risks like cybersecurity and system crashes.

In the digital era, how firms create and capture value has changed profoundly. But most aren’t keeping up. A ccording to recent research , the average firm today is leaving an eye-opening 50% of potential digital value or more on the table, compared to leading firms.

In our experience working with global enterprises in every industry, the main reason for this seems clear: firms often get caught up in thinking about “doing” a digital transformation initiative rather than thinking concretely about how they will create and then capture value with digital. The focus should start and end with value. This means changing the way you think, operate, develop talent, keep score, organize, partner, and innovate to compete in the digital economy. We call companies that are doing this “future ready,” and the most successful among them are generating 70% or more of the potential value from their digital initiatives — significantly more than the average firm.

Three Types of Digital Value

In helping leaders and their firms shift to a future ready mindset, a key step is recognizing three types of digital value — these types represent where and how value can be created, as well as the areas where there is a risk of leaving value on the table. We’ll use the global building materials firm CEMEX to illustrate.

1) Value from customers.

This encompasses increased revenue from cross-selling and new offerings, as well as more customer stickiness and loyalty. Helping customers meet their needs, providing a great customer experience, and acting consistently and with purpose helps create value. CEMEX started their transformation by focusing on customer value. Recognizing that construction site managers are key customers with a tough job, in 2017 the firm created the CEMEX Go mobile app, a single place for those managers to get everything they need from CEMEX such as advice, pricing, ordering, and an Uber-like tracking experience for cement delivery. CEMEX Go was the breakthrough initiative for the firm, resulting in a strong increase in revenue for the channel as well as a substantially higher net promoter score.

2) Value from operations.

The foundation of digital business, value from operations includes reduced cost and increased efficiency and speed. Firms can create this type of value by developing modular components, creating digital components that can be reused, automating processes, and becoming more open and agile. CEMEX focused broadly on operational efficiency and reducing the app’s cost-to-serve while continuing to improve customer experience.

3) Value from ecosystems.

This includes revenue from a company’s ecosystem participants plus new value from customers and operations through partnering. This type is the most overlooked, or deferred as risky, but as firms move to more digitally-enabled and partner-based models, value from ecosystems becomes more important and influential on the bottom line. Almost any firm can generate substantial value from ecosystems in which they leverage partners for both reach (to access more customers) and range (to add more products and services). CEMEX does this with its building materials distribution network, Construrama, the largest retail building material store chain in Mexico, and in other Latin American countries where CEMEX operates. In 2018 CEMEX launched its Construrama Online Store to continue efforts to transform the construction industry using an ecosystem approach.

Taking Action to Create Digital Value

Once you’ve got a clearer view on the different types of value, there are several key actions you can take to create digital value:

1) Identify domain opportunities.

This means thinking beyond your industry. Digital is about imagining what’s next, and what you didn’t think is possible, to develop entirely new value propositions for your customers. For example, Shopify enables the domain of online business, providing a platform with partners that supports the entire customer journey, cutting across several industries. Services include building a brand, creating an online presence, setting up a store, selling, logistics and shipping, processing payments, and managing day-to-day. Any one of these activities could be its own business — Shopify creates value by offering an integrated solution to meet customers’ entire domain needs and is now number two behind Amazon with 10.3% of U.S. retail e-commerce sales in 2021 .

To identify domain opportunities, start by looking at your typical customer’s end-to-end journey, including beyond your company’s scope, and consider how you could improve it — or even own it as a one-stop destination by partnering to add complementary services.

2) Build mutually-reinforcing future-ready capabilities.

Lots of companies fail by setting out to change their culture, often with a program dedicated to describing (or, really, prescribing) the to-be culture. This is putting the cart before the horse. Culture is built through routines, shared values, and informal norms — the work habits of the enterprise — not by dictates and training. This kind of habits change is better tackled by building the future-ready capabilities that will help your firm create value from your digital initiatives, and by ensuring they reinforce each other.

For example, CEMEX integrated CEMEX Go with new systems and processes for order fulfilment and CRM, including a digital confirmation capability — an automatic review of inventory, transport, and other components of the customer journey when an order is confirmed online. By 2022, CEMEX had automated order fulfilment for the cement product type and was then able to build on that capability and its constituent components to automate the more complex coordination process for delivering the ready-mix concrete product type. The complementary systems and their associated habits and processes provided mutually-reinforcing learning that accumulated over time.

3) Track digital value with a dashboard.

Dashboards can be very helpful for measuring milestones of capability and digital value creation along the way, as well as for inspiring the company to stay on track, as it can often take significant time for changes to show up in the bottom line. Effective dashboards enable everyone to see current status and progress, and to make better course corrections, helping to move from a command-and-control model to a coach-and-communication orientation.

Schneider Electric’s Digital Flywheel provides a good example of the benefits of using a dashboard. They built the flywheel to help drive their efforts to expand digital offerings to include energy efficiency management, going beyond selling energy products. The dashboard does this by illustrating the four components of their IoT -enabled business model and capturing and tracking financial performance for each of the four individually. But just as important, it shows how the four components work together to produce higher value and sales for the company — and increased value for clients, often measured as energy efficiency improvement. The dashboard helped them understand how to grow this distinctive business model, which now accounts for 50% of their annual revenue of 30 billion Euros.

4) Recruit digital partners.

Partnering is not the goal, but rather a way fofuture-readydy firms to achieve their goal of creating value from ecosystems. Digital partners can help increase a company’s reach and range through digital connections. Look at companies like Zillow that are finding new ways to meet customers’ needs in the home buying journey. They started with helping customers locate a home, but the journey soon spanned six or more industries such as insurance and finance. Bringing in partners like real estate agents, mortgage brokers, and lawyers, and providing many of those services digitally as an integrated offering, makes that home buying journey simpler and a better experience. And it creates opportunities for Zillow to capture more value from the transaction spend.

5) Invest in digital savviness.

Digitally savvy firms don’t have an “us vs. them” or a finger-pointing mentality between IT/digital and rest of the organization. Everyone aspires to be digitally savvy from the board to new hires. There is joint accountability for the benefits (innovation) and the risks (outages and cyber attacks). DBS in Singapore decided early on in its transformation to foster digital savviness throughout the entire enterprise, with initiatives such as embedding innovation advocates in each business unit, implementing agile practices, training to reskill and upskill employees, and promoting hands-on experience with technology.

The digital wave continues and it is easy to get swept away in the flood of transformation initiatives. But in order to become truly future ready — and to avoid leaving substantial money on the table — stay focused on specific ways to create and capture digital value, and track that value for all to see.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Customer data management is a competitive advantage

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 02 Nov 2022 11:16:34 GMT

It’s one that any company in any industry needs to take on to stay relevant. We can think of no other capability that is so universally needed.

To stay ahead of competitors, companies need to implement a system of privileged insights: unique and relevant information about customers that competitors don’t have access to.

There are different ways companies can use to gain their own privileged insights — including creating a more robust and engaging customer service experience, integrating customers into product and service development, and observing and interacting with customers while they use products.

There are some powerful best practices:

The first is to build trust. Customers that see their lives or businesses intrinsically linked and improved because of what a company offers are much more likely to engage and more willing to exchange unique information and insight into their core needs and challenges.
The second: Privileged insights should be embedded into existing customer touch points (e.g., customer service, warranty support, product delivery, etc.).
The third: Every business unit should be empowered to make decisions based on these unique insights.

Companies spend billions of dollars every year to gain information about their customers, buying data from market research firms, running study after study, and using big data and sophisticated analytical models to make sense of it all. However, most of this data is likely available to your competitors and not living up to your aspiration of gaining meaningful behavioral understanding of your customers.

To truly differentiate and stay ahead on an ongoing basis, you need to implement a system of privileged insights — unique and relevant information you gain about your customers that only your company is privy to.

Unlike market research, privileged insights provide intel on your customers’ real needs, desires, and experiences. These insights can be gained in a variety of ways. Generally, it requires engaging with customers in ways that directly build trust and value. This might include offering services and solutions that go beyond products, creating a more robust and engaging customer service experience, integrating customers into product and service development, and observing and interacting with customers while they use your products.

We’ve researched a number of companies that have undertaken significant transformation to position themselves for success in the digital age, including Adobe, Cleveland Clinic, Citigroup, Eli Lilly, Hitachi, Honeywell, Inditex, Komatsu, Microsoft, Philips, STC Pay, and Titan. It isn’t that these companies necessarily use technology better or were first to build a consumer data lake — it’s that they’re incredibly focused on wiring a deep understanding of customers into the heart of their business models, their operations, and how they make day-to-day decisions. They passionately focus on increasing value for their customers, all while absorbing and leveraging a wealth of information that their competitors don’t have. By doing so, they’re able to further differentiate themselves and remain relevant.

How can you go about building such a privileged insights system that fuels your company’s success? Here are some lessons learned from the companies we studied and more that we have worked with.

Establish a foundation of trust and value

Be clear about how you earn customers’ trust to engage with you, and the benefit they gain from doing so. This goes to the heart of how customers trust you to consistently deliver outcomes they value. Customers that see their lives or businesses intrinsically linked and improved because of what you offer are much more likely to engage with you and more willing to exchange unique information and insight into their core needs and challenges.

Building a foundation of trust also includes having impeccable clarity on your values, principles, and governance around how you will treat customers’ data. Will you use the data to only advance your own commercial position, or to improve the customer experience and benefits? Will you take responsibility to not misuse the data? Will you have strict enforcement if an issue happens? Leaders must ensure that people across the organization understand that it’s not about extracting data from people and making people the product — it’s about making customers an integral partner in the value chain.

Ashley Still, senior vice president and general manager for Digital Media at Adobe, is absolutely clear about the company’s guiding principle for how it uses customer data: “We are committed to data privacy and sensitive to how we use data. Responsible use of customer data can create greater experiences, but the second we start using it to gain tactical advantage, we’ve missed the mark.”

Together with the trust and value that is embedded in their users’ experience and the value proposition Adobe offers, these practices lay the foundation on which the privileged insights system is built.

Integrate how you collect privileged insights into your day-to-day actions

Make the collection of insights a byproduct of your engagement and relationship with customers, not a separate process. This will allow you to gain customer insights while you create value for them, be it through your physical or digital interactions.

This should start with all your existing customer touch points (e.g., customer service, warranty support, product delivery, etc.) and extend to many new opportunities to engage and improve your value proposition. The ultimate question you will need to answer is whether customers feel positively impacted by the information you are collecting.

Consider fast fashion company Inditex, owner of the Zara brand. Its retail employees are trained to serve as its frontline eyes and ears, tracking data, observing customers, and gathering informal impressions — all while helping customers find the styles that suit them best. The stores compile information about the choices customers make, their inquiries about missing items, and their suggestions. Are shoppers looking for skirts or trousers? Bold or subtle colors? These impressions are sent directly to a group of designers and operational experts at headquarters, together with detailed daily data on exactly what is selling and where.

The combination with deep insights from what people are searching for and buying online puts them at a clear advantage over online-only fashion companies. All these insights are rolled up, aggregated, scaled, and analyzed almost in real time and turned into designs for new garments or into improved production, logistics, and marketing practices.

The key is in the flexibility to adapt to customer preferences and the precision to create and produce what customers are asking for, at the moment they are asking for it. At the end of the year, Inditex’s more than 700 designers will have come up with 60,000 different creations, and the stores worldwide will have received new waves of collections twice per week.

Wire your privileged insights into how you work

Put your privileged insights to work by connecting them into your operations — changing structures, processes, incentives, metrics, information flows, etc., to enable every part of the business to make decisions that are based on your unique insights.

The most obvious (though not always well-executed) example of this involves wiring privileged insights into your company’s innovation process, by using them as the basis for ideation and looking for many ways in which customers can be integrated in the actual development process (for example, in beta pilots). But privileged insights need to be linked to many areas beyond innovation, including the determination of investments in tools and technologies that facilitate ongoing experiences, the interaction of your selling and customer teams, and your forecasting and strategic planning. Be prepared for those insights to materially change the fundamentals of your business, not just lead to incremental changes or a new feature in some of your products. And rethink how you measure the impact of your privileged insights capability; the metrics that most companies use today don’t go nearly far enough, and more innovative measures like, for example, return on experience (RoX), should be considered by companies pursuing this capability.

Consider Salesforce. From its inception, Salesforce has been acutely aware of the need to build its business on trust — not surprising given the sensitivity of the data customers share on the platform. This values-based relationship with users allows the company to gain deep insights into what works well, what needs improvement, and additional services customers would like to get.

These insights directly feed into and fuel Salesforce’s product development strategy and allow the company to extend its value proposition. With a customer success orientation at the heart of the relationship Salesforce builds with its customers, the company has established a unique platform that allows it to leverage insights from customer usage data to inform strategies that enhance long-term customer value and thereby drive customer retention and growth. These insights enable Salesforce to more effectively co-develop solutions with partners and customers, tailor them to various industries, and offer them as part of their platform as new industry clouds. This unique system of product development and innovation fueled by proprietary customer insights is one of the key factors that has made Salesforce the fastest-growing software company of all time.

The power of a privileged insights system stems from its self-reinforcing nature: The more customers trust your company and derive value from your products and services, the more likely they are to open up and engage with you. The more they do so, the more insights you’ll be able to gain about what customers want and need; and the more insights you have and the better you are at wiring those insights into everything you do, the more you can improve your customer experience, products, and services and build additional trust and connection with customers. It’s a true flywheel.

For the flywheel to work and fuel your company’s success, you need to work on all three of these areas, starting with a brutally honest assessment of the real gaps you may have across each area and realizing that creating a system of privileged insights will not come without meaningful transformation.

It’s easy to see how neglecting one area is going to keep the whole system from working. Indeed, if customers don’t trust you, they’re not going to open up. If providing insights is a one-way street, it may only appeal to the most loyal and passionate of your customers. And if you’re letting your customers down and don’t act on the feedback, you will most probably not get a second chance to get it right.

This is a big task and requires a fundamentally different way of thinking about data, research, and the entire cycle of touch points with customers. But it’s one that any company in any industry needs to take on to stay relevant. We can think of no other capability that is so universally needed.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The main drivers of the company’s valuation

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 02 Nov 2022 11:16:32 GMT

A data-driven understanding of what drives company value can help management teams in other, less obvious ways as well.

Investors consult detailed, quantitative models before making decisions. Shouldn’t corporate managers have a similar understanding of how the market values their company, so they can make informed decisions to maximize shareholder value?

An EY-Parthenon analysis of quarterly data from thousands of companies in hundreds of industries over a period of 20 years has identified six critical factors that account for most of the variability in market valuations. Management teams can use these to create a model that allows them to compare industries, companies across industries, and companies within the same industry. The model can also help leaders understand changes in how the market values any of these companies over time.

Investors have detailed, quantitative valuation models they consult before making investment decisions. So why don’t corporate managers have a similarly quantitative, detailed understanding of how the market values their company, so they can make equally informed decisions to maximize shareholder value?

When managing their valuations, companies focus largely on improving price-earnings (PE) ratios by driving revenue and margin based on historical heuristics, or summaries of past experiences that aren’t systematically analyzed and thus imprecise. This approach not only handicaps management teams, it can inhibit a company’s ability to recognize opportunities and neutralize threats. These threats can arise from how their company is valued on its own, differences in how their company is valued compared to competitors across and within industries, and how their valuation changes over time.

Lacking such a model leaves executives to operate on decades-old rules of thumb, missing opportunities they could take advantage of and suffering threats they might avoid by failing to “tune” their business models to the market’s changing pitch. Boards of directors might also miss valuable quantitative signals of whether valuation changes are being driven by company or market conditions, and how attuned management is to those shifts.

Six Key Factors that Drive Valuations

An EY-Parthenon analysis of quarterly data from thousands of companies in hundreds of industries over a period of 20 years has identified six critical factors that account for most of the variability in market valuations. Management teams can use these to create a near-universal model that allows them to compare industries, companies across industries, and companies within the same industry. The model can also help leaders understand changes in how the market values any of these companies over time.

These six factors are:

Weighted forecasts of growth in company revenue
Weighted forecasts of growth in company margin
Patterns of cash returned to shareholders
Changes in the company’s debt-to-equity ratio
The economic conditions in the company’s industry
Market volatility in the geographic areas in which the industry’s major companies compete

Below is an example comparing six disparate industries. We show the contribution of each factor to the valuation change.

Not surprisingly, our analysis confirmed that revenue and margin are generally the two most important variables of the four that companies can control, though not in a uniform and balanced manner. It also confirmed some general understanding managers have gleaned about particular industries. As the above graphic shows, the value of interactive media and services companies is driven primarily by revenue, while that of semiconductor companies is driven mainly by margin. The hotel business relies heavily on margin and cash returned to shareholders.

While the relative importance of revenue growth to the household-name tech giants is well understood, the knowledge that the elevator industry is similarly growth-driven is much less well known. Indeed, we have clients in the elevator business who were unaware that their focus on short-term margins was harming both the company’s valuation and its ability to deliver long-term returns. The semiconductor and tire industries are similar: Margin is overwhelmingly important to companies in both industries, and we’ve had enough conversations to know that this is not how many tire company CEOs generally think. And while it’s widely understood that margin and cash returned to shareholders matter a lot in the hotel industry, it may be less obvious to see cash returned to shareholders responsible for so much of the valuation of packaged meats companies.

In at least half of these cases, then, the main drivers of valuation are not a priori obvious. By knowing what is driving your company’s valuation, you know what to focus on.

Valuation Drivers Change over Time

The analysis in the exhibit above is based on an average across a complete business cycle. It offers general truths about the companies analyzed.

Few company managers realize how much these “truths” change over time. The exhibit below shows what drove the valuation of packaged food and packaged meat companies from 2011 to 2020.

For example, packaged food became much more margin oriented over this period, before fading a little in the face of external factors (probably Covid-19). At the same time, margin in packaged meat went from being an important factor to one investors hardly cared about at all, except, presumably, with regard to the companies’ ability to pay dividends and repay debt.

The implications can be startling. Imagine you are a new CEO at a packaged meats company in early 2013, without an analysis of this kind. You find the market is rewarding you for strong margin growth, so that’s where you focus. As the importance of margin growth shrinks and that of revenue growth rises, returns suffer. You listen closely to analysts and financial journalists; you examine PE ratio forecasts. Toward the end of 2017, you focus on revenue. After a few quarters, things begin to improve. But then the importance of revenue shrinks as cash returned to shareholders begins to dominate, and total returns suffer once more.

We see, again and again, management operating based on what was driving their valuation two or three years earlier, or in some cases much longer. It’s the business strategy equivalent of a sports team trying to win a championship with last year’s playbook — and worse, a playbook designed for last year’s team. It’s impossible to be agile without good data and analysis.

Valuation Clarity Can Identify Opportunities and Threats

Of course, you can manage your as-is business to enhance shareholder value if you know what the market is looking for. But a data-driven understanding of what drives company value can help management teams in other, less obvious ways as well.

When companies recruit and hire a new CEO from another industry, the new CEO expects to have to learn a new industry, but likely carries over some preexisting beliefs, including about what drives stock value. Wouldn’t that hiring board want to know in advance that they were recruiting a CEO who will design a strategy based not only on past experience but also data-driven learnings about the new industry?

We often discover the same range of variability within an industry as people expect to find between industries. So, a CEO might come to one packaged goods company from another and run the second the same way they ran the first — unaware that, for whatever reason, the market cares much more about the margins of the second company than it did with regard to the first company, where revenue mattered much more.

The same insight holds when it comes to mergers and acquisitions. Say your company is valued much more on revenue than on margin — but you own a high-margin, low-growth business line that isn’t helping your valuation very much. You can sell it to a company in your industry that is predominantly valued based on margin for a great deal more than it is worth to you. If they have a low-margin, fast-growing business that isn’t worth a great deal to them, you can buy it at a large discount — or find a mutually beneficial swap.

You can also avoid bidding wars by knowing when to walk away. We advised one company that the premium the selling company wanted simply wasn’t worth it to them. But we also made it easier for them to walk away by noting that the other company was worth even less to their other competitors. So being outbid meant the winner would be overpaying even more than they would. It’s a good rule of thumb to never interrupt your competitors when they are making a mistake.

Managers aren’t always free to act based only on the insights offered by an analysis of this kind, and any change of direction requires a plan, which takes time. As the second exhibit above shows, however, the importance of market drivers tends to ebb and flow gradually, so a company will generally have time to craft a plan and execute it before the valuation environment has completely changed. And if the environment has changed, an updated value-driver diagnostic will be there to tell you so.

So, whether it’s running “business as usual” for maximum market impact, taking advantage of valuation disparities that will permit acquisitions and divestments on favorable terms, or shoring up against threats inherent in the ways competitors are valued, the data revolution makes it possible for companies to understand the changing relative importance of the variables that matter to their and their competitors’ valuations — and act accordingly.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Venture funding trends

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 02 Nov 2022 10:32:06 GMT

Startups should reconsider their fundraising strategy and some should consider aiming for profitability sooner than they’d planned.

The startup world is currently debating when venture capital investing will return to its pre-2022 heights. The bullish case is that VCs have lots of “dry powder” — capital that’s already been committed. That money will get invested one way or another, the thinking goes. But that’s not the whole story. In fact, VCs may slow their pace of investing and may focus on helping the companies they’ve already backed, making fundraising harder for newer startups. For that reason, startups should reconsider their fundraising strategy and some should consider aiming for profitability sooner than they’d planned.

Venture funding for startups suffered a 50% year-over-year drop in the 3rd quarter of 2022. While most participants in the startup ecosystem were expecting a downturn, this dramatic pullback in deployment is sure to send chills down the spine of many founders and funders.

Some, like venture capitalist Jon Sakoda, believe the funding downturn is transitory and argue that the $290 billion of committed capital to venture capital firms is “dry powder” that will re-energize the startup market in 2023. As a VC myself, I sincerely hope he and others in the optimistic camp are correct. However, investors and entrepreneurs need to prepare for what could be a massive level of attrition that could occur if the funds are deployed more slowly.

Here are a few reasons why I’m encouraging founders to focus on their existing runway and not to pin their hope on dry powder.

VC funds will focus on their key holdings.

$290 billion is, objectively, a lot of money. But where it’s deployed matters quite a bit. My hunch is that VCs will reserve most of it for the most promising companies already in their portfolio.

Tough times put investors into triage mode. Mature startups with proven business models and the potential to reach the public markets within a few years will be the safest place to park any new venture capital that comes into the ecosystem. The pressure to protect portfolio startups seen as potential fund returners will be profound.

Beyond that fortunate group, the funding situation will be less secure. The market for pre-seed and seed rounds should remain relatively active, since those companies are many years away from even thinking about going public. But even in the seed market the bar could get higher: I wouldn’t be surprised to see valuations drop and for VCs to have rising expectations about the level of traction they expect to see before funding.

Series A and B startups may be the hardest hit. Many teams that have made solid progress but still present unanswered questions about their product-market fit, go-to-market strategy, or total addressable market size may struggle to raise their next round on any terms. As with the growth stage companies, VCs may focus attention on the top 10–20% of their portfolio. Startups that maximized their valuation over the last year or two and whose progress on key metrics hasn’t caught up to that valuation may find it hard to find many eager audiences.

Why doesn’t the VC market adjust to the current climate by just lowering startup valuations? Unless the startup has made substantial financial progress or has developed easy-to-value technical assets, “down rounds” aren’t worth the trouble for most VCs. Recapitalizing a startup requires the new investor to balance the legal rights of the existing cap table, resetting the expectations of employees, and their own needs — it’s almost always easier to fund a new startup.

Investors have to sort out a cash flow logjam.

To understand why VC funding might get harder to raise despite the “dry powder,” it’s important to consider where the money comes from. In his writings, Sakoda compares the dry powder to a snowpack in the mountains that will eventually melt and trickle down to startups of all stages. While this image is a helpful metaphor, it could lead one to believe that the dollars are just sitting around, waiting to be wired to VCs.

The reality is more nuanced. VCs get their funds from pension funds, sovereign wealth funds, endowments, and family offices, among other sources. These limited partners, or LPs, manage incredibly complex portfolios, and this period of upheaval creates a series of nested puzzles they need to solve to fund their commitments to VCs.

In a practical sense, LP cash flow comes from the liquidity received from realizing prior investments — meaning that the money they can actually deliver to VCs depends on their other investments. Unfortunately, liquidity has been much less common over the last year, which may impact their balance sheets. Unrealized gains from markups are nice, but cash returns are what ultimately decides where future dollars are allocated. Add to that the steep drop in public market valuations, which reduces the capital they can expect to receive as lockup periods for recent exits expire. It also forces them to revalue the models of private holdings, further reducing what they expect from future proceeds. All told, these decreases combine to create a “denominator effect” for many LPs whose venture holdings are too large relative to other asset classes. Say a fund aims to have 5% of its assets in venture capital. If the value of all its non-VC assets drop precipitously, it can find itself with more money committed to VC than it is comfortable with.

Then there’s the fact that rising interest rates create other, lower-risk opportunities for LPs’ capital. Funds might rethink their VC commitments not only because their assets are less valuable or less liquid, but also because other investments are looking more promising.

The $290 billion could evaporate.

What happens if an LP suddenly is uncomfortable with their prior commitments to VC? They can either ask out of or “default” on their commitments. Or, they can simply tell VCs they’d prefer the money be invested more slowly.

During the 2001 downturn, many VC firms “returned” their capital commitments to their LPs. As Benchmark’s Bill Gurley recently noted , investors presented this as a noble act of responsibility: they were doing right by their LPs. To a more critical eye, it was an expedient that allowed VCs to start a new fund without the overhang of investments made in a poor vintage. The LPs got their money back and the VCs avoided having to explain a bad track record. Perhaps this practice will make a comeback, freeing funds with high-performing histories to start new vehicles while reducing the overall burden on the LPs.

LPs can exercise soft power.

The scenario above is generally considered unlikely because LPs rarely default on commitments. The implication is that if LPs don’t default then the return of startup funding is a fait accompli. This assessment of the legal structure of partnerships is accurate; reneging on obligations to VCs would damage the LPs’ brands and lead to substantial forfeitures. However, LPs can signal to their VCs that a slower investment preference would be preferable.

While VCs might have a contractual or moral right to deploy at the agreed-upon pace, disregarding their LPs may lead to a lack of participation in future funds. Most GPs will take feedback from their key clients. Whether VCs decide to deploy that $290 billion over two years or five makes a significant difference.

One of my firm’s LPs, Patrick Cairns of Union Grove Venture Partners, explains it like this: “Companies need to raise capital to survive but fund managers don’t need to deploy capital to survive. A 10-year fund might have a 3–5 year investment period in the limited partner agreement. In fact, after a few years of accelerated deployment, LPs might prefer funds demonstrate a bit of patience through the early part of this cycle.”

I hope my assessment of the situation is wrong. Nothing would make me happier than to see a quick rebound of the stock market and a revitalization of the startup bull run that stretched from 2011–2022. Unfortunately, we must also be ready to navigate a prolonged stretch of uncertainty.

In the meantime, startups should reconsider their funding plans and whether they can reach profitability sooner than they might have otherwise. Nadia Boujarwah, the Co-founder/CEO of plus size clothing upstart Dia & Co. recently reorganized her company to get to profitability. “Getting to the point where we controlled our own destiny and were no longer dependent on outside investors was invigorating,” she says. “We had to make difficult decisions, but they have given us the freedom to grow. I would encourage more founders to think hard about finding ways to control their own destiny.”

In any case, the advice I’ve been giving to the founders in my portfolio is simple: hope for dry powder, but until it materializes, try harder to make the most with the resources in your control.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Enterprise Acquirability

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 14 Oct 2022 14:36:27 GMT

Be ready for the next M&A wave

Right now venture investment is falling and the tech market is cooling. Startup founders are thinking more about survival than an exit. But the market can turn around quickly, as it did in 2010. It pays to position yourself now for the startup M&A wave that will likely arrive in the next couple of years. To prepare, you need to see your startup through the eyes of a potential acquirer.

Though these alarming headlines seem all too familiar today, each originally ran from 2007-2010: The Great Recession dramatically slowed venture capital fundraising for many companies, just as recessionary fears are curtailing venture markets today. According to PitchBook, VC investments were down 30% in Q2 2022 compared with 2021, and IPOs hit a 50-year low. While a few iconic brands including Uber, Airbnb, and Square emerged successfully from the last downturn, most venture-backed companies struggled during this period, and many ended up pursuing M&A strategies.

When deal-making slows, VC dollars typically favor the perceived market leader, starving other venture-backed businesses in the same space of capital. While some adapt and survive, others end up retreating and creating M&A opportunity down the line for those left standing. The process starts slowly, but as the chart below shows, venture-backed M&A plummeted during the recessionary period, when venture investing also slowed. During the early recovery, however, VC-backed M&A rebounded and skyrocketed: Annual deal values eclipsed $30 billion in 2010, holding steady before ballooning above $70 billion in 2014.

Regardless of whether you plan to seek a buyer or take advantage of shifting market dynamics to make a strategic acquisition, it’s important to note that M&A processes typically require 12-18 months from start to finish. Today’s abrupt slowdown in VC investment suggests a post-recession-type M&A wave is on the horizon. Startup founders can start positioning themselves now to be acquired in that wave. Unfortunately, many acquisitions occurring between now and then will be distressed. How can you avoid this unnecessary fate?

To get a jump on the process, it’s important to know how you’ll be evaluated by a potential buyer. Most will have a ranked scorecard with specific criteria, such as deal terms, strategic fit, competitive gaps filled, cultural compatibility, potential upside, and finally “lift” – how hard will the purchase and subsequent integration be?

The last category is most actionable. If M&A is likely in your intermediate future, your task today is to reduce a prospective buyer’s lift and increase your “acquirability.” To accomplish this, entrepreneurs should answer the following three questions in preparation for buyers to come knocking:

How scalable are my systems?

You and your potential acquirer may have different definitions of “scalable systems.” From a buyer’s perspective, scalable means they could grow without immediately requiring a substantial investment in infrastructure, even if all they did post-acquisition was direct their pipeline and relationships to your sales operations. While the buyer may eventually integrate your back-office systems, IT stack, and supply and logistics networks, they will first ask whether they could take a hands-off approach and still get value. As an active board member across several companies, I often advise against acquisitions that require additional investments to actualize value. The more straightforward value actualization is, the lighter the lift.

In addition to offering systems with excess growth capacity, scalability also implies audited financials and cleaned-up messes. If you’ve been wavering on closing an underperforming division or settling nuisance lawsuits, do that now. And get dissident shareholders — the ones who demand management’s time in excess of their actual strategic or financial contribution — off the cap table. It’s a delicate message to convey but try framing it as, “It seems the investment no longer meets your needs. When current and new secondary sale opportunities arise, would you like me to contact you?” It’s in the interest of all parties to engage in and explore these conversations early.

How can I insert my company in M&A deal flow?

Getting acquired by the right partner is challenging enough, but if the market doesn’t know both your company and its story, or worse, if the market has the wrong story, a successful M&A process is virtually impossible. Thankfully, there are two tangible things you can do to improve your position.

If you’ve avoided the process until now, it’s time to meet and get to know the three to five investment bankers who know your space cold, and participate in the active transaction flow in your industry. Introductory breakfasts and site visits to your office are a good start, followed by regular 60- to 90-minute check-in conversations. Beyond educating potential advisors, these discussions often yield valuable industry insights.

When you look to hire an advisor, they will need to understand your company, your team and its strengths, and what you’re attempting to accomplish so they’re able to accurately articulate your story to a potential acquirer. This is an exercise in setting your plot line, and while you may never actually activate all these relationships, what you share with a potential financial advisor will likely inform the process later on. Who knows — they may be advising your perfect buyer. This is your opportunity to establish the narrative.

A second non-traditional way to enter the M&A stream is through strategic board enhancements. People join boards for many reasons, but one of them is to leverage their networks. Adding board members who operate in adjacent categories or who have recently retired from larger players in your industry is one of the least expensive ways to expand your profile, gaining access to potential business or strategic partners.

Is my company considered a good business partner?

Buyers are busy, often evaluating several opportunities at once. They’re also humans, and will naturally focus on options that appear most prepared to complete transactions. In establishing your company as a good business partner, ask yourself these questions:

Are your operating plans current?
Is there a detailed version that encompasses the current fiscal year and another higher-level plan for the next 3-5 years?
Do these include detailed organizational design and hiring strategies?
Is your IP fully scheduled and in digital form?

Best practices entail maintaining a consistently refreshed virtual data room even if the business is not actively pursuing M&A. It’s well worth considering how quickly your company could offer this deal-essential information without stressing the organization, or risking underperforming in the middle of acquisition negotiations.

The best CEOs I know keep three active lists on their desks. The first is a list of top executive talent they’d like to hire — a topic for another day. The second is a list of potential acquisition targets, businesses that for the right price and at the right time would increase their long-term value. The third is shorter: companies that could be their right potential acquirer.

Knowing who belongs on your list, and how to get on another company’s list, could make the difference between finding the right partner and settling for a lesser one. When acquisition waves start, they move very quickly. One of the most unsettling feelings is watching weaker competitors get stronger in a downturn by getting acquired by outsized enterprises simply because they were better prepared.

Many of the actions that make your company a desirable acquisition target will also enable you to better weather economic uncertainty. Selling during a period of consolidation isn’t necessarily inevitable, so the goal is to create the option, enabling you to efficiently decide whether that’s the right outcome. The proactive steps above will ensure that the decision to sell is your choice — not a necessity.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Acquisition Strategy, Innovation Strategy and the Patchwork syndrome

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 23 Sep 2022 14:41:37 GMT

Building an innovation strategy leveraging on acquisition is less easy than it seems. Top-down and bottom-up innovation must coexist.

When capital is cheap, companies overdo it on M&A. Too many CEOs have fallen into the comfort zone of strategizing with bankers and external advisors, neglecting to build their internal capacity for innovation. Avoiding this trap requires a wholesale reinvention of a company’s governance, the goal of which is an integrated, internal innovation strategy where top-down and bottom-up innovation support one another.

For more than a decade, unprecedented amounts of cheap money have encouraged companies to outsource innovation through M&A. Too many CEOs have fallen into the comfort zone of strategizing with bankers and external advisors, scheming about which company to buy — and neglecting to build their internal capacity for innovation.

In particular, leadership loses interest in internal innovation when interest rates are low because cheap money creates the illusion that acquisitions are easy. When funding is plentiful, executives can readily execute and justify deals, so they pour all their energy into buying companies instead of empowering internal R&D. But the more a company ignores internal innovation, the more aggressively it must acquire. As it buys more and more outside firms to boost its own innovative capacity, it simultaneously struggles to retain key R&D talent because its internal culture is no longer sufficiently supportive of innovation. I call this the financial control trap because it cedes innovation to financial deal-making.

Even in flush times, this cycle eventually risks the company’s failure altogether, as time and money get spent without sustainably generating any new innovations. Now, as the era of low interest rates and cheap money ends, many companies urgently need a new approach to strategic governance. To escape the trap, companies must replace the top-down approach of M&A with a more inclusive and bottom-up approach to innovation. While M&A seems like a cheap way to become more innovative, the most important information for innovation comes from inside the firm — from those close to customers, clients, suppliers, and technologies. For that reason, effective innovation strategy is inclusive and bottom-up. The financial control trap ignores that truth; and by seeking innovation outside the firm, it weakens internal capabilities.

Let’s make an example:

After the financial crisis of 2008, when interest rates were near zero, a family-owned European media company acquired a series of firms to recharge its profitability and growth prospects. A CEO tasked with transformation invested in digital platforms to make up for declining print media.

The strategy was sound, yet the acquisitions effectively marginalized in-house R&D investment as M&A drew management attention and funding away from internal work. The company did build significant R&D operations in Central Europe, but M&A drove a top-down ethos, inadvertently discouraging innovation from within. In the face of the inevitable culture clashes between old and new, the increasingly financially-minded company failed to integrate the acquired units with the rest of the organization — which kept it from achieving the expected synergies across the portfolio. Organic profit growth did not materialize.

The result was an undifferentiated holding company, rather than the roughly coherent enterprise it was before 2008. All those investments did little to transform the core operations. The “comfort zone” of financial control crowded out the hard path of internal innovation.

The company’s leaders and owners eventually recognized these challenges and began de-emphasizing financial control. But their efforts were too little and too late. Eventually, the company needed a significant capital injection from an outside firm that diminished the family’s authority.

There’s one question I frequently get when explaining the financial control trap: Why can’t the board prevent the company from falling into it? Too often, well-meaning “best practices,” such as ensuring board independence, have estranged the directors from strategy and innovation processes and inadvertently privileged financial control. Board members are not encouraged to go deep into the company’s operations but are instead valued for their networks and introductions they can make to outside firms, including for prospective acquisitions. The board also depends on the CEO’s framing of the innovation imperative since it lacks the inside knowledge and bottom-up connections to challenge and guide a trapped executive leadership. As a result, boards usually end up facilitating acquisitions rather than pushing executives to build internal innovation.

If there’s a silver line in higher interest rates, it’s that M&A will look less alluring. Companies still need to work to avoid the financial control trap, and that requires executives to recognize the importance of internal innovation driven from the bottom up.

To do this, ask yourself: how often am I looking for innovation outside my organization’s walls? And how often do I ask my team what they’re seeing and what they think we should try? The more you do the former relative to the latter, the more at risk you are. It’s tempting to go hunting for that next big acquisition that will transform your company. But that can often be a mistake. The ideas and insights you need are the ones from people who know your company from within and know your customers.

***

Alessio De Filippis , Founder and Chief Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for a different types of organizations (Multinationals, SMEs, startups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Stategic planning and uncertainty

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 15 Sep 2022 07:34:01 GMT

Today’s unpredictable age demands new notions of strategic planning. Planning must be mixed with being fast and flexible.

The future is not perfectly predictable, the more the uncertainty the more is adaptability needed. Nowadays a great strategy is not a good handle on the future but is a focus on making themselves better able to cope with unexpected changes. For strategy, that involves instilling an adaptive mindset among managers, building in flexibility into operations, creating dynamic plans.

Every manager would like to have a clear vision of the future but forecasting the future with any precision is impossible and every single change in the scenario can affect deeply the company's performance. Managers pressure their teams to come up with more accurate projections for how their markets will evolve, competitors will respond, and consumers will react — thinking that forecast precision is the key to defining winning strategies.

But attempting to develop precise forecasts is a fool’s errand. Meteorologist Edward Lorenz proved this nearly 60 years ago when he popularized “ the butterfly effect .” He suggested: “A butterfly flaps its wings in the Amazonian jungle and subsequently a storm ravages half of Europe.” While this statement is often interpreted as meaning “small things can have big impacts,” Lorenz’s insight was actually far more profound: In complex systems, small changes in one variable may have no effect or massive ones, and it is virtually impossible to predict which will turn out to be the case.

Before Lorenz’s work, forecasters assumed that an approximate specification of initial conditions would yield an approximate prediction of future outcomes. Lorenz’s modelling proved that assumption to be entirely false. He found that without a perfect delineation of initial conditions, predictions are useless.

Today, the butterfly effect is almost universal. Our world is more complex and interconnected than ever before. Increasing globalization, advances in telecommunications technology, shifts in consumer preferences, geopolitical instability, and countless other factors have made the future largely unpredictable. Capturing all these relationships in a reliable forecast is impracticable.

To sum up, executives operating under extreme uncertainty have to be sure that their strategy-making is characterized by a willingness to adapt, inbuilt flexibility, and dynamic planning.

Willingness to Adapt

Stephen Hawking is famously credited with saying: “Intelligence is the ability to adapt to change.” If it is impossible to predict what is around the corner, then the secret to success is adapting quickly to what appears.

The Covid-19 pandemic serves as testimony to the importance of adaptability. In early 2020, almost no company’s strategic plan forecast the impact that the global pandemic would have on business. The most successful companies (e.g., Zoom, Amazon, GrubHub, Disney) quickly adapted to the impact that work-from-home orders and other restrictions placed on workers and consumers. Some expanded capacity in response to the sudden surge in demand; others altered their delivery model to serve customers in new and different ways.

Less successful enterprises (say, most commercial airlines and hotel operators) could not — or did not — adapt. These companies saw their revenues and profits plummet. If Covid-19 has taught us anything it’s the importance of being able to pivot quickly in response to sudden changes in the external environment.

Inbuilt flexibility

In volatile times, flexibility has enormous value. As an analogy, consider sailing. In choppy waters, sailing upwind can be extremely challenging — steering as close as possible to the wind can mean pounding into the waves, which slows the boat down. Instead, falling off of the wind can give you a better angle to the waves and allow you to build up speed. While longer by distance, this maneuver is almost always faster, largely because it’s more flexible.

A few industries have already recognized the value of flexibility in the face of extreme volatility and altered their strategies accordingly. Take aluminum production. The future returns on capital projects, such as investments in new smelter capacity, depend upon highly volatile prices for electricity and aluminum. Price uncertainty means that there may be times when the cash inflows from aluminum sales are insufficient to cover production costs, or (stated differently) lower than the revenues that could be derived from the sale of co-generated or contracted power. Most aluminum producers have strategies that enable them to temporarily suspend production during periods of high energy prices and sell available power to the grid. The few aluminum producers that have stuck with rigid, production-only plans have experienced dramatically lower returns than those opting for more flexible strategies.

Another example: sourcing strategy. Today, supply chains are being assailed by a host of exogenous factors. Russia’s invasion of Ukraine, for instance, has cut-off the flow of raw materials such as titanium, nickel, and neon. China’s zero-Covid policy has temporarily halted manufacturing in some sectors, hampering the production of everything from automobiles to smart phones. Companies with flexible supply networks, capable of sourcing from multiple suppliers in different regions, have found it easier to cope with these — and other — disruptions. Those with rigid supply chains continue to struggle in the current tumultuous environment.

Dynamic planning

In an unpredictable world, it might be tempting to throw up your hands and give up on planning altogether. But great performance is rarely the result of happenstance. It requires a direction, even if it isn’t possible to define the exact path.

To better cope with extreme volatility in crafting strategy, companies must change the way they approach strategic planning. They must evolve from a static, plan-then-do model to a dynamic and continuous approach to strategic decision-making and execution.

Dell Technologies was one of the first to adopt this new model shortly after Michael Dell took the company private in October 2013. The company shifted from a traditional planning model — where managers developed a fixed strategic plan each year — to an approach focused on continuously identifying and making critical decisions. This new model — combined with new techniques for making strategic decisions under uncertainty — has enabled Dell Technologies to increase its operating profits by more than four-fold since 2013. Leadership at Dell did not give-up on planning. Instead it adjusted the company’s model to be more fit-for-purpose, given the increasingly uncertain world of technology.

Today’s unpredictable age demands new notions of strategy. As I argue here rigid plans — based on deterministic forecasts — must be discarded and replaced with a more dynamic and decision-focused approach. Flexibility and adaptability must move to the forefront of leadership’s thinking. Otherwise, too many businesses will fall victim to the vagaries of the butterfly effect.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to innovate during a downturn period

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 29 Aug 2022 08:46:45 GMT

The recession is a good period for boosting innovation.

All we expect is a recession but it doesn't mean innovation has to slow down. We have a lot of examples of how in history a downturn period has inspired huge innovation waves.

There are 3 clusters of examples:

Game-Changing Offerings.
Simple, Affordable Solutions.
Bold Strategic Moves.

1. Game-Changing Offerings

Startups with radical products or services that “reverb” off of the big event driving the recession can take off. For example, Airbnb, an online marketplace for “places to stay and things to do,” was founded during the height of the recession in 2008. Its service appealed to thrifty millennials looking for a cheap way to travel, as did Uber’s car-sharing model.

Lingering distrust in traditional finance providers after the global financial crisis helped to spur novel payments providers. For example, Jack Dorsey founded Square (later named Block ), the financial services startup known for its square-shaped white credit card reader, in 2009. “There is no better time to start a new company or a new idea than a depression or recession,” Dorsey, who also helped to found Twitter, reflected . “There [are] a lot of people who need to get really creative to create something new.”

2. Simple, Affordable Solutions

Downturns can be great times to introduce offerings that connect with consumers who have tighter purse strings or are naturally frugal given continued uncertainty.

There was a recession on the heels of World War II, in 1948–1949, before the post-war boom. In 1948, the McDonald brothers fired all their carhops, closed their flagship store, installed new equipment, and reopened three months later with a novel approach for preparing food. Instead of having a single skilled cook who would custom-make orders, McDonald’s simplified the menu so that less-skilled people could prepare the same thing over and over again. All McDonald’s menu items could be eaten one-handed while consumers were driving.

3. Bold Strategic Moves

Downturns can be great times for established companies to make dramatic changes. Shantanu Narayan took over as the CEO of Adobe in late 2007. The 25-year-old company seemed stuck, with products such as Photoshop and PageMaker stagnating. Nimble software-as-a-service (SaaS) competitors were emerging. And the onslaught of the global financial crises would challenge even the strongest incumbent companies.

In the face of these challenges, Narayen and his team undertook a bold transformation strategy. In 2008, they tested a software-delivered model of Photoshop. A few years later Adobe “burned the boats,” stopped producing packaged software and went to a fully SaaS model. In 2009, Adobe purchased Omniture for approximately $1.8 billion, a price 40% lower than its pre-crisis peak (but 2.5 times above its mid-crisis trough!). That acquisition served as the cornerstone of Adobe’s efforts to build a new growth business related to advertising services and analytics. From 2009 to 2019 Adobe’s revenues tripled, and its stock price rose 29% a year, making it one of the decade’s top transformers .

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Digital Culture and AI

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 02 Aug 2022 06:09:04 GMT

AI/ML use will triple to 38% in 2022 thanks to increasing awareness of how growth comes from analytics and digital capabilities.

These are formidable challenges coming. But there are proven steps leaders can take to close the digital marketing gap and manage strategies, organization, and data to make progress toward the growth mission and create a data-driven decision-making organization.

Every company is somewhere along an open-ended journey to achieve data and analytics superiority. While there is no final destination — there will always be more to do — there is a roadmap for efficiently and strategically progressing on the journey. By using the right tools, companies can create a unique profile of their data and analytics capabilities; and they can then address areas of weaknesses and enhance their strengths, driving an ongoing data and analytics transformation that can deliver real sustained competitive advantage.

To help leaders assess their current capabilities and invest in strengthening their capabilities, every assessment must take into account seven dimensions:

Culture : The degree of consensus on the value of data and analytics as a strategic asset
Leadership commitment : The extent to which senior leaders “walk the talk” when it comes to data and analytics
Operations and structure : The level of development of structures and policies that support access to data for those who need it
Skills and competencies : The ability to hire the right people and provide adequate training and support for them
Analytics-strategy alignment : How effectively analytics complements both short- and long-term strategy
Proactive market orientation : The degree to which data and analytics allow the organization to keep pace with and anticipate evolving customer needs
Employee empowerment: The extent to which people throughout the organization are encouraged to pursue creative data capture and analytical methodologies

Preethika Sainam , has developed an interesting framework for helping leaders identify where their firm falls on the spectrum between data and analytics laggards, strivers, and leaders . For example, We use this approach in our Libentium maturity model .

Every company is trying to capitalize on the promise of data and analytics. While a few leading firms like Amazon and Alibaba seem to have cracked the code, most are still finding their way and many remain unsure even where to start. A 2021 study by NewVantage found only 39% of executives believe their organizations manage data as an asset, and even fewer (24%) view their companies as being data-driven. Likewise VentureBeat found, in their 2021 survey, that just 13% of executives believe their organizations are delivering on their data strategy. These underwhelming findings are consistent with earlier studies by McKinsey, Deloitte, and BCG that revealed a large majority of senior leaders are not satisfied with the outcomes delivered through analytics.

Marketers know that digital marketing represents the future of their business. That’s why, according to the February 2022 edition of The CMO Survey , they’re happy to allocate 57% of their budgets to digital marketing activities and are planning to increase spending by another 16% in 2023.

However, the survey also found that this contribution has weakened over the past year. More than 30% of marketers who participated said that they are experiencing average-to-no returns on their investments, which could create funding difficulties in the future if they are not able to overcome this gap.

So, why are returns softening, and what can marketers do about it? There are six reasons behind the digital marketing performance gap.

1 Companies haven’t developed a fully integrated digital marketing organization.

More than 60% of marketing leaders reported in the August 2021 edition of The CMO Survey that their companies were either in the nascent stage (i.e., visualizing and designing their digital transformation) or the emerging stage (i.e., building non-integrated digital elements) of this journey.

Having a digital marketing arm simply isn’t enough. Digital marketing should be fully integrated across the company and used to drive and evaluate marketing decisions to reach its full potential. Unfortunately, that is not currently the case for most companies.

2 Marketing teams face a steep learning curve when it comes to data analytics…

When asked about digital marketing investments, marketing leaders historically have focused on optimizing their companies’ websites. However, in 2022 those investments shifted dramatically, with a 37% increase in the number of companies investing in data analytics, making this the largest investment reported by marketing leaders.

The challenge is that marketing tech stacks are becoming increasingly complex. While companies are investing in the technologies necessary to keep up with their tech-savvy competitors, there is a learning curve associated with enhanced data analytics, so it will likely take time marketers to realize strong returns for their companies.

3 … and they have to master the challenge of converting data analytics to actionable metrics.

Marketers are missing out when they are unable to convert the overwhelming amount of raw data into key metrics — and the strategic actions they would then inform. They need new dashboards to help them interpret and visualize what their new data analytics mean for their current business, as well as formulate actionable recommendations to improve their future business.

It will take time for companies to identify the metrics that are the most important to their business. They must test, iterate, and ultimately agree on reasonable thresholds for metrics that can guide subsequent actions.

4 Mapping the digital customer journey has become increasingly complex.

Today’s companies engage with customers across a growing landscape of apps, social platforms, websites, blogs, third-party sites, and more, meaning the job of mapping the customer journey continues to get more complex.

In fact, only 40% of marketers report having systems in place to track customer engagement in a way that informs their marketing roadmaps. And when asked “how effectively does your company integrate customer information across purchasing, communication, and social media channels” (where 1=not at all and 7=very highly), The CMO Survey has witnessed a flat score of between 3.4 and 3.8 for more than a decade!

While it’s possible that the investment in digital touchpoints is making an increasingly large impact, companies’ inability to track their customers’ end-to-end journeys and accurately attribute sales to touchpoints is negatively affecting their ability to effectively quantify digital-specific contributions.

5 Changing privacy rules mean the loss of third-party data.

In response to growing demand for consumer privacy and in the wake of phasing out support of third-party cookies, the use of third-party data is changing. In fact, 61% of marketers predict consistent or decreased use of third-party data in the coming year.

Marketers see the challenge and report a 24% increase in investments to manage consumer privacy concerns while also working to increase their understanding of consumers outside of their companies’ own websites and apps. This understanding is important to learning about customers, seeing new opportunities, and effectively segmenting and targeting customers — key steps for creating value and converting digital investments into returns.

6 Many firms outsource their digital marketing activities.

Thirty-two percent of digital marketing activities are performed by external agencies and partners, with this number reaching as high as 45% for B2C product companies. Companies historically have not created digital teams in house, given the dearth of talent and the costs of doing so.

But it may be time to rethink this. As digital plays a larger role in companies’ marketing strategies, it becomes increasingly challenging to maintain brand consistency and build out a fully integrated brand strategy if an external agency is driving most, if not all, digital activity.

In addition, marketers are feeling the pressure of accelerating results, just like other business functions. In-house staff are typically able to move faster than agency partners, who have multiple clients. So, if marketers are not at the helm of developing strategy and managing their customer data, they may be losing out on an invaluable piece of the puzzle in their customers’ journey, as well as delivering slower gains back to the business.

How Marketers Can Drive Digital Marketing Returns?

These are formidable challenges. But there are proven steps marketing leaders can take to close the digital marketing gap. Based on our experience studying companies and working with clients, we outline six strategies that offer a broad view of how leaders can manage strategies, organization, and data to make progress toward that objective.

1 Invest in AI and machine learning.

Companies currently use artificial intelligence (AI) or machine learning (ML) only 12% of the time, according to The CMO Survey. Respondents predict that AI/ML use will triple to 38% over the next three years, with 28% of companies investing in this space in the past 12 months.

We think this use and investment level should increase if companies are going to make the most of their data analytics investments to build personalized connections with their customers. Marketers who integrate their first-party data with ML-driven marketing tech can optimize interactions with their most valued customers, rather than all customers, to drive the most valuable outcomes at the most efficient cost.

2 Double down on strategic experimentation.

Some 67% of marketers report that they use digital platforms to test, iterate, and efficiently determine what is or isn’t working in their marketing materials. At the same time, only 47% report increasing investments in online experimentation and A/B testing.

We recommend companies increase these investments with an eye toward more strategic-level experimentation that can offer opportunities for breakthrough growth. Too often marketers get bogged down in tactical experiments, such as whether customers like green or yellow, instead of testing the relevance of new offerings, innovations, or customer segments.

Understanding new challenges and opportunities is business critical, especially in unpredictable times. This makes testing an ongoing, necessary process that requires sufficient budget guided by three key principles. First, budget with current data, not historical projections, by leveraging forecasting tools that take dynamic market changes into account. Second, establish that marketing experiments are an investment, not an expense, by testing to identifiable business-wide goals or outcomes. Third, allow flexibility in any test-and-learn budget. Market trends and consumer behaviors can change, and experiments allow companies to understand and respond to any new challenges or opportunities.

3 Deepen cross-functional collaborations.

Marketers report reasonable success in working with leaders and groups important to the success of digital marketing. Forty-three percent of senior marketing leaders report that their CTO/CIO (or equivalent technology leader) is aware of and aligned with their objectives and path to activate key performance indicators (KPIs) in digital marketing, and 40% report that the same can be said of the CFO (or equivalent financial leader).

These figures are reassuring. However, it still follows that roughly 60% of marketing leaders are flying solo — meaning they are not yet collaborating with these important leaders and groups. Cross-functional collaboration and alignment is essential to not only gain approval/support for marketing investments, but also to accurately understand their impact/contribution. In addition, working directly with the C-suite elevates the marketing function, ensuring that it is a strategic contributor to the business strategy and ideally, protecting it from future cost-cutting initiatives.

4 Embrace a culture of innovation.

Marketing leaders can further digital transformation by helping build several organizational characteristics: a culture of rapid learning, strategic partnerships, specialist skills, and agile structures. We find that collective strength, rather than individual talent, is how organizations should reframe their thinking.

What does this mean in practice? Companies that align their C-suite leaders across the business and focus on shared goals are better positioned for digital transformation. More specifically, organizations execute three key priorities to realize digital marketing transformation. First, they establish a common set of KPIs that ideally are aligned to business objectives, such as revenue, profit, or sales. Second, these organizations prioritize the customer first. And third, marketers truly understand how their customer makes decisions, and they upskill and reskill their teams to ensure that they can accomplish ever-more complex work.

5 Focus on driving growth.

When asked how they evaluate digital marketing’s contribution to their companies, marketing leaders prioritized driving business outcomes (e.g., increased revenue, sales, volume, profits), followed by shaping marketing communications, helping deliver interactive customer experiences, and improving internal efficiencies.

We urge marketers to keep their sights on how digital marketing delivers growth because this objective gets to the core of what they are hired to accomplish and is the strongest statement marketers can make to validate their worth. In other words, all other objectives (e.g., customer experience, internal efficiencies, and marketing communications) should ladder up to increasing sales/revenue/volume, decreasing costs, and ultimately driving the business forward.

6 Leverage first-party data.

Effective first-party data use in marketing delivers more relevant experiences for customers and can generate 2x incremental revenue from a single engagement and 1.5x improvement in efficiency .

Growth comes from incorporating customer data, because only then can marketers truly understand their customer base, including types of customers, their needs, and how their behavior is changing. This understanding allows marketers to find more users like the ones they already have; personalize based on the needs of their customers; and optimize their marketing when looking to find the most profitable customers.

Marketers have used digital marketing to navigate through incredibly difficult business conditions, connecting with customers stuck at home during the pandemic, digitizing products and services, and driving revenues. Now, it’s time to build on those gains by redoubling their commitment to deepening data and digital mastery, building a culture of continuous learning and experimentation, and using insights to deliver personalized services to customers for higher ROI. Those willing to do so will outpace competitors, notching greater revenues and working more closely with the C-suite to drive business expansion.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Corporate Venture Capital: trends to follow

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 29 Jul 2022 18:49:34 GMT

As CVCs become more and more prevalent, entrepreneurs are likely to be faced with a growing number of corporate funding opportunities alongside traditional options.

In the first half of 2021 alone, Corporate Venture Capital funds (CVCs) around the world inked more than 2,000 deals worth more than $70 billion. It’s an increasingly prevalent alternative to traditional funding options such as VCs and angel investors — but how can entrepreneurs determine whether a CVC is the right fit for their startup? The authors discuss the results of a series of quantitative analyses and qualitative interviews exploring the CVC landscape, identifying four common types of CVCs and three recommendations for founders considering a CVC investment: To build a successful partnership, founders must determine the CVC’s relationship to its parent company, the structure and expectations that will guide its decision-making, and most importantly, their cultural and strategic alignment with the key people involved.

Traditionally, startups have looked to three primary sources for funding: venture capital firms (VCs), angel investors, and family offices. But in recent years, a fourth option has grown increasingly popular: corporate venture capital funds, or CVCs. Between 2010 and 2020, the number of CVCs grew more than six times to over 4,000 , and these CVCs inked more than 2,000 deals worth $79 billion in the first half of 2021, surpassing all previous annual tallies .

These corporate investors offer not only funding, but also access to resources such as subsidiaries that can serve as market validators and customers, marketing and development support, and a credible existing brand. However, alongside this added value, CVCs can also come with some risk. To explore these tradeoffs, we conducted a quantitative in-depth analysis of the CVC landscape, as well as a series of qualitative interviews with both founders and CVC executives.

We found that of the 4,062 CVCs that invested between January 2020 and June 2021, more than half were doing so for the very first time, with just 48% having been in operation for at least two years at the time of investment. In other words, if you’re considering a CVC partner right now, there’s a decent chance that your potential investor has little to no experience making similar investments and supporting similar startups. And while more-experienced CVCs are likely to come with the resources and credibility that founders might expect, relative newcomers may struggle with even a basic understanding of venture norms.

Indeed, in a survey of global CVC executives , 61% reported that they didn’t feel like the senior executives of their corporate parent understood industry norms. In addition, because of their parent companies’ business imperatives, many CVCs may also be more impatient for quick returns than traditional VCs, potentially hindering their ability to provide long-term support to the startups in which they invest. Moreover, even a patient, veteran CVC can pose problems if other existing investors aren’t on board. As one founder we interviewed explained, “We had to turn down a CVC because our existing investors believed that taking them on would dilute exit returns and result in a negative perception on the eventual exit.”

Clearly, CVCs can be hit or miss. How can entrepreneurs decide whether corporate funding is a good fit for their startup, and if so, which CVC to pick? The first step is to determine whether the core objective of the CVC you’re considering aligns with your needs. Broadly speaking, CVCs can be sorted into four categories, with four distinct types of objectives: strategic, financial, hybrid, or in transition.

Four Kinds of CVCs

A strategic CVC prioritizes investments that directly support the growth of the parent. For example, Henkel Ventures is upfront about its focus on strategic rather than financial investments. “We don’t see how we can add value as a financial CVC,” explains Paolo Bavaj, Henkel’s Head of Corporate Venturing for Germany. “The motivation for our investments is purely strategic, we are here for the long run.” Similarly, Unilever Ventures explicitly prioritizes brands that complement the consumer goods giant’s existing businesses.

This approach works well for startups that require a longer-term perspective. For example, CEO of nanotechnology startup Actnano Taymur Ahmad told us that he opted for CVC rather than VC investors because he felt he needed “patient and strategic capital” to guide his business through an industry fraught with supply chain, regulatory, and technical challenges.

Conversely, financial CVCs are explicitly driven by maximizing the returns on their investments. These funds typically operate much more independently from their parent companies, and their investment decisions prioritize financial returns rather than strategic alignment. Financial CVCs still offer some connection to the parent company, but strategic collaboration and resource sharing are much more limited. As Founding Managing Director of Toyota Ventures Jim Adler succinctly put it, “financial return must precede strategic return.”

A financial CVC is generally a good fit for startups that have less in common with the mission of the parent company, and/or less to gain from the resources it has to offer. These startups are generally just looking for financial support, and they tend to be more comfortable with being assessed on their financial performance above all else.

The third type of CVC takes a hybrid approach, prioritizing financial returns while still adding substantial strategic value to their portfolio companies. Hybrid CVCs often maintain looser connections with their parent companies to enable faster, financially-driven decision-making, but they still make sure to provide resources and support from the parent as needed.

While certain startups will benefit from a purely strategic or financial CVC partner, hybrid CVCs generally have the broadest market appeal. For example, Qualcomm Ventures offers its portfolio startups substantial opportunities for collaboration with other business divisions, as well as access to a wide array of technological solutions. It isn’t constrained by demands for short-term financial returns from its parent company, allowing the CVC to take a longer-term, more strategic perspective in supporting its investments. At the same time, Qualcomm Ventures still values financial returns, having achieved 122 successful exits since its founding in 2000 (including two dozen unicorns — that is, startups valued over $1 billion). As VP Carlos Kokron explained, “We are in this to make money, but also look for startups that are part of the ecosystem…startups we can help with product or go-to-market operations.”

Finally, some CVCs are in transition between a strategic, financial, and/or a hybrid approach. As the entire investor landscape continues to grow and evolve, it’s important for entrepreneurs to be on the lookout for these in-transition CVCs and ensure that they’re aware of how the potential investor they’re talking to today may transform tomorrow. For example, in 2021 Boeing announced that in a bid to attract more external investors, it would spin off its strategic CVC arm into a more independent, financially-focused fund.

Picking the Right Match

Once you’ve determined whether you want to work with a strategic CVC, a financial CVC, or something in between, there are several steps you can take to figure out whether a specific CVC is a good fit for your startup.

1. Explore the relationship between the CVC and its parent company.

Entrepreneurs should start by speaking with employees at the parent company to learn more about the CVC’s internal reputation, its connectedness within the parent organization, and the KPIs or expectations that the parent has for its venture arm. An outfit with KPIs that demand frequent knowledge transfer between the CVC and parent company might not be the best match for a founder looking for no-strings-attached capital — but it could be perfect for a startup in search of a hands-on corporate sponsor.

To get a sense for the relationship between the CVC and parent firm, ask questions that explore the extent to which the CVC has managed to convey its vision internally, the breadth and depth of its links to the various divisions of the parent, and whether the CVC will be able to offer the internal network you need. You’ll also want to ask how the parent company measures the success of the CVC, and what sorts of communication and reporting are expected.

For example, Tian Yu, CEO of aviation startup Autoflight, explained the importance of in-depth interviews with employees across the business in guiding his decision to move forward with a CVC: “We met the investment team, the key employees from business groups that we cared about, and gathered a sense of how a collaboration would work. This series of pre-investment meetings only raised our confidence levels that the CVC cared about our project and would help us accelerate our journey.”

2. Determine the CVC’s structure and expectations.

Once you’ve determined the CVC’s place within its larger organization, it’s important to delve into the unique structure and expectations of the CVC itself. Is it independent in its decision-making, or tightly linked to the corporate parent, perhaps operating under the umbrella of a corporate strategy or development department? If the latter, what are the strategic objectives that the CVC is meant to support? What are its decision-making processes, not just for selecting investments, but for giving portfolio companies access to internal networks and resources? How long does the CVC typically hold onto its portfolio companies, and what are its expectations regarding exit timelines and outcomes?

For example, after Healthplus.ai Founder and CEO Bart Geerts delved into the expectations of a potential CVC investor, he ultimately decided to turn the funding down: “We felt that it limited our exit options in the future,” he explained, adding that CVCs can be more bureaucratic than VCs, and that for his business, benefits such as greater market access weren’t worth the downsides.

3. Talk to everyone you can.

Ultimately, the people are the most important component of any potential deal. Before moving forward with a CVC investor, make sure you have a chance to speak with key executives from both the CVC and the parent company, in order to understand their vision and culture. It can also be helpful to chat with the CEOs of one or two of the CVC’s existing portfolio companies, to get an inside scoop on issues you might not otherwise uncover.

To be sure, it can sometimes feel uncomfortable to ask for meetings beyond an investor’s typical due diligence process — but these conversations can be pivotal. For example, one entrepreneur explained that their team “loved the pitch from a potential CVC investor, there appeared to be a great match between our strategic objectives and theirs. We got along well with the CVC lead, but meeting the board (which was not intended to be a part of the process) was an eye-opening experience as their questions highlighted the risk averse nature of the company. We did not proceed with the deal.” Don’t be afraid to push beyond what’s presented in a pitch and ask the hard questions of a potential partner.

As CVCs become more and more prevalent, entrepreneurs are likely to be faced with a growing number of corporate funding opportunities alongside traditional options. These investors can bring substantial value in the form of resources and support — but not every CVC will be the right fit for every startup. To build a successful partnership, founders must determine the CVC’s relationship to its parent company, the structure and expectations that will guide its decision-making, and most importantly, their cultural and strategic alignment with the key people involved.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to become a future-proof organization.

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 13 Jul 2022 11:55:51 GMT

When major threats are looming, but their timing is uncertain, it’s hard for business leaders to make an action plan for dealing with them.

What happens when there is credible warning of a looming problem that could disrupt an organization, but the timing and consequences are uncertain? Most likely, nothing. This is the paradox of preparedness, and it happens no matter how loud and clear the alarm. Consider how little happened in response to Bill Gates prescient TED Talk in 2015, when he warned that the world wasn’t ready for the next pandemic. While it’s easy to write off the lack of attention to an uncertain threat, like a viral outbreak at some point in the future, more specific warnings are frequently dismissed as well. The antidote to this disruptive paradox is four attention-getting actions that prompt low-cost preparations, which we outline below.

The paradox of preparedness often prevails because leaders filter warning signals through cataracts of self-deception, myopia, and inertia. Compounding these biases are warning messages that are too cautious and lack a compelling call to action. When there is little incentive to pay attention and prepare, it is too easy to postpone preparations. This was a lesson learned when Roger and his team warned New Zealand businesses in 2015 that their lengthy and fragile supply chains could be disrupted by a global pandemic, yet very little action was subsequently taken.

The prompt for the warning was the 2013–2016 outbreak of the Ebola virus in Africa. Although Ebola was contained, the earlier outbreak of the coronavirus that caused SARS showed that a global pandemic would likely happen again. Two New Zealand companies in the energy and retail sectors agreed with this threat assessment and commissioned a study to prepare themselves and the country. The project included interviewing senior New Zealand government officials and supply chain experts from many of New Zealand’s largest companies. The result was a publicly available report with detailed recommendations for the government and private sector.

The report sent a stark warning that the economy of New Zealand was exposed to the damaging consequences of a global pandemic. It urged companies to improve their visibility into their supply chains, to see which transportation links and suppliers could be at risk, undertake low-cost preparations to make the supply chains more resilient, and strengthen their vigilance capabilities. These vigilance capabilities were needed for the early detection of looming threats and to gain valuable time.

Given the graphic warning, Roger expected leaders to pay attention and act. He was wrong. Neither the government nor the private sector made noticeable changes. Leaders in New Zealand had not experienced a pandemic, and a deadly disease in Africa seemed remote.

Five years after the report was published, it was apparent that an alarming virus was emerging in China. The authors of the 2015 supply chain vulnerability study called the firms involved in the study and recommended they urgently review the report. But it was too late. By 2021, the problems with global supply chains were metastasizing; semiconductor chips were scarce, shipping costs and delays were mounting fast, and many ports were severely clogged.

The pandemic was a classic gray swan event — possible, well-known, and potentially extremely damaging. Compared to black swan events that are entirely unpredictable, gray swans have low expected probability in the near term, and the damage can be contained with low-cost preparations. However, if leadership teams are to pay more attention to the possible threats from supply chain fragility, regulatory exposure, climate change, or digital disruptions, they first need to be persuaded.

Getting and Keeping Attention

The capacity of a leadership team to pay attention is like a sponge. To avoid oversaturating their attention resource with immediate and pressing issues, two principles need to be observed.

First, the collective attention of a leadership team is a scarce resource and can be easily squandered. “Pay attention” is a helpful dictum for parents with distracted children, or leaders overloaded with weak signals of possible threats and opportunities while preoccupied with operational issues. While individual attention may be a fixed resource, a leadership team’s collective attention can be expanded (through setting priorities and changing incentives) while improving the transfer of knowledge via gatekeepers. Someone should be the point person on an issue and accountable for “collecting the paranoia.” Increasing the diversity of a leadership team also expands the collective attention span.

Second, leadership attention must be earned. New information creates the most value when it connects with existing know-how. The richer a team’s existing knowledge base, the more likely they are to pay attention to new information about an issue. There are many ways to get attention. The four approaches we recommend are: learning from past experience, staying alert to anomalies, creating engaging experiences through simulations, and narrating credible stories about the future. Dense reports and PowerPoint presentations rich with detailed recommendations are too easy to forget or set aside.

The choice of approach to emphasize depends on knowing the audience. How do they like to learn? What are their most pressing concerns and priorities? Who do they trust as a source of information and warnings? What barriers to gaining their attention have to be overcome? Answering these questions is key to gaining leadership attention.

1. Start by Learning from Experience

The past is not necessarily a prologue to the future, but it can yield clues about persistent blind spots. A revealing and attention-getting approach is to surface and acknowledge past “hits and misses” by the firm. Leadership teams are asked to recall recent instances when the firm was late in seeing threats and opportunities and had to react — a clear “miss.” The “hits” are when key trends or turning points were seen in time to plot the best moves.

The aim is not to finger-point or scapegoat but to surface persistent patterns in collective foresight or inattention that can be corrected or strengthened. Was there a recurring reason why some events were seen in time and others were missed? This prompts a rich conversation as the leadership team surfaces the underlying reasons in the culture, incentives, or information-sharing systems. A hits-and-misses analysis needs to be done openly. It is prone to hindsight bias due to the tendency to recall past events as more predictable than they actually were.

Toyota executives absorbed some hard lessons after the Fukushima earthquake and the resulting tsunami disrupted their supply chains in 2011. They learned that their famed Just-in-Time production system, with parts reaching their assembly lines only when needed, was an acute point of vulnerability for critical items such as the chipsets powering the onboard computers in their cars.

In a change in practice, Toyota suppliers were required to hold a buffer stock of chips to satisfy Toyota’s requirements for up to six months of production. To add resiliency to their supply chains, Toyota began to practice parallel sourcing. They now have several suppliers of critical components in case one should falter, as happened during the early stages of the pandemic in 2020. Because Toyota learned to protect their supply chains, they were able to operate at 92% capacity, while Ford and GM operated at 60% percent production capacity in the first half of 2021.

2. Stay Alert to Anomalies

The route to preparedness starts when the leadership team is collectively curious about anomalies. Shell CEO Ben van Beurden did this by asking the question: Pushed to the extreme, how quickly could electric vehicles come? His attention had been caught by an alarming anomaly between 2014 and 2016, when oil prices fell while electric vehicles doubled in global sales from 323,000 to 753,000 units per year. In the six years before 2016, the price of lithium-ion batteries used in electric cars had dropped 73%. Shell’s head of planning characterized the resulting challenges facing the company as “radical uncertainty.”

Anomalies are weak signals that are in some way surprising because they don’t fit received wisdom but are not entirely clear in significance. Many anomalies are missed or ignored because people are susceptible to confirmation bias. They are not obviously actionable because they are ambiguous, so pursuing an anomaly requires the exercise of curiosity. But they may be signposts to the future and reveal potential opportunities. This is what Intuit calls, “savoring the surprise.”

Intuit leadership realized that many users of their online money management service Mint weren’t behaving the way they were expected to behave because they were using Mint to manage their self-employment income. They were operating in the expanding gig economy. Embracing this insight, Intuit designed a variation of QuickBooks for self-employed workers, and it became their fastest-growing product.

An anomaly comes to life when it narrates a story about what might happen should it become a reality. A compelling narrative helps spread the news of a potential opportunity or looming threat throughout an organization by capturing the collective attention. It should address these questions: Why is it a departure from our existing assumptions? What could it lead to? What new information would validate or deny the reality of the narrative?

3. Engage the Organization

Preparing an organization for a major change means that key implementers know why the change is needed and accept their responsibility for making it happen. Immersion in role-playing or simulations will help them get there. That was the approach taken by a major health system in New Zealand when leaders foresaw reduced government funding, an aging population to serve, and a shrinking workforce. They needed to fundamentally rethink how health care was going to be delivered.

The process was nontraditional by design. Instead of a top-down vision decided by the leadership team, the process began with the people who worked daily on the front lines. A collaborative view was created of the needed preparations for the future of health care in the region. This became an immersive experience called Showcase — a collection of interactive exhibits that brought to life the vision for their staff. The key question at the end was simple: “What does this mean for you?” The overwhelming response was, “I need to make this change happen.”

Showcase became the foundation for a successful decade-long transformation that also prepared the system to respond to the chaos created when a major earthquake devasted the region. The health system responded incredibly well. When the CEO was asked how people had coped so well, his response was that the leaders had already been preparing the organization for years.

4. Learn from the Future

Preparations against possible threats can be simulated with scenarios. This is a method of rehearsing the future to avoid surprises. It is an engaging process that considers multiple plausible futures and highlights the need to build resilience. Scenario-learning takes intense dialogue that challenges embedded assumptions and conventional wisdom. This provokes a healthy tension that is an essential fuel to collective learning.

A useful set of scenarios is organized around the main uncertainties and offers diverse narratives about what the future might bring. When Shell Oil was trying to grasp the implications of electric vehicles in 2017, company leaders focused on two pivotal uncertainties: (1) global demand for energy, and (2) the likely penetration of alternative energy sources that would reduce the demand for fossil fuels. This created four possible scenarios. One scenario, optimistically labeled Brave New World, combined low energy demand and rapid technological substitution. This worst-case scenario described a world in which demand for crude oil would peak around the mid-2020s. In 2017, Shell leadership had no idea which scenario would best describe the future, nor how quickly each scenario would unfold.

Scenarios work best when they challenge and stretch thinking, and prime the leaders to pay attention to early warning signals that suggest possible preparations. To guide these preparations, a strategic radar is needed to monitor in real-time the leading indicators of the important uncertainties. This is not a dashboard of backward-looking performance metrics, but a forward-looking track of unfolding uncertainties.

“Be prepared” is a useful motto that falls short as a call to action. In retrospect, Roger’s pandemic alert didn’t capture the attention of influential leaders in the New Zealand economy in 2015. The dense report should have been accompanied by an engaging video on the consequences of inaction, an image-rich executive summary, and a press release to highlight the message. We should have convened a meeting of key influencers to push for stress-testing the ability of their supply chains to recover from a future shock. An early warning system to capture indicators of uncertainties and anomalies could have been put in place. The unrealized goal was to create more resilient organizations with robust supply chains that could weather abrupt change. We hope that you use our four action steps, along with the hindsight gained from Roger’s experience, to prevent a miss within your own organization and surmount the paradox of preparedness.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How the new competitive landscape is affecting corporate strategy.

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 01 Jul 2022 13:21:19 GMT

The magnitude of the changes in the technological, social, and natural environment are such that corporate strategy will need to be qualitatively reinvented again for new circumstances.

Strategy is a competitive game, which always evolves in response to competition. But the magnitude of the changes in the technological, social, and natural environment are such that corporate strategy will need to be qualitatively reinvented again for new circumstances.

Back in the 1970s, corporate strategy was largely seen as akin to managing an investment portfolio , in which the corporation allocated capital to different business units as efficiently as possible. The idea was in part that corporate managers were better placed to make well-informed decisions about allocating capital across business opportunities than financial investors. And given thinner capital markets, they needed to carefully balance businesses that generated cash with businesses that consumed it.

But from the 1980s, as capital markets become more effective at financing early-stage businesses, corporate strategy came to be seen as “value management,” in which the job of corporate managers was less about acting as a proxy investor and more about extracting the maximum value from the businesses in hand. In this world-view, investment in new businesses was tied to the concept of synergies — in terms both of real assets and of capabilities — across businesses and it was the responsibility of the corporate center to maximize synergies across its portfolio of businesses and apply the right style of oversight , from hands-off owner through to hands-on manager.

But the business environment has continued to evolve, and it is placing new and different demands on corporate strategists. Six factors are driving these changes:

Dynamism,
Uncertainty,
Contingency,
Connectedness,
Contextuality,
Cognition.

Dynamism

Competitive advantages do not last as long as they used to, as reflected in the acceleration of the competitive fade rate , which measures how quickly market and operational returns regress to the mean, has accelerated markedly in recently years. In practical terms, the result is that the churn rate of companies on lists like the Fortune 500 has increased remarkably.

One consequence of this is that active management of the business portfolio is again important: companies need to ensure that their business portfolios are continually rebalanced in order to maintain growth expectations. A second consequence is that new businesses need to be seeded at a higher rate, requiring large companies to behave more like entrepreneurs in part of their business, and to build the requisite skills and structures to ensure this. A third consequence is that turnaround or transformation has become a prevalent and strategic capability for fixing or renewing businesses which have experienced competitive disruption, reached maturity, or fallen into decline.

Uncertainty

As a product of the technology revolution and other factors, business plans have become less predictable. This is expected to continue with further waves of technological disruption like AI washing through the corporate economy. Furthermore, it seems likely that climate-based technologies and business models will have at least as great an effect.

The consequence for corporate strategy is an entirely new logic for scale advantage. In yesterday’s more stable environment, scale conferred advantage through creating efficiencies, but in environments with high rates of change, scale can potentially help companies manage risk through superior access to information, to maintain operational and financial buffers, and to conduct rapid experimentation. These capabilities combine to create a new dynamic type of advantage: resilience, which delivers long-term performance through uncertain periods .

Contingency

As noted, on average the business environment has become more dynamic and uncertain. But if we look at the disaggregated picture across companies and industries, the variety of competitive environments that businesses — and units within firms — face has also increased. Depending on the uncertainty, malleability or harshness of each, corporations have to adopt very different approaches to strategy-making, each with its distinct processes and tools. These approaches include:

Classical strategy (in which firms compete on scale and position),
Adaptive strategy (they compete on their ability to learn), vision-driven strategy (they compete on imagination, creativity, and innovation),
Shaping strategy (they compete on their ability to collaborate with partners),
Turnaround (they compete on their ability to renew a business).

As a result, corporate strategy must cultivate capabilities to apply and balance these diverse frameworks, picking the right approach to strategy to each business and creating a common platform for operationalizing them.

Connectedness

Only 10 years ago, the list of the world’s largest companies was dominated by banks and oil companies. The same list today is dominated by digital ecosystem orchestrators like Amazon, which create an offering in collaboration with hundreds or thousands of other enterprises. This profoundly changes the role of corporate strategy since the diversity of offerings and capabilities that contribute to a firm’s value creation can now reside beyond the boundaries of the firm. The goal of corporate strategy becomes to create an advantaged position within an advantaged ecosystem, blurring the boundary between corporate and business strategy. More broadly, strategy has become more open to external influence and collaboration, even for non-platform businesses .

Contextuality

For a great part of the last 50 years, succeeding in business was determined by a relatively small set of variables: customer, product, competitor and investor. However, the sheer size of the footprint of business, the size of individual corporations, and increasing concerns about societal and planetary externalities no longer permit managers to adopt such a simplified view: corporations now need to demonstrate purpose, social contribution, trustworthiness, and ecological responsibility. This involves not only matters of intent, measurement, compliance and communication, but also increasingly issues of competitive advantage. A corporate strategy must now create credibility, social contribution, and generate advantage by dealing creatively with new social and ecological constraints, as well as deliver on the traditional variables.

Cognition

Until recently business strategy was largely about human analysis and decision making. But machine learning has now reached a level of sophistication that rivals or exceeds human expert capabilities for an increasing scope of tasks. This has profound implications for corporate strategy. To begin with, the cognitive advantage of corporations becomes a potential axis of competition. This is determined not only by its ability to deploy AI in each business effectively, but also to shift the focus of human minds to more uniquely advantaged areas like ethics, empathy, and creativity. Similarly, companies will compete on designing and orchestrating new types of “bionic” organization that combine human and machine cognition syngergistically.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The valley of tears, the digital transformation and the business modeling

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 01 Jun 2022 17:42:59 GMT

How do leaders make sure that digitalization makes a purposeful and sustainable impact on the business — and doesn’t just follow the next tech hype?

If digital transformation is supposed to be meaningful and lasting, companies must think about changes in products and processes more than changes in technology. But many companies struggle to look past the shiny promises that usually accompany new technologies. As a result, they dedicate too many resources and too much attention to the technology side of digital transformation projects. One approach to counter this imbalance is to think of digitalization as business model innovation rather than technology-related change. I'll show how one simple, well-known tool — the business model canvas — can facilitate the necessary shift in perspective.

At the start of the pandemic, businesses around the globe found themselves exposed to an unexpected boost in digitalization. Those who promised to keep the wheels turning were given carte blanche to do whatever it took to keep a company running. But as many now return to the office, they also realize that the digitalization lodestar is beginning to fade. Where investment priorities were clear early in the pandemic, companies now face the same uncertainties regarding digitalization as they faced before it.

At the heart of this uncertainty is a simple question: How do leaders make sure that digitalization makes a purposeful and sustainable impact on the business — and doesn’t just follow the next tech hype?

Behind this question stand legions of digitalization leaders who have tried to figure out what the latest trend in tech means for them — from AI and blockchain to quantum computing. But while many of these trends are hyped initially, executives have become all too familiar with the hype cycle’s “valley of tears”: the sobering phase after the initial hype when impacts and benefits remain vague and many projects are abandoned. Only a few manage to pull through and reach the “plateau of productivity” where digital investments start to make a difference.

One approach to counter this imbalance is to think of digitalization as business model innovation rather than technology-related change. Over the past three years, I’ve been working with dozens of teams who made that shift. They were faced with digitalization challenges across a wide range of industries (e.g., consumer goods, health care, education, construction, finance) and firm sizes (from small and medium enterprises to larger, international organizations).

While their motivations and goals differed, the teams shared the experience that envisioning business model change — whether in response to digital disruption or to innovate digitally themselves — helped them see technology in context and better understand what measurable changes to expect. Across the teams, one simple, well-known tool turned out to facilitate the necessary shift in perspective: the business model canvas.

The Business Model Canvas

The business model canvas , developed by Alexander Osterwalder and Yves Pigneur, has helped organizations sketch out and transform their business models since the late 2000s. It’s intuitive to use and consists of nine key elements that each business usually relies on. At the heart is the value proposition, usually understood as a combination of pains addressed and gains delivered from a customer’s perspective. Toward the right, an understanding of who a business’s customers are leads to the capturing of customer segments. Between the two, customer relationships and channels sketch out how to relate to customers, stay open to their input, and deliver the value to them. On the more operational left side of the canvas, businesses are encouraged to think about the key activities and key resources needed to create that value. This side of the canvas also takes a look at key partners, such as through strategic alliances or complementary offerings, that help create value or further add to it. The canvas is then underpinned by an understanding of a business’s cost structure and insights into its anticipated revenue streams.

There are two aspects of the canvas that make it particularly suited to the digitally oriented conversations in focus here. First, while it serves as a sort of checklist to ensure that none of the nine key elements is overlooked, it also pulls all of the elements together, allowing for an understanding of the crucial interplay between them. Changes to one element usually have ripple effects across the entire canvas. For instance, changes in how an innovation is monetized will alter cash flows, which then in turn requires key activities to be restructured. This focus on interplay is key in digital transformation projects, too, because their success depends not only on investing in the right technologies, but also on the complementary changes in organizations that ensure that the technologies are used efficiently and effectively.

Second, the business model canvas is strongly rooted in the thinking that a perfect business plan rarely emerges in its final form overnight. Generating one is a process of iterative refinement driven by intense customer testing (e.g., through minimum viable prototypes or focus groups). In this way, smaller aspects of the canvas are subjected to feedback from key stakeholders to see how much sense they make and whether any changes are needed to improve the business model. This spirit is expressed well by the idea of lean startups . Changes that result from such testing can be evolutionary, gradually refining the business model, or even revolutionary, pivoting the whole idea. The same logic can also be applied to digitalization projects because many parts — both technological and organizational — need to be aligned iteratively to make them work.

How to Use the Business Model Canvas for Digital Transformation

Like generating innovative business models, managing digital transformation is one of the key challenges many organizations face today. This is especially true for small and medium enterprises that don’t have the luxury of large transformation budgets, the talent pool that comes with them, or the ability to just spin off their more digital ventures. But luckily, innovating in your business model and transforming your business digitally can use more than one page from each other’s playbook. Like business model innovation, genuinely engaging with digital transformation is not just about paradigm deepening or doing what you’ve always done but with fancier tech. Especially as organizations around the globe are now facing the question of what to make of their recent tech investments, there’s an opportunity to turn the often continuity-focused tech investments of recent years into foundations for a more genuine transformation of the enterprise.

1. Map out the current business model

In order to know where you’re going and how to get there, you first need to understand where you are. Start mapping out your current business model. Going through the nine elements of the canvas for your business — especially if your current business model has grown rather than having been carefully engineered — will add an important baseline to your transformation efforts.

In this process, it is paramount that you do not simply fill in the nine boxes, but that you also pay close attention to the interplay between them. This exercise reveals the fault lines in your current business model.

A company developing and manufacturing medical prosthetics delivers a good case in point. When forced to sketch out their current business model, they realized that their relationship with their end customer (i.e., patients) was fully mediated by the doctors who interact with the patients directly. Not only did this limit the company’s ability to get important direct feedback on products and quality from their end customers, but it also limited their ability to inform patients about further customization options that could be added beyond insurance coverage.

In mapping out their business model, the company realized that they needed to augment their sales and order process to directly involve the end consumer early on. To do that, they brought in a digitally integrated sales management system that would allow them to collaborate with customers and made investments in a manufacturing process that relied on 3D models that could easily be shared online.

2. Engage with the transformation opportunities

Look for ways to engage with the transformation opportunities the previous step revealed. A ranking exercise will allow you to identify the key elements you want to focus on, then you can begin to gradually develop the necessary changes.

In my work with digital transformation teams, an important part of these conversations has been to let go of the status quo to be able to envision not the process of transformation, but its goal. Many of the teams also employed strategic foresight approaches to help with this step. As I mentioned earlier, this second step is not a singular, Eureka-like effort. Carefully experimenting with the key changes and their ripple effects proved to be essential if teams wanted to a) break down the overall transformation effort into manageable chunks and b) make sure that a coherent vision for the transformation resulted.

An international manufacturer of specialized construction machinery had to face the fact that its traditional products were coming under increasing pressure because competitors managed to provide smarter and more networked machines. Working with the business model canvas, the company realized that the challenge they were facing wasn’t a primarily tech-driven one. By carefully reflecting on the impact that smarter and connected products would have on their business model, the company realized that it needed to focus its transformative efforts on developing and communicating an updated value proposition first. Consequently, the focus of the project shifted from being about embedding smart and connected components into their products to understanding the need for an updated value proposition.

Going back to the business model canvas, this step starts introducing changes and updates into your canvas.

3. Derive the necessary changes

Once you’ve sketched out the goal in the second step, the third step is all about deriving the necessary changes to get from the status quo to the digital target. Successful digital transformations have been compared to conducting an orchestra rather than just buying the latest instrument. Beyond identifying the key changes that the transformation relies on, it’s also important to keep an eye on the ripple effects throughout the canvas and be sensitive toward the complementary efforts that will be needed. In this way, training and upskilling can prove to be at least as essential to a successful digitalization as picking the right tool for the task and understanding its business implications.

Continuing the example of the construction equipment manufacturer, the team’s discussions that followed focused strongly on the subsequent adaptations needed in the other parts of the business model canvas and how to design the transformation path to orchestrate the changes. Applying a canvas-based analysis to related cases, such as John Deere’s shift toward smart farming or Caterpillar’s emphasis on monetizing their customers’ need for a reliable service level, provided complimentary insights on how to design the transformation beyond its digital spark.

Work in this step will result in a roadmap that links the transformation goals defined in the previous step to a coherent set of change initiatives. Clearly, technology will remain key in these initiatives. But from the perspective instilled by the business model canvas, providing the actual tool is only a minor change in terms of your business’s key resources. What matters more is a clear understanding and vision of what you can now do differently. Most teams experienced that purposeful changes to their key activities were needed to ensure that technology investments were used effectively and led to lasting transformations in how value was created and delivered.

4. Make sure you’ve hit your target

Finally, a fourth and complementary step is all about making sure that you’ve hit your target. Digital transformations often rely on breaking down the overall transformation efforts into smaller steps and might even prove to be an ongoing effort. Keeping an eye on the business model is essential to keeping iterative efforts on track and helps businesses not to fall into some kind of post-transformation stasis that conveys a false sense of stability. Especially when digitalization of one aspect of the business leads you to realize that other parts of the business need to follow suit, this continued engagement with digital transformation is crucial to staying ahead of the competition.

For example, a chain of convenience stores mostly located at transportation hubs initially faced challenges because of restrictions regarding the numbers of customers in their stores during the pandemic. While this could have been viewed as a temporary issue — requiring a temporary solution — the team’s business model focus encouraged them to think more comprehensively about their value propositions to people “on the move” and pressed for time.

In the process, the team realized that an app they rolled out soon after the first lockdown that allowed their customers to preorder items to pick them up in-store could lead to a more permanent transformation of their business and operating model, including aspects like store layout and supply chain optimizations. For them, focusing on the business model and their key value proposition shifted the key question from “what can we do with this technology?” to “how can technology help us achieve this goal?” and unlocked a cascade of changes far beyond the initial digitalization of the sales channel.

The fourth step is forward looking and emphasizes an important capability-focused approach to digitalization. In other words, rather than focusing on technology, the focus should be on what you can do now that you could not do before. Many organizations around the globe — from businesses to universities and nonprofits — have realized that there is more than one way of doing the things that they do. This has been the real driver and threat behind digital disruption for a long time.

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If your business is facing the challenge of how to maintain its digitalization momentum, drawing on the business model canvas might make for a welcome change in perspective that helps you stay focused on business priorities and impacts instead of just technological trends. If digitalization is supposed to be truly transformative, impacts of corresponding changes are bound to show up in the canvas and its elements.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The winner’s curse and the contingent contract.

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 01 May 2022 08:59:39 GMT

When many investors bid on a target start-up of uncertain value, a common result is that the winner pays more than the target is worth. Winning can thus become more of a curse than a blessing.

The investors should consider adopting what’s known as a contingent contract, which, in effect, allows founders to bet on what they say they believe about their company.

It is common for businesspeople to make excessively rosy predictions of their prospects for success. When entrepreneurs offer overly optimistic forecasts, their investors end up disappointed. But investors can contribute to this problem by encouraging founders to be optimistic. Venture capitalists often push founders to set big, hairy, audacious goals. When potential investors demand a promise of glorious revenues in five years, founders often oblige.

Let’s consider the dynamics created by these incentives. For simplicity, imagine that one investor (an individual investor or a venture-capital firm) is considering dozens of potential start-ups for a single $1 million investment. Using the information provided by the entrepreneurs, the investor assesses the expected value of each investment option. Now think of each assessment as having two parts, signal and noise. The signal is the expectation that arises from an objective evaluation of useful information. In addition, there is also noise or error that comes from the optimistic bias of the entrepreneur, their selective provision of information about uncertain events, and potential exaggerations and other distortions provided by (some) founders. The investor chooses the start-up with the highest apparent expected payoff. The start-up with the most grandiose profit projection is likely to have been biased upward by noise, leading the investor to overestimate its value.

Now let’s turn things around and imagine a start-up founder who manages to start a bidding war between potential investors. The founder is seeking $1 million and wants to give up the smallest percentage of the firm possible in return for the $1 million. You are one of ten potential funders. Due to your willingness to accept the lowest percentage of the firm in return for your $1 million, the founder agrees to the investment terms that you offered. Should you be happy?

We suggest that you should limit your celebration, because you may have just become the most recent victim of the “winner’s curse.” When many parties bid on a target of uncertain value, the winner typically pays more than the target is worth. Winning is thus more of a curse than a blessing. Not only is the “winning” investor in such an auction likely to be the one who placed the highest valuation on the firm, but they are also likely to be the one who most overestimated the value of the firm. If this happens to you, you may ultimately end up cursing both your misfortune and the biased signal that fooled you into overestimating the benefits of winning.

People typically fall prey to the winner’s curse when they fail to take into account the information present in others’ bids. The very fact that other interested bidders are not willing to pay as much as you are suggests they reached lower estimates of the target’s value. If those cautious bidders have useful information, then outbidding them is dangerous, because it increases the risk that you will overpay. The more rivals you outbid, the more likely you are to have paid too much. Winning against a large number of well-informed rivals strongly suggests you have overpaid. Thus, bidders on uncertain commodities competing against many other bidders should lower their bids accordingly. However, most people ignore this consideration, or even bid more aggressively as the number of bidders goes up.

Newly minted MBAs compete fiercely for jobs at venture capital firms, lured by legends of outsized returns from early tech investments. VCs closely guard the decision-making strategies they use to identify those few startups whose fortunes will land in the long right tail of the distribution. Yet a 2012 study by the Ewing Kaufmann Foundation found that the average venture capital fund barely broke even. Why is actual performance so weak? The winner’s curse provides part of the answer: Venture capitalists tend to invest in the most extravagantly overoptimistic start-up founders. Collectively, investors reward founders for providing low-quality, upwardly biased forecasts.

Even if skeptical investors discount entrepreneurs’ forecasts, they will have difficulty knowing the extent to which founders have exaggerated their claims. Access to better information would enable VCs to make better investment decisions. How can they get it?

One idea, used too rarely, allows founders to bet on their forecasts. Professional gamblers challenge each other’s improbable claims with the invitation “Wanna bet?” Negotiators call this wager a contingent contract.

To illustrate, let’s return to the case of the startup seeking $1 million in funding on a valuation of $5 million. A standard contract might provide the $1 million for 20% of the firm. Assume that the founder has made some quantifiable and measurable forecast based on revenues, profits, or future valuation within a specific timetable. As the investor, you could then make an offer in which the percent of the firm that you own for $1 million depends on the founder’s ability to meet their claims.

For simplicity’s sake, imagine that the founder predicts the company will be worth $10 million in two years, at the next round of funding. Instead of offering $1 million now for 20% of the start-up, you could offer $1 million in exchange for:

Fifteen percent of the firm if a new round of funding leads to a valuation of $10 million or more within two years,
Twenty percent of the firm if a new round of funding leads to a valuation of $8-10 million within two years, or
Thirty percent of the firm if the firm’s valuation does not exceed $8 million within two years.

Notice that when the founder believes her claim, this offer will be more attractive to her than an offer of $1 million for 20% of the firm. But if the founder has exaggerated the estimate, she will become less interested in your offer. Her reluctance reveals valuable information. If the founder declines your contingent contract in exchange for someone else’s more generous investment offer, you can feel good that you have reduced your exposure to the winner’s curse.

More broadly, leaders can think about how to create an environment that will lead others to provide accurate information by allowing people to bet on what they say they believe. This same solution can apply to internal funding. Too often, leaders reward employees for their optimism rather than holding them accountable for their predictions. Establishing incentives for accuracy can be a useful tool for helping others reduce bias. The result is better decisions—for leaders, employees, the organization, and the broader society.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The early phase of new ventures and the Death Valley

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 15 Apr 2022 09:12:27 GMT

How do successful companies navigate this tricky period?

The so-called “death valley curve” represents a crucial early phase of new ventures, when substantial work on a new enterprise has begun but no sufficient revenue has been generated. During this period, companies deplete their initial capital in their quest to establish the business.

According to recent estimates, around 90% of start-ups fail . With the global start-up economy valued at $3 trillion , much is at stake.

The so called death valley curve is the phase when substantial work on a new enterprise has begun but no sufficient revenue has been generated. During this period, companies deplete their initial capital in their quest to establish the business.

How do successful companies navigate this tricky period? The steps entrepreneurs should take depend on the strategic situation in which they find themselves. We have identified four phases of the death valley curve and created a matrix on which entrepreneurs can place their business to identify the key challenges going forward.

Our matrix is based on two key challenges that all new ventures face:

Do they have the right business model?
Do they have growth ambitions?

The right business model: to determine whether they have the right business model, entrepreneurs should use the two business-model tests suggested by Joan Magretta: the narrative test and the numbers test. An enterprise passes the narrative test when there is logic and alignment in the business model — in other words, when the story of the business model makes sense. The numerical test focuses on the financial performance of the business model and whether that business model can produce a profit. When turnover exceeds costs, the numerical test is passed.

Growth ambitions: the s o-called “growth ambitions” describe a new enterprise’s projected growth targets in terms of customers and financial performance. It is often these growth ambitions that attract investors to fund the costs in the beginning of the journey. Hence, they comprise an important dimension in the decision-making of new enterprises.

The Four Phases of New Enterprises

When we plot business model success and growth ambitions on a matrix, we can identify four phases of new enterprises: shape-ups, stand-ups, start-ups, and scale-ups. Each comes with strategic challenges.

Shape-ups

These new enterprises have already reached their growth objectives but have failed to maintain a well-functioning business model. The reasons might include a logic that doesn’t make sense any longer as the market has changed (e.g., Tamagotchi ), outdated technology (e.g., investing in personal digital assistants before smartphones emerged), value propositions that are challenged by competitors (e.g., Uber challenging the taxi industry ), or significant changes in customers’ demands (e.g., trends toward non-smoking, veganism, or do-it-yourself). In the latter case, the problem is not that a competitor offers something better, but that customers are disappearing from the existing market altogether.

All these situations have one thing in common: The business model has become irrelevant after significant growth, and the business is now in a declining market. Therefore, these new enterprises need to shape up to survive. Thus, shape-ups face the significant challenge of (re)inventing their business models, whether through innovation, business development, strategic re-positioning, or divestment. At the same time, these companies must restore investor trust as they are managing through a disappointment. Simply put, these enterprises need to reinvent their business models and themselves as entrepreneurs.

Stand-ups

After firms have reached their envisioned size, entrepreneurs’ attention should shift toward stabilizing the business model and securing returns on investment. Stand-ups have momentarily left the valley of death, but that doesn’t mean that their troubles are over. They must do everything they can to remain relevant among consumers, outperform competitors, and fight any complacency that might creep in. Put differently, all their effort must be applied thoughtfully to continue to stand up.

The challenges in this phase are to protect the business model and safeguard related investments. These aims can be achieved by forcing competitors out of the market, optimizing processes and earnings, or gradually developing the business model. Simply put, these enterprises need to protect their business models — both today and in the future.

Start-ups

These new ventures have an ambitious growth target, but have yet to find a well-functioning business model. Their defining elements are their search for a business model and their constant experimentation, often in the form of trial and error.

Start-ups may, for instance, shift focus from one customer segment to another, develop new products and services, or change their payment options from fixed to subscription to on-demand, and back again. They often also try different means of sales and marketing to find customers. Moreover, they develop new capabilities to support all of these mentioned changes.

In short, in a start-up, nothing is fixed and everything is in flux in the quest to find a profitable — and sustainable — business model.

Of course, the search for an excellent business model is not free of charge. However, as everything is small in scale, total investments are typically low. The strategies applied typically include “fail-fast,” “trial-and-error,” “co-creation,” and “crowd-funding” — some of the most popular start-up principles. Simply put, the strategic challenge for start-ups is to find the right business model.

Scale-ups

After a start-up has created a suitable business model, it may choose to scale up in volume, usually following one of two paths:

First, scale can come from onboarding an increasing number of customers. In this case, the business model already encompasses the necessary capabilities and value propositions — the focus is on obtaining as many customers as quickly as possible. This is typical for digital, platform-based business models.
Second, scale can come from replication of the original business model, as seen in franchise systems. Think of a restaurant chain: Apart from back-office functions (such as supply chain, human resources, and IT), identical copies of the business model are established. Scale in customers therefore necessitates scale in resources and capabilities.

For scale-ups, the challenges entail quickly onboarding customers and finding the resources needed to enlarge the business model’s volume so that capabilities grow in line with the number of customers. Simply put, scale-ups need to fund expansion and limit innovation in their quest to live up to the projected growth expectations.

Companies can fall in each of the four phases, but do not have to go through all phases. Consider Amazon, which went rather abruptly from start-up to scale-up . Jeff Bezos found a business model adequate for the advent of the internet, founded a company with a vision of becoming “the earth’s biggest bookstore” from the beginning, and focused relentlessly on long-term growth at the expense of short-term profits.

Yet, sometimes companies do actually go through all phases at different points of time. Consider the trajectory of Facebook. Initially, they were a start-up that had to find a business model. Then Facebook evolved into a scale-up, seeking to obtain growth by scaling their model. When they went public , they essentially turned into a stand-up trying to secure their model. But with the longstanding criticism of their business model and data usage, they may now have fall into the shape-up phase, where they need to reinvent their existing business model and essentially be entrepreneurial again.

Lessons for Entrepreneurs

Our work suggests that there are three key lessons for entrepreneurs:

Know which phase you’re in. First, entrepreneurs need to diagnose which phase they’re in. If you don’t know where you are, you don’t know how to get moving.
Make the decisions required by your phase. All phases come with their own challenges, and entrepreneurs should focus on the important ones related to their current phase. For example, radical innovation and business development are necessary for start-ups and shape-ups — and problematic for scale-ups and stand-ups. Delivering returns on investments are important for stand-ups, but not yet an issue for start-ups and scale-ups, as they focus on selling their dreams and projections to investors.
Secure an alignment between stakeholders. It’s critical that all stakeholders share the same understanding of the phase and related challenges of the new enterprise. If the founder has the understanding that the business is in a stand-up phase, while investors believe it’s in scale-up phase, that will lead to severe conflict that will damage the opportunities of survival of the new venture.

The bottom line is that it’s important to honestly assess the organization’s situation and to craft a corresponding strategy. A failure to understand the situation may result in a significant loss of investor trust and investments. The road out of the valley of death is paved with situational awareness and transparent communication — one phase at a time.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Crafting a powerful long-term vision

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 11 Apr 2022 13:36:25 GMT

You need a vision, a plan, and a team.

Crafting a powerful vision is often considered the sine qua non of great leadership, but it’s only the first step. How can leaders translate that vision into reality — a process that can take years — while the rapidly changing context distracts with the need for daily adaptation? The authors, both advisors to large firms which have undergone significant transformations, suggest three approaches:

Structuring strategic planning processes around the vision, rather than letting it be an afterthought;
Focusing experimentation on questions relevant to the long-term vision;
Investing in training programs to help staff embrace the skills and mindset needed to executive on the vision.

One of the most visible and essential elements of your job as a leader is to create an exciting, unified vision of the longer-term future for your company or unit.

This is difficult enough, but even once a vision is in place, many leaders fail to execute on it over the many years that it may require. For example, a 2018 study by McKinsey found that only 16% of companies that were committed to a multi-year process of digital transformation reported sustainable performance improvement.

Based on many years of consulting work on large-scale change at dozens of firms across many industries, we are convinced that what holds most leaders back is that they don’t translate the vision into a structured plan that they keep in focus over time. Of course leaders know how to set goals, create measurable KPIs, use dashboards, and hold people accountable in the short-term. When change efforts require years, however, tracking often gets fuzzy, falling away in the face of rapidly changing business and economic conditions that force constant adaptation to produce day-to-day results.

Take a large technology firm we worked with. Its senior leaders laid out a five-year aspiration to shift from hardware to software and services. In the first two years after the vision was announced, however, the senior team spent most of its time on activities associated with getting results from hardware products so that the current business wouldn’t suffer. Meanwhile the transformation of the core, while often mentioned in strategic updates and stakeholder reviews, still has not been fully realized.

In contrast, in 2013, Adobe Systems, with revenues of $4 billion, embarked on a major transition from a license sales model to a cloud-based subscription model. The company’s revenue shrank 8% in the first year and was flat in the second year. Skeptics’ and naysayers’ voices rang loud and clear. Bolstered by the resolve of CEO Shantanu Narayen, however, the senior team stayed true to their longer-term strategic intent. Adobe’s recurring revenues reached $6 billion in 2016 and $14 billion today; 80% of those revenues now come from subscriptions and associated sources.

What did Narayen and the Adobe team do right? How do you execute on a vision over time while coping with unanticipated distractions and the pressure to produce short-term results?

There are three approaches that we have seen leaders use successfully to deal with these challenges and realize a multi-year vision — either singly or in combination:

Plan based on the vision. Drive a structured yearly planning process that connects the long-term vision to short-term action
Focus your experimentation. Encourage projects that iterate towards the vision.
Train your people. Develop training and education that makes the vision come alive over time.

Let’s look at each of these more closely.

Vision-Based Planning Process

Most companies engage in a yearly planning process to define corporate objectives and budgets and then cascade these into goals for the business unit, department, and so forth. The starting point of this exercise is often financial, based on questions such as what numbers do we need to satisfy investors? and how much improvement is possible over last year’s performance? But this approach forces short-term thinking . While the longer-term vision might be cited during the process, it isn’t the driver for strategy, resource allocation, or individual action.

Instead, begin your planning process with the longer-term vision. That’s what Jack Welch did as CEO of GE when he insisted that his leaders begin their planning process with “dreaming” sessions. His team would identify longer-term possibilities for what their businesses could become, and then shape their yearly plans with those opportunities in mind.

Similarly, for over 20 years Salesforce CEO Marc Benioff has been using a planning method that begins with his steady overall vision for the firm and its software-as-a-service approach. He calls his method the V2MOM — vision, values, methods, obstacles, and measures. At the beginning of each year, Benioff drafts a one-pager for the entire company which, as the acronym suggests, first articulates the firm’s overall vision and then spells out his thoughts about the key steps needed to move towards it. (The vision stays largely steady from year to year while the implementation priorities and methods change.) He then gives the document to each of his direct reports and asks them to work with their teams to create a V2MOM document for their own groups. The leadership team then goes through all the V2MOMs to achieve full enterprise-wide alignment and commitment to their strategic intent for the next 12 months. Doing this ensures that every unit of the company understands and has agreed to the balance between short term goals and the longer-term vision in their daily work.

Focused Experimentation

Of course, not everyone is a founder or CEO who can drive vision-realization from the top. Leaders at other levels can also drive deliberately toward a large-scale goal over time, particularly if they hone experimentation that is already happening in the company specifically to iterate towards that vision.

More often than not, visions don’t become reality in straight lines; and we don’t always know what it will take to get there. That’s where experimentation comes in — shaping small tests to find out what will work and what will not on the path towards the vision, while also building support for it along the way. But without a focused approach, this experimentation may not lead to the ultimate goal you are trying to reach.

Take the story of Gary Scholten, an executive who led a successful effort to transform the Principal Financial Group, a global investment management firm, into a digital-first enterprise over the course of 11 years, despite all the distractions and change that came from the tenures of three different CEOs.

Scholten began advocating for a digital-first approach in 2011, when he was the company’s corporate chief information officer. Even as the company made impressive initial advances toward that vision, each business unit responded differently, so those achievements were uneven. For example, the international business embraced mobile more quickly than its US counterpart because many of their customers had better access to cell phones than to computers.

Several years later, now as head of corporate strategy as well as CIO, Scholten formed a digital strategy committee to oversee these efforts (the group included the corporate CMO, the business unit CIOs, and their senior business leaders). Together they identified dozens of digital experiments already underway at various levels of the company. Assessing each one, they identified six where the company should double down and invest proactively because of a clear sense that they would lead to faster growth or better efficiency or scalability than competitors. These included retirement enrollment tools to help employees save at a higher rate, robo advice embedded into life events, and AI based investment research tools. By the end of 2020, when Scholten retired, these investments (and others that were added subsequently in a similar process) had an internal rate of return exceeding 20%, with two-thirds of the benefits coming from top-line growth — and the firm had indeed shifted much of its business to digital platforms.

Training and Education

The third approach is to invest in an educational process that gives people in the organization a deep understanding of what the vision actually means and how it could transform their work.

An example of this approach is illustrated by the work of Dr. Mieko Nishimizu, a former vice president for the South Asia region at the World Bank. When Nishimizu took on the role in the late 1990’s, the World Bank addressed economic development and poverty-reduction largely through a top-down approach of expert technical analysis, policy adjustment, and lending. Her vision, however, was for local communities and societies to take ownership of their own economic destinies and for institutions like the World Bank to function as more as partners, catalysts, and providers of resources to help them do that.

This vision required a profound shift in what the Bank did and in how its staff worked with local individuals. For years, World Bank staff would parachute into countries from Washington and tell governments what to do. Now they would need to learn how to listen not just to officials, but to those who experienced poverty and then work with them, side by side, to develop solutions.

To help them make the shift, Nishimizu created what came to be called the “village immersion program” in which members of her team would live the lives of the poor, in their villages, for two weeks. Her goal was not only for her staff members to understand the new role of the organization intellectually, but to help them develop an emotional understanding that would eventually lead to changes in behavior. Eventually, Nishimizu made this program mandatory for certain categories of staff in her region, and over the course of several years, over 200 staff members participated.

While this program was evolving, Nishimizu did continue to meet the yearly requirements for already-established projects and lending, but gradually, with the altered sensitivities of her staff, she changed the nature of the region’s projects — and the image of the World Bank.

Of course, none of these approaches is easy, and they all require adjustments along the way. Benioff still works with his team to deal with tradeoffs between long term vision and short term-results. Scholten was able to successfully encourage digital experiments, but they didn’t coalesce into the full vision until he figured out that the company would need to double down on a few, company-wide investments. Similarly, Nishimizu made progress in changing the World Bank’s approach to poverty reduction not only by giving senior leaders an opportunity to experience village life, but also by gradually leveraging the new-found understanding to reshape the nature of the Bank’s projects.

Turning a vision into a new reality doesn’t happen overnight. But if you have persistence and stay true to your vision, it may be the most important contribution you’ll ever make as a leader.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Srategic M&A: the vision and the long-term strategy

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 25 Mar 2022 13:22:54 GMT

The companies have to continually reinvent themselves and to use M&A as a key enabler to accelerate where the organization is going.

While M&A as a lever to drive growth, innovation, and transformation is more vital than ever, most companies struggle to use M&A effectively. Leaders often attribute this struggle to the difficulties of executing or integrating M&A, but we believe that the root cause is more fundamental: a failure to link M&A to a strategy for the future.

This failure manifests itself in several ways. Many companies have strategies that focus only on the near-term and that do not contemplate major industry or environmental changes. As a result, M&A scans are likely to surface only the most obvious candidates—where competition for deals drives up premiums. Companies that do consider bolder M&A moves may end up making bets that are hard to justify. Even when a robust long-term strategy is present, it often fails to consider the optimal role of M&A in achieving the strategy. Finally, once a deal is made, these problems can be compounded when the acquirer prioritizes short-term tactical objectives—such as realizing synergy targets—at the expense of longer-term, more transformational growth goals.

Without the proper strategic framing, M&A has little chance of having long-term impact. This is borne out by the numbers: more than 70% of mergers and acquisitions fail to deliver on their expected financial benefits.¹ And yet M&A continues to be an imperative for large organizations. Companies with more than $1 billion in revenue typically depend on mergers and acquisitions to deliver as much as 30% to 50% of their growth.²

Meanwhile, the pace of “ creative destruction ” within industries has accelerated over the past 50 years, and the lifespans of large companies are shrinking, with half of the S&P 500 expected to be replaced over the next 10 years.³ This increased turnover heightens the need for companies to continually reinvent themselves and to use M&A as a key enabler to accelerate where the organization is going.

But our consulting work and research reveals significant benefits when companies take a “future-back” approach to strategy and M&A. This involves developing a long-term strategy that incorporates a granular understanding of the role M&A will play in executing it. In doing so, leaders can overcome known shortcomings and design a purposeful M&A program that delivers impact.

Examples of Strategic M&A

Leaders must look beyond consolidating existing markets and towards aligning key deals with a long-term vision of the future. To be sure, moving into new markets is a challenging proposition. But the potential rewards are commensurate with those challenges. One example is Google’s 2005 acquisition of Android for $50 million. The deal was small enough to barely make the business press at the time. But Google saw in the startup the beginnings of a major growth initiative in smart phone technology that led to the world’s largest mobility platform.

Disney’s $7.4 billion acquisition of Pixar was also driven by a strategic vision. Pixar’s content not only meshed well with the Disney brand but also supported its franchise-focused growth strategy combining powerful storytelling with cutting-edge technology. Since the 2006 deal, Pixar has continued to unleash a string of hit movies while also bringing fresh leadership to Disney’s own separate animation division.

For an even more systematic example, we can look to IBM after Sam Palmisano was named CEO in 2002. IBM developed and executed a 2010 strategic plan for moving into higher-value software and services. Key themes included Business Analytics, Smarter Planet, and Cloud Computing. As a result of this strategic and synergistic approach to M&A, IBM’s average acquisition (2002-2005) doubled its direct revenue within two years.4 By 2010, IBM had remade itself into a company better known for its business analytics capability rather than computing devices—and it surpassed original financial targets.

Future-Back Strategy, the right framing fon M&A

What all these M&A programs had in common was a strategic vision. By contrast, strategic planning in most large organizations has a short-term focus; indeed, it is often simply an annual budgeting exercise. This is (somewhat) understandable given the quarterly performance pressures leaders are under from shareholders, but it comes at a cost: a “present-forward” strategy that extrapolates from assumptions about the past and present, without sufficiently contemplating the future and how it might be different. It is essential for leaders to develop a shared, long-term view of the future and the company they want to create for that future. Such a view is critical for having the conviction required to make near-term resource and investment decisions that have a longer-term, transformational impact—including decisions about M&A. Future-back strategy is designed to address that challenge.

A future-back strategy is best developed through a series of “leadership dialogues” with the top executive team. Leaders start by examining transformational trends, internal capabilities, and current growth momentum. Through structured discussions, teams align around a set of assumptions that describe a shared view of the future. The key is not to develop complete certainty around these assumptions (which is impossible), but to make them as accurate as possible and to make them explicit. Having this clear view of the future world is what enables the leadership team to make considered choices about long-term strategy—where to play and how to win.

Desining the Future-back M&A program

Developing a future-back strategy provides a leadership team with the right strategic and financial clarity to make the big decisions about how and when to use M&A. Critical issues include:

When to build, buy, or partner: Very few companies can execute on new strategic opportunities with the skills on board today, which is why partnerships and
acquisitions are required.
How to reallocate capital to support both the core and new businesses : M&A will require capital sourcing, and the financial decisions must be integrated into the plans for earnings, debt, dividend payout, share buy-back, and other investments.
What to stop doing: Companies need to determine select areas to focus on and stop doing what does not have strategic and financial merit.

Aligning the company on these decisions will create a clear point of view about how M&A contributes to the portfolio of current businesses, adjacencies, and new strategic opportunities that are part of a long-term growth plan. This alignment yields a “north star” vision that guides planned investments and financial expectations. The objective is to do deals that are purposeful, proactive, and powerful to accelerate your competitive leadership and your long-term strategy.

The guiding principles of Future-back M&A

Set M&A goals that clearly support long-term aspirations.
Be disciplined in pursuing acquisitions that align to these strategic and financial
aspirations.
Ensure that your approach to due diligence enables the strategic M&A objectives.
Assess leadership talent and align the right leaders to execute integration.
Adopt an integration approach that builds, preserves, and drives value by balancing
short-term tactical goals with longer-term strategic ones.
Put in place a performance management system that ensures accountability of the
strategic and financial expectations of the deal and delivers transparent results to the Board and shareholders.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

AI-enabled platform business model for legacy companies

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 11 Mar 2022 18:24:15 GMT

How Network Orchestration Business Models are enabled by AI technologies.

Platform companies like Facebook, Amazon, Google, and Tencent have created value at stunning rates. They grow rapidly and own few assets — and they’ve all made strong use of AI. What can legacy companies learn from these platforms? And is it possible for legacy companies to use this business model, too?

The growth of artificial intelligence has enabled a variety of new strategies and business models, from programmatic ad targeting to the sharing economy to the metaverse. The companies that have been most successful in employing these models — digital natives, almost to a one — have been “multi-sided platforms,” in which a company at the hub of an ecosystem or business network coordinates services and reduces friction for customers. Facebook, Apple, Airbnb, Amazon, Google, Uber, Alibaba, Tencent, and the other leading platform companies have created value at astounding rates.

It’s no coincidence. Research has shown that multi-sided platforms have the highest valuations of the dominant alternative business models — more than four times the annual revenue multiples attached to some legacy business models. This is largely because they grow rapidly and have to own relatively few assets themselves.

Platform business models typically generate large volumes of data from all participants in the ecosystem, and AI is required to make sense of it all. Machine learning helps match customers with the products and services they need or want, and provides a seamless experience across the ecosystem. And the millions of customers that use platforms need highly efficient customer service, i.e., intelligent agents and chatbots. So, it’s not surprising that the leading platform firms listed above are also world leaders in the application of AI to their businesses.

But traditional businesses can also organize multi-sided platforms. They too can use data and AI models to orchestrate services for customers across an ecosystem of companies. It requires new strategies, new technologies, and new business relationships, but when companies make the transition successfully, they can achieve the rapid growth and customer loyalty that the digital native platforms have accomplished.

There’s evidence that more traditional firms that employ AI aggressively are adopting an ecosystem (and perhaps eventually a platform-based) approach. In the Deloitte 2021 State of AI in the Enterprise survey, the two highest-achieving groups of AI users in the survey were substantially more likely to have two or more ecosystem relationships (83% among the two highest groups, versus 70% and 59% among the two lowest groups). Companies with more diverse ecosystems were 1.4 times as likely to use AI in a way that differentiates them from their competitors. In addition, organizations with diverse ecosystems were also significantly more likely to have a transformative vision for AI, to have enterprise-wide AI strategies, and to use AI as a strategic differentiator.

These firms may not have full-fledged platform business models, but creating broader ecosystem relationships is a first step toward AI-enabled platforms. Beyond that step, here’s how companies are turning themselves into platforms with AI.

Not Just Digital Natives

A few “legacy” companies have already created AI-enabled platform models. Using these models, the companies are generating more customers, leading to more data, leading to better models, leading to better customer offerings — a virtuous circle. Others are in earlier stages but hope to eventually achieve the same outcome.

Perhaps the single best example of the virtuous circle from platforms is Ping An in China, which began as an insurance company in 1988 but now describes itself in terms of its five ecosystems — financial services, health care, smart cities, automobiles, and real estate — each of which constitutes a platform. In health care, for example , Ping An’s platform connects government, patients, medical service providers, health insurers, and technology. The Ping An Good Doctor system provides both online and in-person consultations, and uses AI to dispense medical advice to members with mobile devices.

The size of the ecosystem is staggering — it provides diagnosis and treatment for more than 3,000 common diseases, has almost 350 million users, more than 1,800 medical and nursing practitioners, and nearly 10,000 health care experts across China. It partners with 110,000 pharmacies, 49,000 clinics and more than 2,000 medical examination centers. In 2020 it performed more than 830,000 medical transactions per day. These numbers illustrate not only the size of China’s population, but also the rapid scaling possible with a platform-based business model.

While the primary value of the platform is to grow the business and provide effective health care, it’s also useful for accumulating insights to train AI models. The Ping An health care ecosystem can draw upon claims and payments data from payers, treatment data from care providers, prescription data from pharmacies, symptom data from patients, and other types of data from other ecosystem members. By 2020 Ping An had data on more than 30,000 diseases and more than a billion medical consultation records.

Several other AI-fueled companies, including Airbus’s Skywise, Shell, Anthem, and SOMPO in Japan are also pursuing the ecosystem idea, but are at earlier stages than Ping An. At this point they are still exploring business and revenue models, but are pursuing data sharing and integration approaches, and beginning to develop AI applications to analyze the data.

How Midsize Companies Can Compete

It’s not just major companies with big R&D budgets that can make this pivot, however. CCC Intelligent Solutions, founded in 1980, illustrates how a midsize company can compete effectively using an AI-enabled platform model. Its platform is focused on digitizing the automotive insurance economy, and easing claims and damage repair friction for millions of drivers every year. Through its relationships with more than 300 insurers, more than 27,000 repair facilities, more than 4,000 parts suppliers, and all major automobile OEMs, it has assembled more than $1 trillion of historical claims data, billions of historical images, and other data on automobile parts, repair shops, collision injuries, regulations, telematics and multiple other entities. As with several of the other ecosystems mentioned above, each new member provides more value to the network and more data, leading to better AI models.

CCC aggregates data — and increasingly, powers AI-enabled decisions — for its platform in order to quickly and efficiently process claims for the end user. All of the resulting transactions take place in the cloud, which connects 30,000 companies, 500,000 individual users, and $100 billion in commercial transactions.

Over the past several years CCC has developed a “touchless” claims offering that is being used by USAA and other leading insurers. Insured customers who are involved in an accident can take guided photos on their mobile devices, send them to their insurer, and receive an automated estimate in seconds. Such an AI-enabled innovation required years of technology refinements, as well as collaboration with ecosystem members to integrate the capability into their claims and repair processes.

What It Takes to Succeed with AI-Based Platforms

The companies above all have different business needs and are deliver different services, but there are common threads in how they’ve approached their platform pivots. Companies wishing to create and prosper with AI-enabled platforms need to accomplish a series of steps. They include:

Strategize about how ecosystem relationships will improve your offerings, and seek out those partnerships. Business strategy will dictate what platforms your company needs to form and how that will improve its products and services. Implementing the strategy may require building or buying new business capabilities. Ping An, for example, decided that instead of offering only insurance services it would build a financial supermarket for customers. It already had some capabilities, but it built a wealth management offering (Lufax) and bought an automobile portal (Autohome). Ensure that data comes with the relationship. A big part of the value of the platform is access to partners’ data, so ensure that partnership deals include access to the needed data and the ability to use it in AI models like customer/offering matches and recommendations. Develop an API-based IT services architecture. Ecosystem partners will need to easily access data and decisions made by AI systems. By far the easiest way to do that is with application program interface (API) architectures. CCC, for example, has built its cloud-based API network that lets providers easily interface with the company. Identify the key decisions that AI needs to make, and gather the data to train models. In most cases AI will be used to make a decision. For Ping An’s health care platform, key decisions include what is a patient’s most likely diagnosis, whether the patient needs to visit a doctor, and what is the recommended treatment. The decisions facilitated by CCC’s platform include the exact damage to a vehicle and the cost to restore it, which ecosystem partners need to be involved in the repair, and which off the needed services. Design a seamless process from the customer’s standpoint. A major part of the appeal of an platform model for customers is removing friction so that they don’t have to understand all of the participants and complexities involved in a solution, whether it’s a medical treatment, a collision repair, or airplane maintenance. Companies creating a platform need to work with their partners to design and implement a smooth, seamless process to meet the customer need. Use data from across the ecosystem to improve models and offerings. The machine learning models that power platform decisions are not a “set and forget” approach. They will get better at predicting or recommending over time if they are retrained on new data. They should be retrained whenever major new data sources emerge, or when they are no longer doing an effective job at the decision with which they are charged.

Looking at legacy firms that have successfully done just that, companies should:

Strategize about how ecosystem relationships will improve your offerings, and seek out those partnerships.
Ensure that data comes with the relationship.
Develop an API-based IT services architecture.
Identify the key decisions that AI needs to make, and gather the data toBoth the digital native platform companies, and the legacy companies we’ve studied as well, illustrate the value of an AI-enabled platform business model for companies and their customers. It’s difficult to grow rapidly without a set of close business partners, and it’s difficult to make sense of their data and provide value to all parties without AI. We expect to see many more of these platforms in the future. train models.
Design a seamless process from the customer’s standpoint.
Use data from across the ecosystem to improve models and offerings.

Both the digital native platform companies, and the legacy companies we’ve studied as well, illustrate the value of an AI-enabled platform business model for companies and their customers. It’s difficult to grow rapidly without a set of close business partners, and it’s difficult to make sense of their data and provide value to all parties without AI. We expect to see many more of these platforms in the future.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Cross-pollination, Innovation and Problem-solving

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 08 Feb 2022 14:52:53 GMT

Someone else has already solved your problem.

According to our previuos article Cross-industry innovation as a competitive " Someone else have already solved your problem ... let’s start looking to the outside world ". We continue to explore the idea of cross-pollination as a strategic asset.

The chaos and crises of the last two years have created all kinds of questions for leaders and organizations. One of the biggest questions is: Do we have new ideas about where to look for new ideas? When it comes to innovation and problem-solving, there will always be a place for old-fashioned, time-consuming R&D — research & development. Today, though, there is also a place for a different kind of R&D — rip off and duplicate. The fastest way for organizations to make sense of challenges they are seeing for the first time is to survey unrelated fields for ideas that have been working for a long time. Why gamble on untested strategies and insights if you can quickly apply strategies and insights that are already proven elsewhere? That’s how leaders can help their colleagues keep learning as fast as the world is changing.

A big challenge in times of disruption and uncertainty is for people and organizations to keep learning as fast as the world is changing — to analyze problems they haven’t encountered before, to make sense of opportunities they haven’t thought about before, to process emotions they haven’t experienced before.

That’s why leaders should encourage their colleagues to learn from experts in fields they haven’t worked in before. Practices that are routine in one industry can be revolutionary when they migrate to another industry, especially when they challenge conventional wisdom in that industry. What better way to fuel your company’s imagination than to look for inspiration outside your field? If you want to learn fast, learn from strangers.

In their well-researched guide to leadership and organizational learning , Christopher E. Bogan and Michael J. English share a case study from business history that illustrates how accepted ideas from one field can quickly transform another field. “In 1912, a curious Henry Ford watched men cut meat during a tour of a Chicago slaughterhouse,” Bogan and English write. “The carcasses were hanging on hooks mounted on a monorail. After each man performed his job, he would push the carcass to the next station. When the tour was over, the guide said, ‘Well, sir, what do you think?’ Mr. Ford turned to the man and said, ‘Thanks, son, I think you may have given me a real good idea.’ Less than six months later, the world’s first assembly line started producing magnetos in the Ford Highland Park Plant.”

Or consider a more timely case study: A recent article in the Wall Street Journal described how the chaplain of an overwhelmed hospital in Chicago helped nurses deal with the mental and emotional fallout of the early Covid-19 wave and the rise of the Omicron variant. This chaplain, who was also an Army veteran, “noticed that phrases nurses were using in conversation sounded like what he had heard from troops who had served in combat zones.” So he borrowed concepts and techniques developed by the Army to help troops deal with the trauma of war to create a program to help nurses stay strong as they waged war on the virus. “He could actually draw that parallel between us and people that have been veterans of war,” one nurse marveled about the chaplain and his program. “I never even made that connection because for me — I’ve been a nurse for 20 years on this unit — I’ve never seen this kind of trauma to our team ever. So when he made that connection, I was like, oh my gosh, how have I never realized that?”

Not all cases of learning from strangers can be this inspiring, but they can be just as effective. Several years ago, for example, London’s Great Ormond Street Hospital for Children, renowned for its cardiac care, was struggling with poorly designed “handoffs” when it transferred patients from one step of a complex medical procedure to the next. So Dr. Martin Elliot, head of cardiac surgery, and Dr. Allan Goldman, head of pediatric intensive care, studied high-powered professionals from a totally unrelated field who were better than anyone at organizing handoffs — the pit crew of Ferrari’s Formula One racing team.

The doctors and the pit crew worked together at the team’s racing center in Italy, at the British Grand Prix, and in the hospital’s operating room. Members of the pit crew were struck by how clumsy the hospital’s handoff process was, not to mention the fact that it often lacked a clear leader. (In Formula One races, a so-called “lollipop man” wields an easy-to-see paddle and calls the shots.) Moreover, they noted how noisy the process was. Ferrari pit crews operate largely in silence, despite (or because of) the roar of engines around them. Thanks to the techniques they learned from these outsiders — techniques that were accepted wisdom in racing circles — the hospital redesigned its handoff procedures and sharply reduced medical errors.

Learning from strangers doesn’t always have to be about dealing with an unfamiliar situation or solving a specific problem. Colonel Dean Esserman, during his transformational tenure as chief of police in Providence, Rhode Island, pushed his insular department to open itself up in general to original ideas, fresh perspectives, and new ways of thinking. One of his initiatives was a fabulously creative program called “Cops and Docs.” On a regular basis, Esserman’s detectives sat in with doctors at Brown University Medical School as they discussed tough cases. The detectives watched and listened as the doctors analyzed clues about a patient (symptoms), sorted through evidence (test results), and identified the culprit (disease).

In turn, doctors sat in on the police department’s command meeting to learn how cops dealt with conflicting and confusing information, ruled out suspects, and cracked their cases. Esserman’s goal was for his department to “become a place that embraces research, that figures out and spreads methodologies that work in ways that medical schools do.” For set-in-their-ways detectives to learn new perspectives on policing, their chief understood they had to learn from experts in a field unrelated to policing.

Today, though, there is also a place for a different kind of R&D — rip off and duplicate. The fastest way for organizations to make sense of challenges they are seeing for the first time is to survey unrelated fields for ideas that have been working for a long time. Why gamble on untested strategies and insights if you can quickly apply strategies and insights that are already proven elsewhere? That’s how leaders can help their colleagues keep learning as fast as the world is changing.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Technology investment and the Strategic Vision

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 29 Dec 2021 17:04:27 GMT

Sometimes there is a lack of vision in managing large technology investments.

Too many executives today feel they are behind on digital investments, including cloud computing, AI, and other technologies that competitors and tech vendors flaunt, all while using a significant portion of their discretionary investment to keep existing technology up to date. Despite large investments, relatively few of these technologies are driving companies toward a differentiating outcome that truly matters to customers. So how do you shape your technology agenda so it enables you to build the right capabilities and deliver outcomes that fuel your competitive advantage? Here are six imperatives to consider. Thoroughly working through these six areas before you engage in any large tech project will help you focus your investment on the outcomes that matter most, raise the return on these investments, and connect technology directly into the center of your differentiated future.

If your organization isn’t making large tech investments, you’re in the minority. Indeed, nearly half of the CEOs in PwC’s 24th annual CEO survey (2021) reported plans to increase their rate of digital investment by 10% or more — more than any other spending category.

With all of this investment, it’s alarming that most executives we talk to are concerned about their struggles to meaningfully differentiate from competitors. Much of their current tech investment, unfortunately, is made in an effort to “keep up” with the rising table stakes requirements of the digital age. In fact, while 56% of executives taking the PwC U.S. Cloud Business Survey see cloud as a strategic platform for growth and innovation, a full 53% of companies are not realizing substantial value from their investments.

Turning this around requires a change in mindset. CEOs need to challenge every major tech investment by asking, “What if we had to realize twice the value in half the time?” This question has the power to change your dialogue about these massive investments and can keep you from falling into the trap of typical large-scale platform implementation programs that last multiple years, cost huge amounts of money, require massive effort to get employees to adopt new ways of working — and ultimately don’t help you differentiate and win.

The key to realizing twice the value in half the time is to not focus primarily on technology, but to have an obsessive focus on the outcomes the technology is supposed to enable. This may sound like semantics and you may think, “Of course, we’re interested in the outcome, and not the technology per se.” But are these outcomes defined in support of a very clear value promise to the market? And will they create massive incremental value and differentiate your company? Most often, the answer is no.

So how do you shape your technology agenda so it enables you to build the right capabilities and deliver outcomes that fuel your competitive advantage? Consider, for example, how the Spanish multinational clothing company Inditex uses technology to create unique outcomes and differentiate in a hyper-competitive market.

Inditex’s Zara has been known for its “fit to demand” business model, ensuring that stores have the right amount of the right inventory that will sell at the right time. Like its competitors, Inditex had invested in an enterprise supply chain management system and was confronted with investing even more in new technologies to stay ahead. However, to fully deliver on the outcome of a “fit to demand” model, Inditex deployed a new take on an old technology — embedding a cheaper, recyclable RFID chip in the tag of every item Zara sells. This tag allows individual tracking of garments from the logistics platforms until their ultimate sale, enabling a much more intelligent system.

But Inditex does not rely on the technology alone. Information from RFIDs is complemented by insights from store managers into why certain items didn’t perform well on certain days, as well as from salespeople who’ve been trained to engage with customers and give feedback about what they’ve learned to designers. This combined tech and non-tech intelligence allows Inditex to work in a highly integrated manner across marketing, design, merchandising, supply chain, and retail operations to uncover fashion trends, create new waves of collections, and get customers their desired garments much faster than the competition.

Building on this learning from Inditex and other companies, here are six imperatives to consider to deliver differentiated results from your own tech investments:

1) Connect the technology to clear, differentiated customer outcomes

Ask yourself: What is the unique value our company creates for customers and stakeholders? What are the few things we need to be great at to deliver that value? How can technology help us excel at those differentiating capabilities? Can we clearly articulate and measure how technology will help us differentiate vs. our competitors? Having clear answers to these questions will help you prioritize outcomes and technologies that advance your unique value proposition vs. incrementally digitizing how you work today.

2) Balance your investments across big tech, small tech, and no tech

Not every problem needs a big tech solution. Often, the solution requires complementing big technology platforms with simpler “small tech” automation and processes, new policies, and behavior changes. This does not mean only launching a slew of small tech pilots and delaying fundamental investments that may be needed for long-term value. The key is to have a portfolio of solutions that delivers outcomes faster wherever possible and that funds and supports the investments that require larger transformation.

3) Be very choosy about where to innovate vs. integrate

Creating amazing outcomes for customers doesn’t always mean you have to do everything in-house or have your own unique customer solution. The opportunities for innovation via ecosystems are rapidly growing as companies bring new technology capabilities to market every day. Don’t be afraid to integrate technologies offered by others, in particular from your ecosystem partners. Customize and innovate only where it leads to true competitive differentiation — and where that differentiation is something your customers are willing to pay for. If you cannot honestly answer whether customers will be willing to pay for the investments you are making in customization, don’t do it.

4) Align your operating model to achieve your desired outcome

Delivering the desired outcome will require much more than just system implementation. It will take changes in roles, processes, policies, ways of working, skills, metrics, incentives, behaviors, data, and more. You will most probably find that without a multi-disciplinary team that shapes the outcomes targeted by your technology investments, you won’t be able to capture the full value. We call these “outcome-oriented teams,” because they bring together the right skills and talent from anywhere in the organization and focus on clear deliverables that drive customer value. Increasingly, they need to be permanent, not just formed with part-timers working together for the duration of the project. This is a significant re-wiring of your operating model to break down the traditional silos that often stand in the way of achieving differentiating outcomes.

5) Change the relationship between technology and your people

Engaging people who will use the new technology and upskilling them will be one of your most challenging and time-consuming tasks — but one that is absolutely critical. Don’t just focus on making people comfortable with using the technology (e.g., teaching them how to use remote working tools effectively); get them excited about working in this new way (e.g., being comfortable managing and motivating their teams remotely). Work with people to change their daily activities with technology, and in the course of doing so, they will get familiar with the underlying systems. Show them what’s in it for them — how this will enrich their jobs and allow them to connect to the organization’s purpose.

6) Rethink the business case behind tech investments

Business cases typically focus almost entirely on efficiency improvements — e.g., headcount savings from performing tasks faster or with less human intervention, or reductions of the technology cost itself. Be more ambitious. How will the investment change success in customer acquisition or retention? How will it improve your insights and help you better deliver your value proposition? What will it do to your carbon footprint? If your business case doesn’t address outcomes, the project itself is likely not transformational enough.

As you broaden the articulation of benefits, you will also need to hold your teams accountable for delivering that value. No longer should success be measured by whether the system “goes live,” but by whether it drives a change in your outcomes with customers. Defining clear fact-based measures isn’t easy, but without them, you’re basically just crossing your fingers and hoping that transformation will come on the back end of massive technology bets.

Thoroughly working through these six areas before you engage in any large tech project will help you focus your investment on the outcomes that matter most, raise the return on these investments, and connect technology directly into the center of your differentiated future.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Digital Trasformation and business modeling

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 29 Dec 2021 16:35:19 GMT

How Digital Trasformation change the way we create value

Digital transformation is about changing where value is created, and how your business model is structured. More and more, value creation comes from outside the firm not inside, and from external partners rather than internal employees. Tthis new production model is called the “inverted firm,” a change in organizational structure that affects not only the technology but also the managerial governance that attends it. Executives must understand and undertake partner relationship management, partner data management, partner product management, platform governance, and platform strategy. They must learn how to motivate people they don’t know to share ideas they don’t have.

Since at least the 1980s , firms have engaged in digital transformations by coordinating, automating, and outsourcing productive activity. Client server architectures replaced mainframes, remaking supply chains and fostering decentralization. Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems automated back office and front office processes. Shifts to cloud and SaaS have changed software evolution and the economics of renting versus owning. Machine learning and artificial intelligence uncover patterns that drive new products and services. During the Covid-19 pandemic, virtual interactions replaced physical interactions out of sheer necessity.

Some of these changes were as straightforward as converting processes from analog to digital. In other cases, companies changed how they worked or what they did.

Yet, amidst all this transformation, something novel — and perhaps fundamental — has changed: where and how companies create value has shifted . More and more, value creation comes from outside the firm not inside, and from external partners rather than internal employees. We call this new production model an “ inverted firm ,” a change in organizational structure that affects not only the technology but also the managerial governance that attends it.

The most obvious examples of this trend are the platform firms Google, Apple, Facebook, Amazon, and Microsoft. They have managed to achieve scale economies in revenues per employee that would put the hyperscalers of the 19th and early 20th centuries to shame. Facebook and Google do not author the posts or web pages they deliver. Apple, Microsoft, and Google do not write the vast majority of apps in their ecosystems. Alibaba and Amazon never purchase or make an even vaster number of the items they sell. Smaller firms, modeled on platforms, show this same pattern. Sampling from the Forbes Global 2000 , platform firms compared to industry controls had much higher market values ($21,726 M vs. $8,243 M), much higher margins (21% vs. 12%), but only half the employees (9,872 vs. 19,000).

In the past, high revenues per employee gave evidence of highly automated or capital intensive operations such as refining, oil exploration, and chip making. Indeed, automation allowed Vodafone to reduce headcount for managing 3 million invoices per year from over 1,000 fulltime employees to only 400. But this time transformation is different. Inverted firms are achieving far higher market capitalization per employee not by automating or by shifting labor to capital but by coordinating external value creation.

The highest value digital transformation comes from firm inversion, that is, moving from value the firm alone creates to value it helps orchestrate. Cultivating a successful platform means providing the tools and the market to help partners grow. By contrast, incumbents typically use digital transformation to improve the efficiency of their current operations. New revenue projections typically focus on value capture. Of course, digital transformation can and should support operating efficiency, and this often comes first, but it cannot stop there. Digital investments must set the firm up to partner with users, developers, and merchants, at scale, with a focus on value creation, which is the foundation of firm inversion. Unconstrained by the resources the firm alone controls, inverted firms harness and orchestrate resources that others control.

How Inverted Firms Create Value

The most compelling evidence in favor of digital transformation as firm inversion comes from a recent study of 179 firm s that adopted application programming interfaces (APIs). As interface technology, APIs allow firms to modularize their systems to facilitate replacement and upgrades. APIs also serve as “permissioning” technology that grants outsiders carefully metered access to internal resources. These functions not only allow a firm to quickly reconfigure systems in response to problems and opportunities but also allow outsiders to build on top of the firm’s digital real estate. Researchers classified firms based on whether API adopters used them for internal capital adjustment, the upgrades and opportunities the firm pursued itself, or used APIs for externally facing platform business models that allow developers and other partners to create their own upgrades and opportunities.

The difference in results between these two approaches is striking. Measured in terms of increased market capitalization, gains for firms that took the internal efficiency route were inconclusive. By contrast, firms that took the external platform route, becoming inverted firms, grew an average of 38% over sixteen years. Digital transformation of the latter type drove huge increases in value.

Inverted firms rely heavily on engagement from their external contributors. This strategy relies on partners the firm does not know volunteering ideas the firm does not have — a very different process than outsourcing, where the firm knows what it wants and contracts with the best known supplier. For firm inversion to work, others must join the ecosystem, otherwise it’s about as useful as hosting a potluck where nobody comes. Good management is what gets you RSVPs and guests creating good things to share. How new guests are rewarded, what resources they are given, and the willingness of the firm to help create that value can determine whether previously unknown partners choose to add value. This requires a different managerial mindset, from controlling to enabling, and from capturing to rewarding. The more that a firm can coax partners to volunteer investments, ideas, and effort, the more this external ecosystem thrives.

To attract partners, these inverted firms follow one simple rule: “Create more value than you take.” A little reflection shows the rule’s potency. People happily volunteer investments in time, ideas, resources, and market expansion when they get value in return. Partners flock to a firm that makes them more valuable, which in turn helps the firm’s ecosystem flourish. By contrast, a firm that takes more value than it creates drives people away. Why should they cook in a kitchen where the head chef keeps all the sales or build on digital real estate where the landlord takes all the rent? Such ecosystems wither.

Good platform husbandry means taking no more than 30% of the value and it can be far less . Too many product firms start from the bad habit of asking “How do we make money” when instead they should start by asking “How do we create value?” and “How do we help others create value?” Only by creating value is one entitled to make money.

The New Rules of Creating Value

Firm value used to be tied to tangible assets but that is no longer the case. IP valuation firm Oceantomo has documented a 30 year trend of shifting firm values from tangible to intangible assets. As of their 2020 accounting, intangible assets made up 90% of the valuation of S&P 500 firms. Of course, intangible assets cover a broad range of things including the value of brand, intellectual property, and goodwill. Those assets, however, have been known long before the 1980s.

Among inverted firms, the network effects that arise when partners create value for one another are a major source of growth in intangible assets. Adding the ability to coordinate value creation and exchange — from user to user, partner to partner, and partner to user — is one way that traditional firms transform. It also provides means to scale. Transforming atoms to bits improves margins and reach. Transforming from inside to outside magnifies ideas and resources.

To be sure, firm inversion entails risks of outside interference and partner negligence. If partners are part of the value proposition, then a brand can suffer when that proposition fails. Someone rented a host’s home on Airbnb only to discover it had no shower or bathroom, a fact carefully omitted from the service description. Airbnb quickly stepped in to discipline the host and provide the renter with a nicer no cost accommodation. Relying on third party producers entails also having strong quality curation of partner offerings and a rapid ability to swap in one’s own or a different partner’s offering. Furthermore, those that expose their data and systems to outsiders can face increased risk of cyberattack . This means taking responsibility and being a good steward of others’ data. Getting data means giving value back and protecting those who share it. On balance, those firms that understand and mitigate these risks significantly outperform the firms that stay closed and avoid the upside while avoiding the downside.

Creating the inverted firm has a number of important implications. Most important is that there are likely multiple holes in an organization’s skill set regarding orchestration of third party value. Adopting digital technology alone will not transform an internal organizational structure to one that functions externally. Executives must understand and undertake partner relationship management, partner data management, partner product management, platform governance, and platform strategy. They must learn how to motivate people they don’t know to share ideas they don’t have. Firms as diverse as Barclays Bank, Nike, John Deere, Ambev, Siemens, and Albertsons have listed 200,000 job openings for these platform functions and to run their increasingly inverted firms. Indeed, firms that look only inward will be the ones that fail to move upward.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The strategic impact of Co-opetition

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 22 Dec 2021 17:35:28 GMT

The competitive landscape is full of examples of cooperation among competitors, even in less predictable context

Jio Platforms’ unique experiment of co-opetition with global tech giants combined with its local prowess allows it to address an enormous market of price-sensitive customers. Thanks to its sheer scale, innovation, and unique collaborations, it can offer integrated solutions for retail, grocery, fintech, medical, agricultural, e-commerce, and e-payment needs, in addition to telecom services and home entertainment — at affordable prices. The resulting knowledge, access, business possibilities, and even entertainment opportunities could transform the lives of over one billion people in India.

More than a traditional telecom business, India’s Jio Platforms is proving to be a disruptor. Jio was launched as a “freemium” service, offering free internet services to price-sensitive Indian customers to increase the adoption rate and scale up the market. Previously, Indian customers, whose average income is about $150 per month , had never had access to such a high-speed internet — bundled with so many apps and digital solutions — at such low prices.

Soon after its launch, Jio Platforms managed to capture over 426 million subscribers to become the top telecom operator in India. It now ranks as the third-largest telecom network in the world by subscription numbers. It attracted investments and technological partnerships from tech giants such as Intel , Qualcomm , Meta (Facebook) , and Google , creating a unique model of co-opetition — cooperation among competitors.

Why has Jio been so successful, and what lessons can we learn from its evolution?

It adds innovative value to the telecom ecosystem.

In a previous article we described how Zomato, a food delivery company, could transform a whole industry by leveraging existing technologies. Similarly, Jio didn’t invent any new technology. Instead, it created a blend of high-speed internet connection and a comprehensive ecosystem of digital technologies that were previously unavailable to most customers in India. It epitomizes a modern technological disruptor that creates unique value for a segment of extremely price-sensitive customers.

Jio offers 4G telecom services and a network of apps at about $15 per month . An affordable solution, tailormade for the Indian market on a massive scale, could potentially improve the daily lives of the average person, most of whom don’t have access to a computer . Jio can offer them improved access to banks, commerce, suppliers, and merchants. A micro-entrepreneur can improve coordination among employees and supply chain partners while connecting with other micro-entrepreneurs and customers on JioMart , the company’s retail network. The resulting network, improved coordination, and seamless information transfer among parties can reduce inefficiencies, wastage, and exploitation that often results from a lack of knowledge and communications in less-developed areas. Those improvements can also create what economists call a “ producer and consumer surplus,” which has value for the whole society.

This unparalleled scaling also presents a great business opportunity. Jio Platforms has raised $20 billion from digital giants like Google and Meta (Facebook) and leading technology innovators like Qualcomm and Intel. These giants, in turn, provide their services using Jio Platforms at prices suitable for the mass market in India. This gives Jio Platforms a total equity valuation of $72 billion , which is rumored to have increased to about $110 billion .

It leverages a partner ecosystem.

Jio’s chairman, Mukesh Ambani, believes “ data is the new oil .” Securing, managing, and sharing data are key to creating the network effects of the telecom sector. Each new engagement with a customer not only improves revenues, but it also leads to additional data, enhancing the momentum of the “ data flywheel .” Jio Platforms, having already secured over 426 million customers , keeps adding services to enhance its touch points by leveraging its ecosystem of partners.

Jio Platform’s diverse partnerships provide entertainment, payment, health, communication, and e-commerce apps . For example, Jio has partnerships with online content streaming platforms like Netflix, Amazon, Disney+ Hot Star, and Sony, bringing consumers entertainment content that wasn’t available previously, in a single platform and at affordable prices. JioMoney inserts itself between customers, banks, MasterCard and Visa cards, and merchants for all payments , improving micro credit availability and reducing costs in the financial system. Additionally, Jio enables other players, such as car manufacturers, to connect to customers through customized apps.

It engages in co-opetition.

Jio Platforms is an example of co-opetition , where seemingly rival companies collaborate on common goals. Tech titans seek partnership with Jio due to its futuristic technologies (4G now, but 5G in the near future) and unique ecosystem. Jio’s fiber optic-based cable network can facilitate Microsoft’s Xcloud cloud-based gaming console ; Google offerings, including YouTube and Google Maps ; and Facebook’s WhatsApp Pay — and reach a billion people in India.

A partnership with Microsoft promises to bring cloud technology and infrastructure to millions of micro, small, and medium-sized businesses in India while establishing cloud data centers to cater to their needs. Integration with WhatsApp Pay can provide digitally enabled connectivity between merchants and customers. While Jio’s basic 4G handsets are priced at $20 for the ultra-price-sensitive segment, Google’s partnership has enabled building a next-generation smartphone for Indian consumers. Priced around $90 , it comes with Android OS and other preloaded Google apps and can read and voice assist in 10 Indian languages. Partnerships with Intel and Qualcomm aim to enable the development, launch, and expansion of 5G services.

Such partnerships involving global leaders are often complementary and synergistic but can also be challenging to manage . For example, there’s the question of how revenues from payment systems will be divided among the players, all of which want a share of the pie.

It provides a unique digital experience through intermediation.

With its reach and technological capabilities, Jio Platforms can encourage new-age entrepreneurs to create solutions for the masses and sell them via Jio Stores . Many such apps have already been launched. JioHealthHub , for example, can enable people to access quality healthcare and online consultations, while hospitals can use it to offer outpatient and home quarantine services during emergencies and securely store medical records. Embibe is an AI-based EdTech platform for interactive learning, used in partnership with local governments to launch in schools. JioMeet is a cost-effective and secure platform for conferencing and meeting, and JioGlass is connecting teachers and students through 3D virtual classrooms and holographic images. Music streaming app JioSaavn provides music in partnership with Sony Music, T-Series, Tips, YRF, Saregama, Eros, and Warner Music. JioGames encourages developers to make games and distribute them over Jio Platforms. JioDevelopers provides a software kit and infrastructure to develop and build apps.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Open Strategy

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 04 Nov 2021 08:54:35 GMT

How to balance openness and secrecy in the decision making process.

Someone said, "the commons creates the value, the market captures the value. Opening creates value, enclosing captures value". But it's important to have a clear method about how to balance openness and secrecy when trying to apply the open innovation mindset to the strategic level.

Open innovation methods can also be applied at a more general and strategic level — and this article presents a framework for doing just that. A simple insight sits at its heart: strategy is developed in three distinct phases, each of them requiring a different solution to get the balance between openness and secrecy right. Depending on whether a company wants to open up in order to determine the direction it wants to take (idea generation phase), work out the exact details of their strategy (formulation phase), or mobilize staff around its strategy (execution phase) strategy making will involve different numbers and types of people, and requires different amounts and type of data. By using the framework, strategy-makers can benefit from outside perspectives while maintaining appropriate levels of secrecy.

Kurt Matzler and Christian Stadler have tried to developed an holistic framework to address this issue. In 2014, topcoder.com, a platform for crowdsourcing digital talent on a project basis, asked for technical advice on how to glean useful data from bison photography. Participant were asked to develop an algorithm to use structured and unstructured data such as time stamp or GPS from photos posted online to develop procedures that helped to pin-point the migration patterns of bison herds.

Although this looked like a contest set up by conservationists, it was actually a ploy by the U.S. Intelligence Community (IC) to tap the wisdom of the crowd to help with a very different kind of problem: tracking Russian military personnel operating in the contested territory of Crimea, as well as the everyday movements of hostile military vehicles across borders.

If the IC had posed a challenge to coders directly, they would have attracted unwanted attention. Russian spies might have sabotaged the contest, for instance, by filing submission after submission with smart-looking but useless algorithms. Or they might have tried to file the winning algorithm, and then used their inside knowledge to adjust their movements on the ground to avoid being tracked. By framing their challenge as an exercise in nature conservation, the IC could get the knowledge it needed undetected.

The IC experience clearly demonstrates that organizations can use open methods to leverage diverse perspectives from a large number of participants from both inside and outside their organizations for very specific questions of strategic relevance, while retaining partial or even total secrecy. But open methods can also be applied at a more general and strategic level – and Matzler have developed a framework for doing just that. By using the framework, strategy-makers can benefit from outside perspectives while maintaining appropriate levels of secrecy.

Identify where you are in your strategy-making

A simple insight sits at the heart of our framework: strategy is developed in three distinct phases, each of them requiring a different solution to get the balance between openness and secrecy right. Depending on whether a company wants to open up in order to determine the direction it wants to take (idea generation phase), work out the exact details of their strategy (formulation phase), or mobilize staff around its strategy (execution phase), its strategy-making will involve different numbers and types of people, and requires different amounts and type of data.

1) Idea Generation

In general, companies benefit most from including a large crowd of very diverse external participants in the first stages of strategy-making. Partly because this offers new thinking but even more importantly as it legitimizes a new idea, thereby helping executives to unite around a particular direction. Happily, participants during idea generation don’t need very much company-specific information to get the ideas flowing, making secrecy easiest to maintain during this phase.The U.S. Navy for example set up a multiplayer online game to develop ideas for a new aviation strategy. Six hundred participants — including industry partners, academics, and others not part of the Navy — posted and voted on over 5,000 ideas.

The Navy relied on four crucial elements to exert considerable control. First, they previously experimented with openness in a limited, low-risk way, e.g., in a game asking how to respond to the Somali piracy issue. This helped them to understand how open strategy worked and to build a robust, leak-proof system. Second, they could rely on a culture of confidentiality — which every organization must build in the age of social media — to enforce secrecy. Third, they sought legal counsel to ensure that no issues will emerge on this front. And fourth, they were aware that in order to generate new ideas it was not necessary to share what they had already done and what they are planning to do in future.

After the exercise they distributed a report that identified 127 ideas. This satisfied the curiosity of participants without telling what the Navy would actually do next.

2) Formulation

During the second phase (strategy formulation), openness requires you to release a substantial amount of information to get meaningful and valuable outcomes. As a result, most companies will want to involve primarily their own staff or select a smaller group of people from outside the organization who sign non-disclosure agreements (NDAs). They are hand-picked. In our own experience it is most beneficial to match equal numbers of employees and outsiders.The European axle systems and brake technologies company BWP illustrates how this can be done. The €1.5 billion business knew that digitalization presents a considerable challenge. Previous attempts to create digital businesses had produced subpar results. It was less of a technology issue than an inability to part with old mental models. When the company came up with a new technology that would enable lorry drivers to continuously monitor the forces on a freight-securing belt (a big market considering annual insurance claims of €1.2 billion for damaged freight in Germany), it opted for an open strategy approach.

As details of the technology needed to be shared, they used a workshop model that included a diverse but selected set of internal and external participants. Over the course of three days several competing business models were generated and refined across rounds of judging, ending with a final presentation for senior leaders. Initial customer responses to early trials of the new product — named i.Gurt — have been highly positive. Armed with a business model significantly different from the one driving BWP’s core business, the company is expecting a return on its investments in fewer than three years.

Execution

During the third phase of strategy-making companies have to share company-specific knowledge in order to glean the main benefits, understanding of the strategy and buy-in from those who implement it. This sharing makes secrecy difficult. Fortunately, the benefits of openness here far outweigh the dangers. Although competitors might learn your plans, they are at this stage committed to their own. Secrecy is also less of an issue as the main participants here should be employees and business partners rather than outsiders.Telefónica has used an internal social network tool to mobilize its workforce around strategic initiatives for several years. Scaling the use of this tool gradually, allowed them to find an appropriate way to share the right amount of information. Secrecy has actually never been too much of a concern, as the main goal has always been to make strategy actionable.

The top management team first used social media to discuss strategy with 1,300 senior leaders prior to the bi-annual strategy days in 2013. This helped to de-mystify the event and give a larger group a voice. A couple of years later all 125,000 employees were invited to join and today strategy discussions are ongoing with on average 42,000 interactions every day. Mobilization and the ability to fine-tune has become part of the company’s fabric. The real competitive advantage stems not from a few highly guarded ideas but the permanent calibration in the operational front-line.

Choose the right tools for engaging with outside participants

As shown in “The Open Strategy Decision Tree” graphic above, the team have developed a framework to help managers determine for each of the three phases of strategy (1) whether participants should come exclusively from inside the company or should also include externals and (2) whether they should recruit large groups of randomly selected or self-selecting people or target small numbers of hand-picked individuals. In each case, htey identify the appropriate open strategy mechanisms for engaging with external participants, which include:

1) Competitions

Online contests typically prove most productive for companies when the strategic challenges are complex or novel, when established best-practice approaches don’t exist, and when companies don’t know which precise combinations of skills or areas of expertise are required to succeed. As the amount of information you have to divulge in the ideation phase is limited, it is best to opt for a large crowd. This increases the chances of spotting anomalies and lends legitimacy to new ideas, facilitating collective decision making. To keep up engagement it makes sense to gamify the contest as the Navy did when participants received votes and comments as a form of micro-incentives. The analytical capabilities of many of these platforms will help you to identify trends as well as anomalies.

2) Trend radar

Drawing on the wisdom of the crowd, the Trend Radar process allows you to derive a shared understanding of emerging developments. It’s particularly useful if you are looking for new ideas which require you to share some sensitive insider-knowledge, as the tool has been designed with the intention to achieve maximum diversity with relatively low numbers of selected participants. For example, Stadler worked with a large industrial company, who brought together 94 of its employees with 95 participants from 12 organizations. They developed 159 hypotheses about the future, asking a larger crowd to vote on them before grouping them into larger trends. These were not necessarily new, but as a senior executive explained, that was never the goal anyway. The real value was to create a shared understanding of the challenges these trends pose, and agreement on what to do next.

3) Business Logic Contest

When making strategic decisions, leaders typically focus on where to compete (which industries and markets), pushing off consideration of competences, resources, and value chains they’ll need to the implementation phase. The Business Logic Contest helps leaders to think about the “what” and “how” of strategy together — to explicitly integrate the customer interface related to value creation with value capture and its associated process. This ensures that a company collectively engages in developing a unique proposition that works coherently for them. As described in the BPW example above, a workshop type model brings together a relatively small but diverse set of people preventing leakage of proprietary information.

4) Employee Social Network

To mobilize the workforce, large crowds need to be involved. Internal social networks can be used effectively here as the Telefónica example demonstrated. A number of simple actions help to create momentum: Emphasize the social element, establish clear rules to reduce inappropriate behavior, communicate what exactly will happen with contributions, and most importantly, have strong moderators. Leaders also have to take a personal interest. When they reference comments, employee participation, acceptance and satisfaction rise substantially.

5) Jams

Remember that famous Peter Drucker slogan, “Culture eats strategy for breakfast”? Strategy only has a chance, Drucker pointedly suggests, if people actively buy-in and champion it. An online event called a strategy jam helps ensure that they do, providing an opportunity for large groups of people to engage in moderated strategy discussions. Barclays’ UK retail arm, for example used the approach in 2012, knowing that banking would become more digital. Involving the unit’s 30,000 employees in a jam meant that they could all understand what the shift could mean for them. But even though jams are primarily used to mobilize, it can have effects on the detailed direction: shortly after the jam, Barclays launched its first mobile app which today is one of the UK’s most successful fintech products.

When confronted with the risks of open strategy it is easy for executives to hold back. As the wisdom of the crowd changes engagement with customers, innovation, and many other corporate domains, it’s becoming clear how much companies can gain. With the process we’ve outlined, executives no longer have to be afraid to open up the C-suite.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Business model design and prioritization

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 12 Oct 2021 07:20:21 GMT

What Business Are We In? There are two possible types of answers according to two different perspectives

The customer-centric approach has proved to be more powerful than expected in driving business designing and supporting strategic and operative decisions in a more invasive way.

Twitter is a great example: at the outset of a conference call with securities analysts in July to discuss Twitter’s second-quarter earnings, CEO Jack Dorsey laid out his company’s strategy: “We intend to build an ecosystem of connected features and services focused on serving three core jobs: news, which is what’s happening; discussion, conversation; and helping people get paid,” he said.

The language Dorsey used — “three core jobs” — refers to a concept called “ jobs to be done ,” which is an approach to defining a business from the perspective of what really matters to its customers.

Such strategic focus is essential for a company such as Twitter — which competes against a varied set of players ranging from Facebook and TikTok to the Wall Street Journal and the New York Times. And Twitter’s results have been impressive. Its daily active user count has grown above 200 million, up from 126 million at year-end 2018. It is on pace to cross $4 billion in annual revenue in 2021, up from a little more than $3 billion in 2018. And as of September 20, Twitter shares had appreciated over the past five years by 170%, which compares to 185% for Facebook.

It wasn’t always this way for Twitter. As it turns out, jobs to be done has been a key tool in Twitter’s turnaround, which began in earnest after Jack Dorsey reassumed the CEO role in October 2015. At the time, the company was still evolving and its focus was unclear. Facebook was growing users at an astounding scale and pace, and Snapchat was the shiny, new social network set to take it on. In 2015, Twitter posted a net loss of $521 million on revenue of $2.2 billion, and its shares were trading below its IPO price of $26. As Dorsey described it later, at the Innosight CEO Summit in August 2019, “We got overly reactive to everything our peers were doing. We didn’t have a clear sense of what our purpose was, and that really hurt us a lot.” (Disclosure: Twitter has not been a client of our firm, Innosight, and he was an unpaid speaker at the 2019 CEO Summit.)

Dorsey and his team were grappling with one of the most profound questions every leader must answer: What business are we really in?

How an organization defines its business impacts nearly everything it does: what customers it serves, how it serves them, whom it competes against, what external forces it regards as relevant, how it interprets those forces, what strategies it contemplates and pursues, and how it innovates.

Yet too often this question is answered in ways that are overly constraining, myopic, and obscure what’s really happening in the world around them. Even worse, the wrong frame of reference can result in completely missing emerging threats and opportunities. This often happens when an organization defines itself by the products it sells (“we are in the insurance business” or “we are in the car business”) or by some other inward-looking characteristic such as its business model (“we are an online marketplace” or “we are in the rental business”) or a capability (“we are a software development business”).

What’s needed is a way to define a business from the perspective of what’s at the heart of any company’s success: its ability to create value for customers. The “jobs to be done” approach provides such a solution.

A job can be a problem to solve (e.g., “repair my car,” “soothe my sore throat”) or a goal (e.g., “run a marathon,” “get into college”). When jobs arise, people are motivated to seek products, services, or experiences to “hire” to perform those jobs, just like they might hire a person to do a job such as fixing a leaky pipe or babysitting the kids.

The idea can also be used to define strategic direction — and has particular value as a way of framing an organization’s identity.

How Twitter Applied the Approach

For Dorsey, jobs to be done provided a tool for strategic clarity at a critical time. “It cleared something up that was missing for me, which was how do we plan and build around a customer-centric framework that would focus the organization on why our customers are coming to us in the first place,” he said. Upon his 2015 return to Twitter, Dorsey launched an effort to identify the jobs that people hired Twitter for and, importantly, which jobs it would focus on going forward. It involved three steps.

Understand the full set of jobs for which Twitter was already being “hired.” The company obtained this information by gathering insights from customer interviews, observations of how people engaged with Twitter, and data gathered from website usage.This generated a long list of quite specific jobs, which could then be aggregated into higher-level jobs to consider for strategic prioritization. For example, jobs such as “get real-time updates about a live sporting event,” “learn more about a longstanding interest or hobby,” or “be alerted to breaking news events” could all be grouped into the higher-level of job of “inform me.”
Prioritize the jobs Twitter wanted to focus on. This was done via workshops in which company leaders evaluated the list of jobs by criteria such as how widely they were shared by customers, the expected value of solving them, and where Twitter had a compelling and differentiated solution. The result was alignment around three priority jobs for Twitter consumers: “inform me,” “have a conversation,” and “inform others.” Similar work was done to identify the priority jobs of other Twitter stakeholders such as advertisers and the developer community.
Communicate the results and use them to allocate resources. Ultimately, priority jobs were communicated throughout the company. They also provided a powerful mechanism for focusing how resources were allocated. For example, once the strategic jobs were identified, all product groups and teams reviewed their existing product roadmaps and assessed, for each project, how it connected to one of the priority jobs. If it couldn’t be connected, the product was taken off the roadmap. The jobs framework also helped move the organization from describing its strategy in terms of features to developing longer-term plans aimed at solving priority jobs to be done.The framework was also applied at Dorsey’s other company, Square , to help the financial services and mobile payments company redefine its business and figure out where it might look for growth. The initial approach it took was similar to that of Twitter: The company gleaned insights into the jobs to be done from customer interviews and observations of how business owners used Square solutions. For instance, Square’s managers realized that its technology, which enabled small businesses to process credit cards, was a means to solving the broader job of “grow my business.”

That realization opened up the team’s eyes to other opportunities. For example, growth requires access to capital, and many small businesses have difficulty obtaining bank loans. Square responded by launching Square Capital (now called Square Loans ), which leverages its deep understanding of its customers’ financial health (derived from their transaction history) to provide them with direct access to capital.

The Value of a Jobs Perspective

Defining an organization from the perspective of the customer jobs it exists to solve often leads to valuable new insights about the competitive environment, risks, and opportunities, while expanding the possibilities for growth and innovation. To illustrate this, consider the contrasting implications of a car company defining its business from a product perspective (“we are in the car business”) versus a jobs perspective (“we are in the personal mobility business”).

What Business Are We In? Two Perspectives

Viewing a market through the “jobs to be done” lens produces a much broader picture of the competitive landscape.

Clearly, these two perspectives lead to very different conclusions about what’s happening in the world, what developments merit attention, and what the strategic options are.

Beyond the clarity it provides, there are three primary benefits of the jobs-to-be-done approach:

Expanding options for strategy and innovation. Jobs provides a customer-centric lens for strategy development, clarifies who the real competition is, and opens up a range of new avenues for growth and innovation.
Investing in what matters most. When everyone in the organization understands and is oriented towards the jobs it exists to solve, employees at all levels are empowered to make better decisions in their day-to-day tasks.
Aligning and inspiring the organization. Clarifying the priority jobs your firm exists to solve helps all employees understand how the organization creates value in the world and realizes its purpose. It also plays an important alignment role by communicating strategic focus and ensuring this is a shared language and methodology for innovation, product development, and developing customer insights.All organizations should reflect on the customer jobs they’ve solved up to the current moment and determine how this focus might need to change in the future. By doing so, they can can chart a clear path forward.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Creating value in an unpredictable environment

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 29 Sep 2021 08:19:57 GMT

Reacting to change is not the answer.

It's impossible to have, by definition, an all-encompassing framework to navigate the constant change or, in other words "a one best way", nontheless it's possible to collect some hints for setting up a "future-proof" organization with the right DNA. I want to highligth that It's not about having a plan, it's about the importance of planning, it's not about predicting the future, it's all about having the right method to face the constant change. It's about be ready to survive and grow in the uncertainty.

Let's think to Verdadera Destreza, the spanish traditional way of fencing: it's not just swordsmanship, it's a universal way of fighting and winning, no matter what weapons you are using. It's based on filosofical, mathematical, intellectual ideas. It is thought mainly like a mindset a collection of principles and values for fighting no matter what are the conditions.

All we knew, even before Covid-19, that the future is defined by more uncertainty, more unpredictability, and more unknowns. Individually, we wonder (and often worry) about our jobs, our well-being, and our children’s future. Organizationally, we grapple with business model disruption, digital transformation, and the Great Resignation. Societally, we face unprecedented changes to our climate, economies, demographics, and political systems (to name but a few). These changes and their effects will multiply and intersect.

According to April Rinne "As a futurist, I spend much of my time helping companies, executives, and teams make sense of the forces shaping the future and prepare responsibly. The objective is not to predict the future (which is a futile quest), but rather to be ready for many different possible futures that could unfold. In this role, working and traveling across over 100 countries for more than 25 years, I’ve seen time and again how every organization struggles with change in different ways. However, there is hope for organizations that plan in order to get ahead of change."

The time to prepare for change is not when it hits. It’s before it hits, and during times of relative calm. Reacting to change in the moment keeps you forever on the defensive, and the consequences can be severe. You’re unable to see where the future is heading because your attention is consumed with dodging the next curveball. This exposes your organization to unnecessary risks and overlooks new opportunities. It’s a recipe for frustration and lagging performance at best — collapse at worst.

While finding “the right time” to prepare for change can be difficult, there are myriad ways to get started. Here are four steps leaders can take to prepare their organizations to thrive amid constant change.

Conduct a “change audit”

Holistically assessing your organization’s readiness for a world in constant flux provides the foundation for a future whose only steady state is more change, yet few leaders do it consistently. A change audit seeks to provide clarity on multiple levels.

First, where is change hitting hardest in your organization, industry, team, and customers’ lives? It’s easy to silo changes into specific departments or functions, but this often misses key dynamics and interdependencies that can make change easier to gauge moving forward. Get clear on which departments or functions are consistently more change-ready than others: Who has excelled over the past 18 months, and why?

Second, what kinds of changes are most challenging? Humans tend to love changes we opt into (a new job, relationship, or haircut) and fear or resist changes we can’t control (layoffs, a breakup, or a health scare). These dynamics often transfer into the workplace, with outsized implications.

Finally, what are your organization’s impediments to navigating change well? Common candidates include:

Team burnout and/or anxiety: It’s harder to assess uncertainty when we’re exhausted. When we’re tired, we’re more likely to develop tunnel vision and feel anxious.
Lack of trust: When change hits, trust will get you farther than any other single resource. Consider: Who do you turn to when you don’t know what to do? To your trusted relationships. And do you trust all employees to act in the organization’s best interests and uphold its values in both work and life?
A “just deal with it” culture: Are all levels of the organization (including leadership) not only allowed but encouraged to show up fully, including when they feel vulnerable? When things don’t go as planned, is that seen as loss, or a learning opportunity?
Insufficient metrics: The ability to navigate change well goes beyond dollars and cents. For example, how much are exhaustion or trustworthiness “worth?” They don’t show up in any budget line item, yet they’re invaluable. Where and how do you account for such things? In a constantly changing world, metrics must go beyond short-term benchmarks of productivity and quarterly returns.

Ideally, a change audit includes input from all talent in an organization, from the most seasoned executive to the newest joiner. Not only does this underscore an inclusive culture, but the fact is that everyone has unique wisdom and perspective when it comes to change.

Put mindset before strategy

All too often, leaders assume that change can be “managed” and controlled, as if in a vacuum. Change management books abound and feed this narrative. But in today’s world in flux, change management is insufficient. Leaders must start with their mindset about change.

Navigating change well is both art and science. It requires the right strategy and the right mindset. If your mindset is rooted in change and you’ve become comfortable with it, then you can’t help but see every change — good or bad, big or small, expected or unwelcome — as an opportunity for growth and improvement.

Yet many leaders get these dynamics backward. Mindset drives strategy, not the other way around. Similarly, human relationships to change develop and manifest from the inside out. This is called a “flux mindset.” When leaders and employees can open a flux mindset, this attitude and enthusiasm for upgrading one’s relationship to change can become part of organizational culture. A flux mindset can show up in many ways, from how we speak about change to how strategy, policies, and talent priorities are set. Leaders play a key role in signaling that mindset matters.

Clarify and reassess who is responsible for your organization’s change-readiness

In recent years, some organizations have established the role of a chief change officer. In most cases, this is in the context of digital transformation: A change officer is tasked with overseeing a company’s shift to digital business operations, services, and online presence. At the same time, a range of other CXOs may be expected to add change to their respective portfolios and domains. CEOs, COOs, CHROs, CTOs, chief innovation officers, chief insights officers, and chief culture officers are all part “change” officers, too (though you may be hard-pressed to find a common definition among them). Worst of all, some companies appoint a chief change officer in what amounts to little more than a marketing stunt.

But in a world in flux like the one we’ll be in for the foreseeable future, the role of a chief change officer takes on new meaning and new urgency. It’s no longer defined in relation to how other roles are changing, nor is it limited to one function, department, project, or end goal.

Depending on the size of your organization, it may be time to add a chief change navigator whose cross-functional role is dedicated to helping the entire company prepare for a change-heavy future. The role’s design and mandate are guided by the principles of change and, as such, are intended to evolve over time. Required characteristics include:

Nested, not siloed: A chief change navigator has a clear link to organizational culture and acts as the connective tissue between myriad changes affecting an organization. As such, it’s nested between the C-suite, HR, chief culture officer, and board and is responsible for guiding and advising those functions.
Clear yet fluid responsibilities: The chief change navigator is akin to an in-house futurist whose role is to prepare the organization for a largely unknown future. (This includes factors that could accelerate, surprise, overwhelm, or even destroy the company but are beyond day-to-day operations.) Part of this role is to spearhead a scenario-mapping process that boosts organizational readiness for a range of possible futures and to create an in-house community with these skills.
Relentlessly talent-centric and future-forward: A chief change navigator helps all employees develop their flux mindsets and improve their relationships to change.

Embed and integrate “fluxiness” into organizational culture

At the broadest level, truly thriving in constant change means putting change at the core of what you do. It means a shift in mindset, assumptions, and expectations. Rather than feeling stressed, anxious, or unmoored when change hits, you’re ready for it. Instead of chasing an illusion of control, you have clarity about what really matters.

As “certain uncertainty” becomes the norm, there will be many ways for leaders to meaningfully upgrade their approaches to change. Updating organizational mission statements and cultural values to reflect a world in flux is a good starting point. But integrating flux into organizational culture must be rooted in actions, norms, and practices over time.

Reward employees for their plasticity when they forge new solutions and new ways of being instead of merely trying to bounce back from setbacks. Value mindset as much as management. Give more responsibility to employees who can lean into uncertainty, trust what it can teach them, and guide others toward key insights. That way, when change hits, rather than defaulting to worst-case scenarios, employees will have developed the practice of asking: What’s the best thing that could possibly happen?

We have before us a new set of opportunities — and new urgency — for navigating change well. Leaders and businesses need to radically reshape their relationship to uncertainty in order to sustain a healthy and productive outlook. As we look toward a future in which the only “steady state” is one of more change, it’s time to open your flux mindset, upgrade your organization’s “flux capacity,” and prepare to thrive in constant change.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to build the Company’s Agility

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 06 Sep 2021 08:13:42 GMT

Strategic agility is the ability to improve performance — not just survive but thrive — amid disruption. Companies that successfully navigated the Covid-19 crisis identified when to deviate from their strategic plan and adapt to the changing environment.

There are three distinct ways that companies have implemented during Covid-19 crisis to be agile (Triple As of strategic agility):

They were nimble enough to avoid the worst impacts;
When they were hit, they were robust enough to absorb a lot of the damage;
They were resilient enough to accelerate forward faster and more effectively than their peers.

For helping companies to boost their strategic agility in these three areas, Amit Joshi and Elizabeth Teracino, from Harvard Business Review, have identified 6 different principles.

In early 2020, Airbnb was headed for a banner year — bookings were up, expansion plans were in place, and an IPO was set for the spring. Then Covid hit, and more than $1B of bookings disappeared , expansion plans were postponed, and one-quarter of the workforce was cut. However, by the end of the year, revenues had recovered, and the company completed one of the most successful tech IPOs in history .

California Pizza Kitchen (CPK) is well known for its innovative offerings. It was one of the first pizza chains to offer gluten-free crusts, “take and bake” home pizzas, and iron-chef-style innovation competitions for its cooks. During the Covid crisis, it moved quickly to offer curb-side delivery and upped its online capabilities. Yet, despite its reputation for innovation and forward thinking, the company filed for bankruptcy protection in July 2020.

Why was one able to thrive while the other floundered?

As soon as it became clear that Covid-19 travel restrictions would be inevitable, Airbnb took steps to avoid impact to its business. It implemented strict disinfectant protocols for its properties and added a mandatory free night between stays to allow additional time for cleaning. It also relaxed guest cancellation policies and put measures in place to compensate hosts for lost revenue. Of course, the company couldn’t entirely avoid the effects of the pandemic, so it raised capital to bolster its ability to absorb the impact of reduced bookings and cancellations. Even before the business was stabilized, the company began to accelerate into areas that were less affected, such as in-country travel and stays at rural locations. It also started to promote longer “quarantine” stays and added details such as internet speed to its listings.

California Pizza Kitchen, by contrast, was unable to shift its core dine-in business to delivery fast enough after stay-at-home orders were issued, and thus was unable to avoid a direct revenue hit. Furthermore, years of mismanagement had left the company with a high debt load, inhibiting its ability to raise additional capital to cover its costs. With its locations either closed or operating at limited capacity, cash started to dwindle. The company entered bankruptcy protection in June 2020. After a few months of restructuring, it emerged in November 2020 owned mostly by its debt holders, who had swapped their loans for equity. The company is now trying to make up for lost time by focusing on “Cali-health” menu items like non-meat proteins (BBQ Don’t Call Me Chicken Pizza), expanding its global franchise footprint, and investing in marketing and digital channels.

The Six Principles Behind a Triple A Rating

Strategic agility is the ability to improve performance — not just survive but thrive — amid disruption. Strategic agility can be further broken down into six principles. These principles are not definitions, rules, laws, tools, or frameworks, but guidelines to help organizations leverage disruption proactively to their advantage.

1) Avoiding shocks: Speed and Flexibility

Opportunities come and go quickly during a crisis, so organizations need to be ready and willing to act quickly, even if they sacrifice quality and predictability in the process.

During the multi-day celebration of Chinese New Year, movie theaters are typically full of families. However, in January 2020, due to the spread of Covid-19, most theaters were empty, and many had closed their doors. The Huanxi Media Group (Huanxi) stood to lose millions on its New Year-themed movie Lost in Russia. While most of its peers decided to postpone their releases, Huanxi approached Bytedance, the Chinese company behind the blockbuster app TikTok. Bytedance was not an obvious distribution partner, as its properties mostly stream short-form, user-generated content. TikTok, for instance, caps videos at 15 seconds — and Lost in Russia clocked in at over 2 hours.

In just two days, Lost in Russia racked up 600 million views on Bytedance platforms. Not only did the movie gain a huge following, it also led to a flood of goodwill from Chinese citizens who were frustrated about not being able to leave their homes during the outbreak. By waiting, other studios missed out on a major opportunity to build market share and capitalize on a limited-term opportunity.

2) Prioritize flexibility over planning

Strategy is often taught in business schools as a cascade of choices around where to play and how to win. These choices are typically built into strategic plans that are devised and approved over a period of several months, and then executed over three or five years, before the cycle repeats. However, in a crisis, a strategic plan can easily become an anchor that locks an organization onto a path that is no longer relevant.

Faced with a massive drop in revenue during the pandemic, Qantas abandoned its five-year strategic plan and dusted off an old idea from the 1980s to offer “flights to nowhere.” These excursions included fly-bys of some of Australia’s main tourist destinations, such as the Great Barrier Reef and Uluhu. The entire stock of seats sold out in 10 minutes , making it the fastest-selling promotion in Qantas’ history.

Qantas was not only quick off the mark, it was flexible in how it operated. The airline recognized the public’s latent desire to travel, even if they couldn’t leave the country, and it quickly adapted its services to meet this need. It then built upon its initial success, next offering viewing flights to Antarctica .

Absorbing shocks: Empowerment and Diversification. When it’s impossible to avoid a shock, like the Covid-19 pandemic, the next best thing is to minimize the damage. This step is often misunderstood by managers. Some of the hallmarks of strong shock absorption — scale, inefficiency, or centralization — are seen as impediments to effective competition in volatile environments. Yet, when set up in the right way, these elements can enhance the ability of organizations to withstand shocks without inhibiting performance.

3) Prioritize diversification and “efficient slack” over optimization

Many organizations struggled — and some failed — during the pandemic not because they weren’t nimble or innovative, but because they were felled by a single devastating blow. The root of this problem, in many cases, was either a lack of diversification or an overemphasis on efficiency and optimization.

The principles of diversification and slack have fallen out of favor recently. The share price of diversified organizations is often hit with a “conglomerate discount,” and markets and activist investors are quick to penalize any sign of slack. Yet, these are both powerful hedges against the impact of shocks. Pain in one area can be compensated by gain elsewhere. During the pandemic, when sales in P&G’s personal care brands dropped, the company was able to make up the difference in increased revenue of its cleaning and disinfectant brands. By contrast, Gold’s Gym, Avianca Airlines, and Brooks Brothers suffered from a lack of diversification and ultimately went bankrupt.

Swiggy, one of India’s largest food-delivery startups built a platform that included more than 160,000 restaurants in 500 cities. During the Covid lockdown, restaurant activity, including deliveries, fell by more than 50%. Swiggy realized that its overdependence on fixed location, traditional “sit-down” restaurants as delivery partners was a severe vulnerability. In response, it started a program to add street food vendors to its platform, ultimately adding more than 36,000 of these vendors. While servicing these vendors was less profitable, they provided valuable “slack” during the crisis, while also delivering a societal benefit. As a consequence, the company rebounded to about 90% of its pre-Covid food delivery volumes.

4) Prioritize empowerment over hierarchy

Systems are most vulnerable at their weakest points. A hierarchy, for example, is most vulnerable at the top.

Empowered teams, by contrast, are inherently robust. Since they’re decentralized, no single strike or crisis can take them all out. The key is to maintain open and regular information flows so that they are working from the same page.

Zoetis, a leading global-health company for animals, adopted this approach during the pandemic, which arrived just as they were about to launch their largest ever new product, a medication for dogs. A number of challenges, including supply-chain disruptions, marketing delays, and reduced opening hours at testing enters and laboratories, threatened to scupper the launch. In response, Zoetis’ CEO decided to allow local leaders across 45 global markets autonomy to conduct the launch in the most appropriate way. For example, social distancing regulations varied massively by location, as did requirements to wear protective clothing. The empowerment extended to field-based employees, managers and teams who were encouraged to “run it like you own it.” To further enable these employees, a priority was placed on data-driven decision-making, and dashboards containing up-to-the-minute information on the pandemic were made available to everyone in the organization.

Accelerating away from shocks: Learning and modularity. Bouncing back from shocks is partially operational (being able to redeploy and reconfigure resources) and partially cultural (fostering a tolerance for failure and implementing an environment that encourages risk taking and rewards learning). The application of the acceleration principles has a major impact on performance in highly uncertain environments.

5) Prioritize learning over blaming

It has been well established that organizational cultures that reward risk taking and tolerate failure move more quickly that those that don’t. If people are criticized for failing, they are less likely to take risks; in a crisis, this can be fatal.

Evalueserve is a mid-sized global IT services firm with offices in India. When the country declared a strict lockdown with six hours notice, it had no choice but to shift almost all of its 3,000 employees to work-from-home. This move created an increased risk to employee wellbeing and morale, as home environments were often stressful and not conducive for working. In response, the company instituted several changes to promote a “no blame” culture. It added mental health and wellbeing initiatives such as “no agenda check-in calls” to maintain motivation, as chairperson Timo Vättö and co-founder Marc Vollenweider explained to us in an interview. The company also adjusted its incentives to reward employees for learning and adaptability. As a result, Evalueserve faced negligible attrition of both employees and clients during the period of the lockdown.

6) Prioritize resource modularity and mobility over resource lock-in

Since it is difficult to predict how the future will unfold in a crisis, it is hard to effectively plan the allocation of resources. Thus, it important to build resources that are modular and/or mobile so they can be reconfigured or moved as needed.

An example of resource modularity comes from the “Paranoid Fan” app, which allowed NFL fans to order food to be delivered to their seats in sports stadiums. But with live events curtailed by the pandemic, the app lost its users. Seeing long queues outside food banks in New York City, founder Agustin Gonzalez recognized an opportunity to reconfigure the app’s mapping and delivery technology . The company launched a new app, named Nepjun, that allowed food banks to set menus and create delivery protocols, while also allowing users to find operational food banks in their neighborhood.

Putting Strategic Agility into Action

2020 was an extremely disruptive year for the media and entertainment sector. Streaming companies like Netflix and Amazon Prime Video experienced strong growth, while organizations involved in live events and cinematic releases suffered massive drops in revenue. The Walt Disney Company was caught in the middle. In early 2020 , media and broadcasting operations accounted for about a third of its revenue, 17% was earned from direct-to-consumer brands, and the remaining 50% came from movie studios, theme parks, and product sales.

Gains in broadcasting revenues failed to offset heavy losses from the closure of movie theaters, theme parks, and retail stores. Disney’s share price began 2020 at $146, but by March 20 it had dropped to $86 a share as the global scale of pandemic became apparent. The company managed to avoid the worst impacts of the pandemic for as long as it could by keeping its theme parks open in a limited capacity and adding strong safety protocols for all facilities, staff, and guests. It saved money by laying off employees across its portfolio of stores, parks, and cruise ships, and worked with local governments where possible to supplement its income. A strong balance sheet allowed it to absorb the drop in revenue.

Meanwhile, the company reallocated resources and people to its Disney+ streaming service that had been launched in November 2019. The company worked hard to accelerate enhancements to the offering, adding new content throughout the year . For example, the live-action cinematic release Mulan was offered through the service as a special paid feature. By the end of the year, the company had attracted more than 90 million paying subscribers to the Disney+ service, significantly outperforming competitors such as HBO Max and Peacock, and far exceeding a goal it had hoped to meet by 2024 .

When conditions improved, Disney was quick to take advantage. It reopened its theme parks in Shanghai in May and Tokyo in July. Most importantly, it continued to heavily invest in Disney+, building it into one of the world’s largest video subscription services just a year after launch. It empowered local managers to make decisions as situations shifted across the world, and it moved people and resources around to focus on growing areas. Its story shows that even large companies that are in the firing line of shocks like Covid-19 can respond effectively as long as they leverage the Triple As of strategic agility.

While we will eventually see the end of the Covid crisis, there is no doubt that organizations will continue to face other challenging situations in the future. Under these circumstances, incorporating avoidance, absorption and acceleration can be the difference between survival and collapse.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Ginkgo's $15 billion promise

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 26 Aug 2021 15:10:38 GMT

The vision has been successfully sold, but no details are available

The Boston genetic engineering company Ginkgo Bioworks and its CEO, Jason Kelly, have been spectacularly successful selling a story: that synthetic biology will transform the manufacture of physical products. What computers did for information, Kelly says, biology will do for the physical world. Instead of making a chemical from petroleum, why not have Ginkgo’s multi-floor “foundry” in Boston's seaport design a yeast cell to manufacture it instead from a broth of sugar water?

Ginkgo pi tch, shared few years back by its CEO, was the same talk he’d been giving successfully in Silicon Valley for years. One slide featured a photo of an Apple computer, an iPhone, a camera, and a metal watch on a gray desk decorated with a potted plant and a black swivel lamp. “What’s the most complicated device on this table?” Kelly asked.

Of course, it’s the house plant. The point is that biology can make just about anything. Think of its incredibly sophisticated miniature machines, like the swirling flagellum that helps a bacterium swim. In Ginkgo’s hands biology would become programmable, revolutionary, and insanely lucrative, just like those famous tech products in the slide. “This is a much more powerful manufacturing platform than any of those other things,” Kelly said.

Given Kelly’s spiel, it is surprising that 13 years after it was founded, Ginkgo can’t name a single significant product that is manufactured and sold using its organisms. To the company’s fans, that’s no problem. They say Ginkgo embodies the biggest trends in DNA science and surely will become the Intel, Microsoft, or Amazon of biology. Kelly has compared Ginkgo to all three. To skeptics, however, Ginkgo is a company with modest scientific achievements and little revenue, and its greatest talents lie in winning glowing press coverage and raising money.

Ginkgo’s story matters because it has become the face of synthetic biology to many investors as it prepares to begin trading as a public stock in September following a merger with a special-purpose acquisition company, or SPAC, called Soaring Eagle. A SPAC is a shell company that sells shares to the public in an IPO with the intention of merging with a promising private business, thereby taking it public too. SPACs can open exciting (and risky) young tech companies to ordinary investors, although it’s at a price negotiated by a small circle of dealmakers. Earlier this year, Soaring Eagle announced it would merge with Ginkgo in a deal that valued the Boston company at $15 billion. Kelly’s stake will be worth well more than $700 million.

Some biotech investors believe this valuation is excessive for a company with little revenue; in 2020 Ginkgo brought in $77 million providing research services and covid-19 tests but lost plenty of money while doing so (more than $137 million, to be exact). “It looks like a great example of a clever story that caught the attention of investors,” says Jean-François Formela, a venture capitalist at Atlas Venture in Cambridge, Massachusetts. “If you boil down the message, it’s that biology is programmable. But it’s not that easy,” he says. Formela adds that the $15 billion valuation “seems insane.”

But in today’s bull market, being a skeptic doesn’t pay. So it’s difficult to say with any certainty what Ginkgo is really worth. After all, a single Bitcoin now costs $48,500 and Tesla has a market capitalization of around $700 billion, more than 10 times that of Ford. “Speaking with assurance on certain types of companies belies how difficult it is to know,” says Doug Cole of Flagship Pioneering, an organization that forms biotech startups in Cambridge. That’s especially true with companies that, like Ginkgo, are “creating new markets.”

Ginkgo’s success telling its story and raising money without introducing significant products has some skeptics wondering whether it will be next in line to crater once reality sets in. Earlier this month Zymergen, a competing synthetic-biology company, saw its stock price plunge 75% in a day after it said sales of its main product, a biological film for foldable phones, would be delayed by at least a year . Zymergen’s CEO, Josh Hoffman, who had also touted a coming era of “biofacturing,” resigned as well.

In a phone call, Kelly said his company purposely isn’t betting on any one product. Instead, he says, Ginkgo is a science and engineering “platform” for other companies to use. He compared Ginkgo to an online app store, except that the apps are programmed cells. Like an app store, Kelly says, Ginkgo will eventually profit by taking a cut of customers’ revenues, in the form of royalties or shares. It will be up to them to make and sell the biomanufactured products.

“I am not a product company, and I have no desire to be a product company,” Kelly told me. “People in biotech are brainwashed to think only products matter.”

Super unicorn

Ginkgo was started in 2008 by Thomas Knight, an MIT computer engineer who had become fascinated with “standardizing” biological research, along with four graduate students, including Kelly. At first the company got by on government grants and cast-off equipment salvaged from MIT’s campus; “we had $150,000 and a U-Haul,” says Kelly. It was a time flush with funding for “synbio” companies, many dreaming of brewing transportation fuel, and Ginkgo barely stood out.

Its fortunes were transformed in 2014, when the startup entered the Y Combinator entrepreneurship program in Silicon Valley. Soon Ginkgo was selling the dream of biology West Coast style, likening it to computing, and investors’ successive injections of cash put it on a path to “super-unicorn” status. It was a private, profitless company that investors were valuing at $1 billion by 2017 and $4.8 billion by 2019, according to PitchBook.

“They were the first real biotech company to come through Y Combinator,” says Michael Koeris, a professor of bioprocessing at the Keck Graduate Institute, who once ran a startup, Sample6, in the same building as Ginkgo. “I think the YC people taught them to package the story so that it is fundable. That is a skill. A lot of science doesn’t get funded because there’s no story.”

Kelly’s gifts as a pitchman are widely acknowledged, and his company is famous for its lavish scientific window dressing. Last year it started printing its own glossy vanity magazine, Grow by Ginkgo, which exists to “tell creative stories” about the endless possibilities of synthetic biology. A recent issue contained a scratch-and-sniff card impregnated with the scent of an extinct flower.

“Times must be good when a young biotech company can afford to hire people to write unrelated magazine-style articles,” snarked Dirk Haussecker, a savvy biotech stock picker who is active on Twitter.

Kelly says the magazine was inspired by Think , a periodical printed by IBM starting in the 1930s. “Why did they do that? Well, no one knew what the heck a computer was,” says Kelly, who sees Ginkgo playing a similar role as an evangelist for the possibilities of genetic engineering.

During a podcast , journalists with Stat News compared Ginkgo to a “meme stock,” or “stonk,” positioned to appeal to an investing public chasing trends without regard for business fundamentals. When the SPAC deal is finalized—sometime in September—the company is going to trade under the stock symbol “DNA,” once owned by Genentech, an early hero of the biotech scene. “Ginkgo Bioworks does not deserve to use the DNA ticker,” said Stat stock reporter Adam Feuerstein.

SPACs are a Wall Street trend that offers an IPO path with a little less than the usual scrutiny of a company’s financial outlook. Will Gornall, a business school professor at the University of British Columbia, believes that they democratize investor access to hot sectors but can also overestimate companies’ value. Some deals, like the one that took Richard Branson’s space company Virgin Galactic Holdings public , have done well, but five electric-car companies that went public via SPACs were subsequently pummeled with what Bloomberg called “ brutal ” corrections.

Gornall can see a bettor’s logic to the Ginkgo gamble. In recent years stock market profits have been driven by just a handful of tech companies, including Amazon, Apple, Facebook, Google, and Microsoft—each now worth more than a trillion dollars. “The valuation could make sense if there is even a 1% chance that biology is the computer of the future and this is the company that achieves that,” says Gornall.

Other people’s products

Since it was founded, Ginkgo has spent nearly half a billion dollars, much of it building labs equipped with robots, gene sequencers and sophisticated lab instruments such as mass spectrometers. These “foundries” allow it to test genes added to microorganisms (often yeast) or other cells. It claims it can create 50,000 different genetically modified cells in a single day. A typical aim of a foundry project is to assess which of hundreds of versions of a given gene is particularly good at, say, turning sugar into a specific chemical. Kelly says customers can use Ginkgo’s services instead of building their own lab.

What’s missing from Ginkgo’s story is any blockbuster products resulting from its research service. “If you are labeling yourself ‘synbio,’ that is setting the bar high for success—you are saying you are going to the moon,” says Koeris. “You’ve raised so much money against a fantastic vision that soon you need to have a transformative product, whether a drug or some crazy industrial product.”

To date, Ginkgo’s engineering of yeast cells has led to commercial production of three fragrance molecules, Kelly says. Robert Weinstein, president and CEO of the US arm of the flavor and additives maker Robertet, confirmed that his company now ferments two such molecules using yeast engineered by Kelly’s company. One, gamma-decalactone, has a strong peach scent. The other, massoia lactone, is a clear liquid normally isolated from the bark of a tropical tree; used as flavoring, it can sell online for $1,200 a kilogram. Running a fermenter year-round could generate a few million dollars’ worth of such a specialty chemical.

To George Church, a professor at Harvard Medical School, such products don’t yet live up to the promise that synthetic biology will widely transform manufacturing. “I think flavors and fragrances is very far from the vision that biology can make anything,” says Church. Kelly also sometimes struggles to reconcile the “disruptive” potential he sees for synthetic biology with what Ginkgo has achieved. Church drew my attention to a May report in the Boston Globe about Ginkgo’s merger with Soaring Eagle. In it, Kelly said his firm was an attractive investment because the world was becoming familiar with the extraordinary potential of synthetic biology, citing the covid-19 vaccines made from messenger RNA and the animal-free proteins in new plant burgers, like those from Impossible Foods.

“The article was a list of achievements, but the most interesting achievements were from others,” says Church. “It doesn’t seem to add up to $15 billion to me.” Still, Church says he hopes that Ginkgo does succeed. Not only is the company his “favorite unicorn,” but it acquired the remains of some of his own synthetic-bio startups after they went bust (he also recently sold a company to Zymergen). How Ginkgo performs in the future “could help our whole field or hurt our whole field,” he says.

While Ginkgo’s work has not led to any blockbusters, and Kelly allows it’s “frustrating” that biotech takes so long, he says products from other customers are coming soon. The Cannabis company Cronos , based in Canada, says by the end of the year it will be selling intoxicating pineapple-flavored candy containing CBG, a molecular component of the marijuana flower; Ginkgo helped show it how to make the compound in yeast. A spinout from Ginkgo, called Motif FoodWorks, says it expects to have a synthetically produced meat flavor available this year as well.

More recently, Ginkgo has sought to play a bigger role in the manufacture of new biotech drugs, a more lucrative arena. For instance, it says it helped a research supply company called Aldevron improve the production of capping enzymes, which are used in the manufacture of mRNA vaccines . Those enzymes are in high demand because of the covid-19 crisis, and if the process is commercialized, they will represent the most important product Ginkgo has been involved with. That product could see several hundred million in annual sales, which Kelly says Ginkgo will collect part of as royalties.

One problem some see is that making real money in industrial biology is notoriously difficult. Engineering a microbe that performs well in a laboratory reactor is just a first step. Often the organisms need to be further tweaked to grow and thrive under pressure in steel tanks before it’s possible to actually manufacture something. But the trickiest part is making bioproducts inexpensively enough to compete with existing chemical production.

“The biotech landscape is scattered with bodies of companies that couldn’t scale or didn’t think about the economics,” says Chris Guske, a chemical engineer who has worked on some of the world’s largest biorefining products. “Just because you have a bug that produces a gram per liter in a flask doesn’t mean you are ready to be commercial.”

John Melo, CEO of Amyris, another synthetic-biology company, says Ginkgo does not have expertise in large-scale production, and he thinks Kelly is “paranoid” that betting on products “equates to difficulty and failure.” Amyris itself almost collapsed after it failed in a plan to sell biofuels for transportation but is staging a turnaround by manufacturing and selling beauty ingredients and flavors. In Melo’s view, unless things are made with biology at large scales, the dream of renewable manufacturing won’t be any closer. “I think this notion of not being a product company misses the point,” he says. “How can you be enabling sustainability if you don’t make a product?”

Circular revenue

All of biology is being pushed forward by the ability to read DNA, write it, and use those instructions to program organisms or human cells. By automating the use of these technologies, Ginkgo’s backers believe, the company is uniquely positioned to take a commanding position. Harry Sloan, a lawyer and Hollywood executive, is one of the business figures behind Soaring Eagle and previously took the fantasy sports betting company DraftKings public. “These are companies that are not only leaders in their field but actually created the field themselves,” he told the Globe. “That is certainly the case with Ginkgo and synthetic biology.”

In its presentations to investors, Sloan’s group predicts that within four years Ginkgo’s app-store model will have five hundred clients and generate billions of dollars in cash flow. Ginkgo frequently issues press releases announcing new customers, suggesting a growing clamor for its scientific resources. However, many of its customers are not fully independent from Ginkgo. According to Ginkgo’s financial documents, more than half its foundry’s 2020 revenues came from a few “related” companies that it partly owns.

One way Ginkgo creates demand for its services is to form spinout ventures, which then become the customers of its foundries. These deals have sometimes been financed by Ginkgo’s own largest investors, which include the hedge fund Viking Global and Cascade Investments, Bill Gates’s investment firm. Besides Motif FoodWorks, which operates in the same building, Gingko created Allonnia, a company developing microbes to break down pollution, which also subscribes to its foundry services.

Another example of how Ginkgo has financed demand for its services was a collaboration it announced in June 2019: a ” transformational ” project with a startup called Synlogic, which is engineering E. coli bacteria to treat serious metabolic disorders. In ongoing studies, patients are swallowing pills filled with germs that have been programmed to carry out helpful functions, like digesting certain excess amino acids, the cause of a disease called phenylketonuria. The deal was important because it signaled that Ginkgo could get involved in potentially profitable new drugs, not only industrial ingredients.

But the way the deal was structured, it was Ginkgo that ended up paying for most of the R&D, not Synlogic. As part of the agreement, Synlogic did cut a check for $30 million in cash to Ginkgo for foundry services aimed at improving its strains. But Ginkgo simultaneously invested $80 million in Synlogic at a sizable premium to its stock price at the time. In effect, the money took a round trip, starting as cash in Ginkgo’s bank account and ending up as payment for foundry services.

Even though Ginkgo underwrote the research, Kelly has touted the collaboration to investors as an example of its successful business model. After adding DNA to organisms and testing them, he explains, “we give you a tube the size of a thimble that’s got a cell with the genome that you need. And that is all that leaves that big factory. And then you as the customer would grow that in your big tanks … if you’re Synlogic, it’ll go into clinical trials as a therapeutic, right?” In preparation for the SPAC merger, Ginkgo also told investors that it improved the performance of one Synlogic strain of E. coli several times over.

Aoife Brennan, Synlogic’s CEO, says Ginkgo “has absolutely demonstrated” it can improve the performance of E. coli strains, particularly if the job involves an automated “bake-off” between versions of a genetic pathway to see which is best. With its investment in automation, Ginkgo says, it is constantly lowering the cost of experiments and increasing the number of organism designs that it can test, a metric it refers to as “Knight’s Law,” after its founder. In this case, Ginkgo screened more than 1,000 genes and created several hundred strains.

That type of automated procedure, Brennan says, is helpful “when you know what you are looking for” but is “sometimes is still not what we need.” Often, research problems are instead solved by scientific “tinkering,” she says, or tests that aren’t easily automated. Indeed, the particular organisms Ginkgo helped engineer were not successful after they failed to “reach our criteria to advance the project” into clinical testing, Brennan says. Instead, this summer Synlogic announced it would begin human tests of a new version of its E. coli engineered by enEvolv, a startup recently purchased by Zymergen, which she says brought capabilities to the project that Ginkgo did not have at the time.

By forming spinouts and taking equity in its customers, Ginkgo can seem to be acting as much like a venture capital firm as a research company. For instance, Kelly, along with his company’s largest outside investor, Viking Global (it owns 20% of Ginkgo), came to the financial aid of Genomatica, a company making plastic precursors and facing a costly push to commercialization. That company wound up largely owned by Viking and Ginkgo, while also becoming one of Ginkgo’s customers. A person formerly close to Genomatica described Ginkgo as acting as “an arm of Viking” whose true business could be described as financial engineering, not genetic engineering. This person called Gingko “effective” because of how they can use capital to “organize the market” and “reignite interest in synthetic biology.”

Ginkgo’s practice of juicing up demand by investing in its customers, trading foundry work for equity, and financing demonstration projects was the subject of a 2020 Harvard Business School case study, which concluded that the arrangements were useful for “explaining Ginkgo’s future growth and untapped potential” to its own investors. But the arrangements make Ginkgo’s finances a little harder to figure out, even for Ginkgo.

Kelly confirmed that some of the CEOs of Ginkgo’s partners, including the head of Motif, raised concerns to him about the unpredictable way Ginkgo was expending the foundry credits they’d been awarded. Basically, Ginkgo was dividing the cost of running its foundry between whatever customers it happened to have, rather than billing them at fixed rates, which it does now. Brennan says the “loosey-goosey” accounting created challenges for her company, which is public already and had to file quarterly reports with the US Securities and Exchange Commission. After the group raised the concerns, Kelly quickly solved the problem. “You can fix a lot of things with money,” says Brennan. “And they have a lot of money.”

In an interview, Brennan says she is pleased to have Kelly involved with her company, calling him a supportive shareholder whose view is that “if we are successful, then they are too.” Kelly’s belief in synthetic biology is unflagging, and his support has continued even as Synlogic’s stock price has slumped, erasing about three quarters of Ginkgo’s investment on paper.

“He is very charismatic,” she says of Kelly. “It is nirvana to have an investor who also believes engineered bacteria will be on the shelf at CVS helping people one day.”

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Innovation and Value Creation. How to measure when innovation is actually paying off.

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 22 Aug 2021 09:26:06 GMT

By studying Research Quotient Index at the unit level and over time, companies can identify internal benchmarks to improve their overall R&D efficiency and even identify external practices to adopt.

Corporate innovation is the major source of economic growth. Unfortunately, companies are generally wasteful in R&D spending, according to a measure called RQ, which allows you to assess the profits generated for the last dollar of R&D. The evidence suggests that activist investors are good at figuring out when companies are wasteful with their R&D budgets but their fixes are oriented to cutting expenses and speeding up project completion in order to deliver immediate stock price performance and do not deliver more innovation in the long run. By studying RQ at the unit level and over time, companies can identify internal benchmarks to improve their overall R&D efficiency and even identify external practices to adopt.

In order to reduce the debt levels imposed by Covid-19, governments will need their country’s economies to grow.v For example, the combined cost of government programs for Covid-19 has pushed U.S. federal debt to 102% of GDP , the highest level since World War II. In order to afford the new debt without dramatically decreasing spending or increasing taxes, we need substantial economic growth. The primary path to this is innovation, which from 1990-1995 accounted for an estimated 60% of growth — and 70% of that innovation was funded by companies . As such, company-driven innovation is likely going to play a huge role in the recovery.

A possible obstacle in their path is activist hedge funds. The hedge fund industry began 2021 with a record $3.6 trillion assets under management. Its size, coupled with corporate weakness from Covid-19 shutdowns, portends an increase in activist investment. To be sure, some of this investment will be beneficial. However, the 10-year average life of hedge funds dictates that activists must pursue relatively short-term gains, which typically come at the expense of long-term investments. According to this logic, companies with substantial R&D investment would seem to provide low-hanging fruit: cut R&D, immediately increase profits.

But, that’s not necessarily the case. As we’ll see, some companies do spend too much on R&D, but that’s because they’re not doing R&D very well, not because the investment isn’t needed. There are also companies that could benefit from investing more in R&D because what they’re doing is productive. And while smart investors can often figure out which category a given firm falls into, they’re not best placed to take a direct hand in deciding how to actually spend the money, which is where the real action has to be.

R&D: Overspending or Not Good Enough?

There is evidence that activist investors do in fact distinguish between valuable R&D investment and wasteful investment. Take for example Trian Fund’s 2013 campaign to get DuPont to cut costs, including in R&D operations. Nelson Peltz was castigated for seeking to dismantle one of the preeminent industrial research labs. I, too, worried he would be destroying a valuable national asset, so did some digging to assess that.

What I found was that DuPont’s innovation engine had deteriorated fairly dramatically. While its R&D productivity, according to a measure I developed called RQ (which stands for Research Quotient), ranked it as one of the most innovative firms in the early 1990s, now it was below average. DuPont was not alone in that: R&D productivity in companies has declined 65% on average over the past four decades. Not only had DuPont’s deterioration gone undetected, worse, as a consequence, in 2013 they were over-investing in R&D by about $1 billion dollars per year (over-investment refers to dollars of R&D that generate less than a dollar of profits). It was this waste that likely provided the low-hanging fruit for Trian.

How do we know this? Because Trian didn’t go after Dow. The two firms were in the same industry, specialty chemicals, and were investing similar amounts in R&D: $1.75 billion for Dow, versus $2.1 billion for DuPont. Moreover, to a naïve outsider, Dow’s R&D budget looked more wasteful: it amounted to 6% of revenues — twice the R&D intensity of DuPont, which was investing only 3% of its revenues in R&D.

What did Trian know about Dow and DuPont that naïve outsiders didn’t? They knew that Dow had the better innovation engine. My numbers confirmed this: Dow’s RQ had been among the top 50 firms for 15 of the prior 25 years. In fact, its innovation capabilities were so much stronger than DuPont’s that even at 6% of revenues they were under-investing in R&D.

This comparison illustrates a few important points. First, activists aren’t necessarily the villains they’re made out to be — DuPont needed to reshape its R&D, and without Peltz’ intervention that was unlikely to happen. Second, the best defense against an activist campaign is taking opportunity off the table — keeping your company’s innovation engine tuned, and not flooding it with excess investment. How do you go about doing that?

What’s Your RQ?

RQ can help managers fix their innovation engines before the activists do it for them. Knowing your RQ lets you compute the expected growth in revenues, profits, and market value from a given change in your R&D budget. It also allows you to compute the optimal investment in R&D — i.e., the level where your last dollar spent on R&D generates a dollar of profits.

As the Dupont and Dow examples illustrate, the likelihood of an activist campaign increases when companies spend beyond their optimal levels, and a statistical analysis of firm’s RQs and their financial reports makes it possible to create a vulnerability score for each firm. The analysis shows that companies are primarily at risk because they are over-investing in R&D. Two thirds of the 1,416 companies loked at over-invested by more than 10%, and on average each is leaving $445 million of profits on the table every year. Like DuPont, many are over-investing because their RQ has declined, rather than because they have dramatically increased R&D. In most cases, if they could restore their prior RQ, they would be spending at appropriate levels. (Under-investment also leaves profits on the table. But this tends to attract less attention from activist investors because figuring out how to invest more in R&D is harder than simply cutting wasteful expenditure.)

Note that companies normally appearing on “most innovative companies” rankings, often have below average RQ, as was the case with DuPont. These rankings are typically based on either R&D spending or surveyed opinion. As we’ve just established, basing your assessment of how innovative a company is on the size of its R&D budget is simply going to favor the companies that spend the most, regardless of how effective that spending is. Similarly, innovation surveys generally ask executives and consultants to rank all companies. Accordingly, the results favor companies producing innovations visible enough that that executives outside the industry notice them, which may be equally misleading.

In principle, companies can reach the optimal budget for their RQ simply by cutting the overall budget. That’s certainly what activist shareholders push for. Research by Alon Brav, Wei Jiang, Song Ma and Xuan Tian indicates that activists usually get companies to reduce R&D spending by 20%. But the problem with this is deciding what goes into the 20%. If companies cut fat, they may at the same time change the RQ for the better and find that they’re now underinvesting. And if they cut productive activities, they will end up more vulnerable than before. The devil, as ever, is in the details — in this case of what you cut and what you don’t.

That’s why activists usually insist on adding a technologist to the company’s board and increase the equity stakes of chief technology officers (CTOs) at their targets by about 50%, which motivates these executives to increase the target companies’ market value. That sounds good and does appear to increase innovation when using some measures (stock-optioned CTOs do seem to get more patents processed, for example). However, the evidence suggests that target company RQs generally tend to decrease following activist investment, suggesting that the choices of the CTOs may be aligned to the shorter time horizon of the activists who bring them on. Since RQ is the only measure theoretically and empirically linked to long-run growth, it seems that activists are probably detrimental to our goal of paying off the federal debt from Covid. The question, then, is how to go about increasing RQ.

Moving the needle

The first step is taking care of the low-hanging fruit identified in the book How Innovation Really Works . The book serves as the final report for practitioners from two NSF funded studies linking companies’ R&D practices to their RQs and documents the RQ impact of all practices for which there is publicly available data linked to firms.

But the book only documents practices that hold across the entire economy. High RQ requires going beyond that to implementing practices whose value may be confined to your technologies or your industry. Begin by benchmarking against your past history . On average, companies’ RQs have declined 1.5% per year, so their RQs were generally higher in the past. Pick a year that is particularly high, then determine what were you doing differently at that time. Identify which of those differences likely made you more productive and implement the ones still relevant to your current technologies.

Next, start benchmarking across divisions . Divisional RQs can differ dramatically within a company and, while some of these differences are undoubtedly market or technology related, often the high RQ divisions employ distinct R&D practices. So once you’ve identified the RQs of each of your divisions, determine how R&D is conducted differently in the high RQ divisions, assess which of those practices are relevant to remaining divisions, and diffuse those throughout the company.

These kinds of internal benchmarking should be easy — aside from effort, there is nothing precluding you from obtaining the necessary information about where higher RQ resides (or has resided) and what practices underpin it. External benchmarking , however, is harder because companies typically keep information about their R&D very close to their chests. But if you can work out a competitor’s RQ and discover that it’s higher than yours, you should be able to figure out what your rival does differently from analyzing public sources such as annual reports, conferences, and media accounts, not to mention from employees you have who have moved from your competitors.

***

Although efforts by companies to decrease vulnerability to activists, and efforts by activists to restructure innovation both increase shareholder value in the short-run, only the vulnerability-reducing mechanisms increase long-run growth. Accordingly, companies should continually assess the vulnerability of their innovation engines to activist investment. If they find themselves vulnerable, they should take measures to remove those vulnerabilities. Doing so will not only increase their own long-term growth and market value, it will contribute to the growth of their economies — a win-win path to getting out from under the mountains of Covid-related debt.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Zomato's IPO and the future of tech companies

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 12 Aug 2021 09:02:08 GMT

The six features of a tech company

Despite operating a traditional food business, Zomato epitomizes a modern tech company: Zomato, a food delivery company, is aiming to transform the eating habits of 1.36 billion people in India. In mid-July, its initial IPO was oversubscribed 35 times, giving it a valuation of $12 billion. Despite operating a traditional food business, Zomato epitomizes a modern tech company, and its successful IPO can teach us what a modern tech company is — and what it isn’t. It can transform whole industries, achieve expansion of scale and scope at breakneck speeds, and make enormous profits, all without requiring significant capital investments.

In mid-July, Zomato, a food delivery company, listed its shares in Indian stock markets. Its initial public offering (IPO) was oversubscribed 35 times , giving it a valuation of $12 billion . Why does a loss-making company — with no real properties or assets — command such high valuation and attract global celebrity investors like Fidelity, Morgan Stanley, Canadian Pension Fund, and the Singapore Government?

Like DoorDash and SkipTheDishes, it delivers ready-to-eat food to homes without owning farms, food stores, restaurants, warehouses, trucks, or delivery vehicles. Its business model is similar to those of other tech companies like Uber, Amazon, and Airbnb, but differs subtly from the likes of Facebook and LinkedIn.

For example , WeWork is not a tech company, despite its claims to the contrary. WeWork’s failure at IPO and Zomato’s success can teach us what a modern tech company is and what it isn’t . In our opinion, a successful modern tech company can transform whole industries, achieve expansion of scale and scope at breakneck speeds, and make enormous profits, all without requiring significant capital investments. It typically has most, if not all, of the following six features.

The 6 Features

Rapid industry transformation. Zomato aims to transform the eating habits of 1.36 billion people in India, where 90% of the population doesn’t eat at restaurants. Compare that to China, where 58% of people routinely eat at restaurants. Previously, there were two hurdles to dining out in India. The first was sheer logistics: Just 2% of Indian households own sfirts (compared to almost 98% of U.S. households). The second was cultural taboo: Some people would never eat food cooked in someone else’s kitchen. Zomato clears both of these hurdles: It gives a new segment of the population access to restaurant food by delivering it with the touch of a button. It also brings down cultural barriers by encouraging users to provide feedback — people will be less reluctant to try restaurant food when they see their own family members or people from their own caste and peer groups doing so and providing recommendations about dishes and restaurants. While a food-delivery app may feel familiar to many of us, Zomato potentially transforming the eating habits of a huge number of people is no less ambitious than what Uber or Airbnb set out to do. Uber empowers millions to get rides from strangers and now employs more cars than any taxi company in the world. Airbnb facilitates staying at strangers’ houses and offers more rooms than any hotel chain in the world. Thanks to these companies, people don’t need to own their own kitchens, cars, and homes in order to enjoy their privileges. This virtual shared ownership creates value for people by improving asset utilization and lowering the risks that come with asset ownership.
Low capital costs yet extremely valuable local assets. Google Search, Airbnb, Yelp, Uber, LinkedIn, and Facebook share one common feature: They have scalable virtual models that can be magnified exponentially with few additions to their asset bases. This is unlike a company like, say, Ford or Target that would require land, factories, distribution centers, or warehouses to expand. Put another way, tech companies can expand their revenues and income statements with little addition to their balance sheets. Zomato, a multibillion-dollar company, doesn’t even own an office. Nevertheless, Zomato differs from other tech giants in one important respect: Companies like Google and Facebook can serve a foreign country without having a physical presence there. In contrast, Zomato enters new cities only after establishing relationships with local restaurants, assessing their offerings, and working with them to improve their menus and pricing. It also identifies, evaluates, and appoints local delivery agents. Zomato thus invests large amounts in local relationships and local knowledge — soft assets that cannot be easily replicated by competitors.
Customer intimacy. Modern tech companies collect, store, organize, and analyze years of user data. This data is virtual gold, as it enables companies to run targeted ads and personalize the customer experience. The key difference between a customer walking into a Walmart supercenter and Amazon’s online store is that Amazon instantaneously reorganizes the whole store (layout, displays, product offerings, etc.) in a way that’s tailormade for that customer.Similarly, Zomato or Uber Eats can track a customer’s tastes, need for discounts, and preferences in terms of cuisine, delivery time, and price and combine those insights with local food trends, seasonality, and holidays and festivals to offer a customized menu instantaneously. This level of customer intimacy increases switching costs for customers, imposing significant barriers to entry for new players.
Network effects. For most modern tech companies, the bigger the network, the more valuable the company. There are three types of network effects: direct network effects, indirect network effects, and data network effects. Tech companies like Facebook and LinkedIn benefit from direct network effects. Each new customer joining Facebook or LinkedIn creates value for an existing customer because both customers can now create direct links with each other, even if they’re located in different places. The thousandth customer joining the network creates more value than the tenth, fiftieth, or hundredth customer, because the thousandth customer can create 999 new links, while the tenth can create just nine links.Tech companies like Netflix, Amazon, Uber, and Zomato don’t have direct network effects. If a new customer joins Zomato, they don’t create a direct new link with current customers. Similarly, a new restaurant joining Zomato doesn’t create value for current restaurants using Zomato. However, in a two-sided platform like Zomato, there are indirect network effects. The greater the number of customers, the greater the value for restaurants and vice versa. Now customers have more choices, restaurants have a bigger market, and the delivery network can be utilized more efficiently. More important, Zomato benefits from data network effects. Every new customer and restaurant contributes valuable data that Zomato can use to improve the value proposition for all existing users by enhancing the quality and depth of feedback, understanding usage habits, optimizing logistics, troubleshooting, and growing the repository of local tastes and preferences. Machine learning keeps improving these insights. Zomato can now provide more personalized recommendations for each customer and better connect restaurants to their target customers based on collective learning across its customer base. This improvement, when coupled with increases in the number of delivery agents and restaurants that get attracted to the bigger market, improves the options of products and services while lowering costs. Zomato calls it a “flywheel effect,” and every new customer and supplier joining the network adds to the flywheel’s momentum.
Ecosystems that boost expansion with minimal costs. A modern tech company can leverage its relationships with customers to deliver new lines of products and services. Companies that rely on ecosystem partners’ assets can achieve this at little additional cost. Consider Apple’s use of the iPhone and Amazon’s use of Echo devices to sell apps, music, games, and videos produced by third parties — and doing so using their own payment services. Apple and Amazon then take a cut from each dollar flowing through their systems. As another example, Uber extended its rideshare service to Uber Eats with minimal investment. Similarly, Zomato can extend its offering of restaurant food to include pre-sorted, ready-to-cook ingredients; in fact, it’s already leveraging its relationships with restaurants to source ingredients for them. Zomato also books tables for customers at its partnering restaurants by offering customized recommendations based on customer insights.
Variable costs and margins. Google Search, Microsoft, Twitter, and Facebook can scale up their revenues with minimal variable costs. It costs relatively little to make another copy of Windows 10 or service another Google or Facebook customer. Facebook’s gross margins, for instance, run as high as 80–85%. The same concept, however, does not apply equally to Uber, Airbnb, Amazon, and Zomato. Large parts of their revenues are passed on to suppliers, such as restaurants (Zomato), car drivers (Uber), and homeowners (Airbnb). In addition, Amazon and Zomato must pay their delivery agents. But improvements in scale, knowledge about suppliers, and increases in bargaining power cut those variable costs. Futuristic technologies like drones, robotics, and autonomous vehicles can further reduce delivery costs. Increased scale improves unit economics — the profits obtained from each new unit of transaction — by improving revenues and lowering per-unit costs . Rapid growth thus becomes an inherent part of a firm’s strategy.

It’s noteworthy that asset-light “tech” companies that display these six characteristics have commanded large valuations in the 21st century. As of July 2021, the combined market capitalization of FAANG companies (Facebook, Apple, Amazon, Netflix, and Google) plus Microsoft stands at almost $9 trillion, which exceeds the GDPs of all nations in the world except two. So Zomato’s high valuation shouldn’t come as a big surprise. After all, it’s a proven tech company that aims to transform the eating habits of a billion-strong nation.

Today’s tech giants must be contrasted against industrial giants of the 20th century: Ford Motors, General Electric, Dow Chemicals, Standard Oil, Union Pacific, etc. Those companies also transformed industries and society. They required large-capital investments that took decades to build. Yet despite being asset-light in traditional sense, a 21st century tech giant commands a library of soft assets that cannot be replaced or reproduced easily. How easy is it to obtain the trust of and reproduce the relationships, networks, and personalized data of 1.5 billion Facebook subscribers?

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Green Assets rally may stop soon

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 30 Jun 2021 16:33:00 GMT

How climate concerns affect the market value and the cost of capital

Recent years have seen high returns for investments in green assets, or stocks and bonds of companies that espouse environmental, social, and governance (ESG) principles. Green funds are being aggressively marketed and about $3 billion a day is being invested in such assets. The $17 trillion of sustainable assets under management make up a third of the $51 trillion that is professionally managed, according to the 2020 report on trends in sustainable and impact investing by the U.S. SIF Foundation.

Many investors are attracted to ESG securities on promises of high returns, but they are “misguided,” Wharton finance professor Luke Taylor said on the Wharton Business Daily radio show on SiriusXM . The past performance of ESG securities is not a reliable indicator of returns in the future, especially when past returns were largely driven by “shocks” such as bad news about climate change, he noted. “Absent more unexpected shocks in the future, we don’t see those green stocks outperforming [‘brown’ or environmentally unfriendly stocks] in the future.”

Taylor’s comments are based on a new research paper titled “ Dissecting Green Returns ,” which he co-authored with Lubos Pastor , finance professor at the University of Chicago Booth School of Business, and Wharton finance professor Robert F. Stambaugh .

The paper explains how shifts in investors’ preference for green assets could move prices. “First, investors’ preference for green assets can increase, directly driving up green asset prices. Second, consumers’ demands for green products can strengthen — for example, due to environmental regulations — driving up green firms’ profits and thus their stock prices. Similarly, investors’ preference for brown assets or consumers’ demand for brown products can decrease, again making green stocks outperform.”

The authors use an economic model to understand what the past performance of green assets implies about their future performance. The model predicts that green assets have lower expected returns than brown due to investors’ tastes for green assets. Yet, green assets can have higher realized returns while investors’ tastes “shift unexpectedly in the green direction.”

The paper’s authors constructed a “green factor,” which captures the return spread between stocks of U.S. companies that are environmentally friendly (green assets) and unfriendly (brown assets). The green factor earned a cumulative return spread of 35% from November 2012 to December 2020. “[That shows] green stocks significantly outperformed brown stocks in recent years,” they stated. “But our main [finding] is that this outperformance completely disappears once you remove the effects of shocks to climate change concerns.”

The study measures shocks to climate concerns using data from U.S. newspaper articles about climate change. “During this period you see a large, steady increase in concerns about climate change, and that pattern closely mirrors the green factor’s strong performance,” explained Taylor. The authors then perform a “what if” analysis, computing what the green factor’s past performance would have been in the absence of climate concern shocks. The study shows that green stocks would not have outperformed brown in recent years without strengthened climate concerns.

While some investors expect green securities to continue delivering high returns, “we predict the exact opposite of that,” said Taylor. “Green stocks today are expensive and dirty stocks are cheap” because investors care much more about environmental issues than they did eight years ago, he pointed out. “Investors today are willing to pay more to hold environmentally friendly stocks, so they are willing to accept lower future returns from such stocks.”

In other words, the more expensive a stock, the lesser is the upward room for its price to appreciate. “During a period when climate concerns strengthen sufficiently, the green factor delivers a positive return, as investors demand greener stocks or customers demand greener products,” the authors stated. “Outperformance caused by the strengthening of investor concerns is followed by lower expected performance of the green factor going forward. That is, a shift in the green factor’s expected future performance relates inversely to its realized performance.”

That wedge between expected and realized returns is central to the paper, the authors stated. The wedge is especially clear in the case of so-called German “twin” bonds — a green bond with an otherwise identical non-green twin, both issued by the German government since 2020. The green bond trades at lower yields, indicating lower expected returns compared with non-green bonds. The yield spread between the green and non-green twins, known as the “greenium,” reflects investors’ willingness to accept a lower return in exchange for holding assets more aligned with their environmental values.

Since issuance, the 10-year greenium has widened nearly three-fold, “presumably due to growing climate concerns,” the paper noted. “As a result, the green bond outperformed its non-green twin by a significant margin over the same period. However, this outperformance does not imply green outperformance going forward. Rather the opposite is clearly true, given the now wider greenium.”

The green bonds outperformed their non-green twins as “the green bonds’ yields got lower and lower relative to their twins,” said Taylor. The lower yields meant higher prices and therefore more capital appreciation for investors. But new investors who buy the green bonds at the relatively high current prices should expect even lower returns in the future.

Advantage Social Impact

Looking ahead, Taylor said the paper’s authors have both “bad news and good news” for ESG investors. “Our bad news is, don’t expect this recent outperformance of green assets to continue. But the good news is that if you’re an ESG investor, you want these companies to have lower expected returns, because the expected return is the cost of capital. And one way that ESG investors have social impact is by reducing the cost of capital of green companies. Since we’re predicting these brown stocks to outperform in the future, that means brown companies have a higher cost of capital.”

In that setting, a coal company would have a higher cost of capital, Taylor said. “That’s good news for the environment, because that means it’s going to be harder for coal companies to raise money to do things like dig new coal mines.”

Another way ESG investing has social impact is “it induces all companies to become greener,” Taylor continued. “It induces a company that’s already green to become greener, but it also induces that brown company, for example that coal company, to become a little greener. Why? Companies know that if they can become a little bit greener, it’s going to increase their market value.”

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The price discovery process, the stock market and the new normal

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Jun 2021 19:09:21 GMT

There are new trends and risks.

Business leaders must constantly manage risks and opportunities in an uncertain world in the hope that their companies will continue to operate and appreciate in value. But in today’s world, there are five trends that could are colliding that have the potential to distort a company’s valuation: 1) low interest rates; 2) the shift to passive investing; 3) the rise of ESG investing; 4) nationalism, protectionism and other global cross-currents; and 5) cryptocurrency and other financial innovations. The dangers of this distortion, especially at a time of buoyant stock markets, is that company executives and investors use these incorrect valuations as a basis to enter unaffordable M&A transactions and/or overleverage the company.

Traditionally, price discovery — determining a company’s fair value price — is based on the interactions of buyers and sellers in a marketplace. The publicly quoted share price demonstrates how capital markets value a company, and it’s the basis upon which the company issues debt and equity. It also helps determine how the company allocates capital towards paying dividends, buying back company shares, compensating employees, paying down debt or reinvesting in the enterprise for future growth.

But today, five trends are colliding to distort how markets are pricing companies. The dangers of this distortion, especially at a time of buoyant stock markets, is that company executives and investors use these incorrect valuations as a basis to enter unaffordable M&A transactions and/or overleverage the company.

The five trends

1. Low Interest Rates

Historically low interest rates, massive stimulus in response to the global pandemic, and the rising threat of inflation are leading to questions on appropriate discount rates to value a company in its entirety — its equity and its debt, including its pension obligations.

Low interest rates are also fueling enormous money flows into private capital, as are lower expected returns from public markets. Private equity investors are sitting on approximately $2.5 trillion in cash, according to Preqin . That is the highest on record and more than double what it was five years ago. Looking ahead, venture capital and private equity combined ware predicted to more than double their assets from $4.4 trillion at the end of 2020, to $9.1 trillion by 2025.

Capital flows to private equity have been accompanied by a decline in publicly traded companies. According to the Wilshire 5000 Total Market Index , the number of publicly listed U.S. stocks peaked at a record of 7,562 in 1998. At the end of 2020, there were fewer than 3,500. This decline means there are fewer public peers for business leaders to value their companies against, and less liquidity for companies as capital drains from the public capital markets.

2. Shift Towards Passive Investing

Another shift occurring across the investor landscape that can affect company value is the trend away from active investing toward passive funds. From 1995 to March 2020, passive funds grew from 3% of equity markets to make up 48% of assets under management in equities as of March 2020, according to a paper by the Boston Fed.

As of 2019, passive funds are estimated to be around $4.3 trillion , and they’re expected to reach parity with active funds with each totaling $13.4 trillion in assets by 2025 , according to Price Waterhouse Coopers.

The growth in passive funds can materially improve stock price stability in the markets, reducing volatility in the shareholder register and potentially in the stock price itself, because passive funds strictly track benchmarks, only sell stocks that leave the benchmark, and are therefore considered long-term, permanent capital. These data suggest a shift which should aid in a better price discovery process – more price stability from permanent capital.

However, the shift towards passive investors tips that balance of power toward a small number of dominant investors, which could create additional complexity for companies. For example, the three biggest passive investors by volume — BlackRock, Vanguard, and State Street — own around 20% of the shares of the typical S&P 500. These three funds combined own 18% of Apple shares, 20% of Citigroup, 18% of Bank of America, 19% of JPMorgan Chase, and 19% of Wells Fargo, according to Bloomberg .

In practice, this means these passive investors wield enormous power – and potentially could find themselves on both sides of, say, an M&A transaction, not only unveiling conflicts that have to be cleared but also potentially impacting the price of a deal. Specifically, the same passive investors would be important shareholders and voters on both sides of a merger between two companies. When the vote comes on whether to accept a bid, investors on both sides of the trade might be willing to accept a lower price than those who solely own shares in the company being sold.

3. The Rise of ESG Investing

ESG market trends, purported to be worth $45 trillion in assets under management in 2020 , are creating a quandary for how global corporations think about fair value for their companies and price discovery.

On the one hand, ESG trends impose additional costs of compliance, which can reduce revenues by shutting down products and business lines, as well cutting operations in certain jurisdictions. This creates a risk of undervaluation compared with companies from countries where ESG expectations and costs of compliance are lower.

On the other hand, there is increasingly a risk, particularly in Western capital markets, that companies without strong ESG credentials could see their valuations marked down. These conflicting ESG forces add opacity to the price discovery process.

4. Nationalism, Protectionism, and Other Global Cross-Currents

Fourth, the risk of greater deglobalization promises to impact all manner of how companies do/operate business. Rather than benefit from the synergies of a global business – such as centralized logistics, supply chains and procurement – companies face financial loss as they navigate a series of threats, including:

Curbed trade in goods and services due to protectionist policies
Limits to investment and repatriation amid capital controls
Barriers to global recruitment under restrictive immigration policies
More balkanized intellectual property platforms as a “splinternet” pits a China-led platform against that of the U.S.
The breakdown of global cooperation – so that global standards and multilateralism take a back seat to national interests.

The collision of these trends suggests price discovery itself is at risk of becoming a more balkanized and less transparent exercise. In a more siloed world, a company’s valuation could suffer from the risk that the sum of its parts may not be equal to, and could be lower than, the whole.

5. Cryptocurrency and Other Global Financial Innovations

Finally, fundamental changes in the global financial architecture — whether the rise of cryptocurrency or the threat of China’s efforts to unseat the U.S. dollar as a reserve currency — could also materially affect the price discovery of a company depending on how it is exposed and positioned.

With respect to cryptocurrencies, issues of volatility and speed lead skeptics to wary of its effects. With customers and suppliers adapting to their use, companies should consider the effects of placing cryptocurrencies on their balance sheet — and the potential impact on company valuation. For instance, Bitcoin’s volatility would make it harder to calculate the true value of a company at any given point. Bitcoin’s three-month realized volatility, or actual price moves, is 87% versus 16% for gold according to a February 2021 report by JPMorgan.

Meanwhile China is now the largest trading partner, foreign direct investor and lender to numerous developed and developing countries around the world. It’s also the largest foreign lender to the U.S. government. Through expansive cross border efforts, such as the Regional Comprehensive Economic Partnership (RCEP) trade agreement , the Belt-and-Road Initiative and its use of derivatives in trading contracts, China is stamping its imprimatur on the globe. But perhaps most crucially China is backing its own digital currency, a virtual yuan, which, although not a peer-to-peer cryptocurrency, could challenge both Bitcoin and the U.S. own attempts at a digital dollar.

Corporations will have to weigh up the risks and benefits of crypto and digital currencies and decide whether to hold them as assets and liabilities on the company balance sheet; a decision that will affect the company’s value.

Business leaders are constantly managing risks and opportunities in an uncertain world in the hope that their companies will continue to operate and appreciate in value. Yet, quite clearly, there are a number of trends that could left unchecked, undermine and harm a company’s valuation — many of which remain widely overlooked by consensus views.

Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The bright side of common ownership of startups by VCs

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 25 May 2021 14:05:29 GMT

Common ownership rates are positively correlated with the ratio of R&D output to funding

When venture capital firms own equity in more than one competing startup in an industry, they have the ability to improve innovation efficiency by redirecting their investments away from laggards in their portfolio towards those that show more promise. The VCs may stop funding those lagging startups, but continue to extract value from them by getting them to shift their focus to non-overlapping projects.

Those are the main findings of a recent study conducted by Wharton finance professor Luke Taylor , Xuelin Li , assistant professor of finance at the University of South Carolina and Wharton doctoral finance student Tong Liu . They detailed their findings in a research paper titled, “ Common Ownership and Innovation Efficiency .”

The researchers studied common ownership in the pharmaceutical industry, covering 1,045 Phase I drug projects conducted by 481 U.S. startups between 2015 and 2018 and financed by 764 VC firms. They measured innovation efficiency as the total number of drugs receiving approval from the U.S. Food and Drug Administration (FDA), scaled by the total amount of VC funding provided to all startups active in that category.

The study found that “common ownership rates are positively correlated with the ratio of R&D output to funding,” where R&D output refers to drug candidates reaching FDA approval. That measure of innovation efficiency does not have a causal interpretation, but “it is consistent with common ownership helping to avoid excess duplication of R&D, producing more approved drugs per dollar of aggregate R&D,” the paper stated.

Common ownership of startups by VCs has both a “dark side” and “a bright side,” according to Taylor. “I was interested in knowing if there would be a bright side of common ownership, through its effect on innovation,” he said in a recent episode of the Wharton Business Daily show that airs on SiriusXM . (Listen to the podcast above.) “The research lately has been focused on the dark side of common ownership, which can lead those companies to compete less with each other, and that can raise consumer prices.”

The study found common ownership achieving gains that go beyond those for the firms in their portfolios. “The bright-side interpretation of what we’re finding is that common ownership may be helping us as a society,” said Taylor. “It may be helping us to reduce duplication of R&D in patent races. When firms are in a patent race with each other, and when they’re competing really fiercely with each other, they tend to invest more in R&D than is good for society. A common owner can come in and coordinate these firms that are in a patent race, and help solve that market failure.”

The paper noted that startups are important for generating innovation, and VC-backed startups generate a large share of the innovation in the U.S. economy. The study focused on the pharmaceutical industry because that “is a big part of what VCs do,” Taylor said.

He summarized the three main results from the study: First, common ownership leads investors to hold back lagging drug projects. Second, common ownership leads investors to restrict funding to lagging startups. And last, common ownership leads these VC investors to redirect innovation at startups that have fallen behind.

“If the firms instead have different owners, they fail to internalize the negative spillovers they impose on each other,” the paper stated. “The lagging project is therefore likely to continue, even if it is socially suboptimal.”

The paper documents the case of New England Associates (NEA), a VC firm that in 2012 had invested in two Boston-based startups, Intarcia and Rhythm Pharmaceuticals, which were in Phase I clinical trials of their drugs to treat obesity. By December 2012, Rhythm’s project moved from Phase I to Phase II, gaining an edge over Intarcia’s project. NEA subsequently cut off its funding to Intarcia, which abandoned its obesity drug project and shifted its focus to diabetes treatments. This case fits well into study’s overall pattern: Some VCs use a “horse race” investment strategy where they invest in closely competing startups, wait for one to gain an edge, and then reduce funding to the lagging startup while redirecting its innovation.

Common ownership is “way more common” in the pharmaceutical industry than one would have expected, Taylor said, noting that 39% of startups the study covered share a VC in common with a close competitor. “If you’re the founder of a pharma startup, it’s important to know whether your VC investors are also invested in your competitors. According to our results, common ownership can influence whether your funding gets cut off in the future and whether your drug projects make it through clinical trials.”

The study identified other outcomes when VCs have common ownership of startups:

When a common VC abandons a startup, other VCs often do not step in to fill the financing hole.
The results on financing patterns are stronger for VCs with larger ownership stakes in a startup since they have stronger control rights, making it more feasible for them to hold back projects.
The results are stronger also for less-diversified VCs and for lagging and pioneering projects that have similar technologies or belong to a narrowly defined drug category.
Common owners redirect lagging firms’ innovation activities toward new projects in non-overlapping categories and form financing alliances with large pharma companies in those categories.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Why Start-ups Fail

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 19 May 2021 09:11:41 GMT

Harvard Business School highlights six patterns that doomed ventures.

Most start-ups don’t succeed: More than two-thirds of them never deliver a positive return to investors. But why do so many end disappointingly? That question hit me with full force several years ago when I realized I couldn’t answer it.

As Tom Eisenmann said "...That was unnerving. For the past 24 years, I’ve been a professor at Harvard Business School, where I’ve led the team teaching The Entrepreneurial Manager, a required course for all our MBAs. At HBS I’ve also drawn on my research, my experiences as an angel investor, and my work on start-up boards to help create 14 electives on every aspect of launching a new venture. But could I truly teach students how to build winning start-ups if I wasn’t sure why so many were failing? ... I became determined to get to the bottom of the question."

Eisenmann interviewed or surveyed hundreds of founders and investors, read scores of first- and third-person published accounts of entrepreneurial setbacks, and wrote and taught more than 20 case studies about unsuccessful ventures. The result of theresearch is a book, Why Startups Fail , identifing patterns that explain why a large number of start-ups come to nothing.

The findings go against the pat assumptions of many venture capital investors. If you ask them why start-ups fall short, you will most likely hear about “horses” (that is, the opportunities start-ups are targeting) and “jockeys” (the founders). Both are important, but if forced to choose, most VCs would favor an able founder over an attractive opportunity. Consequently, when asked to explain why a promising new venture eventually stumbled, most are inclined to cite the inadequacies of its founders—in particular, their lack of grit, industry acumen, or leadership ability.

Putting the blame on the founders oversimplifies a complex situation. It’s also an example of what psychologists call the fundamental attribution error—the tendency for observers, when explaining outcomes, to emphasize the main actors’ disposition and for the main actors to cite situational factors not under their control—for example, in the case of a failed start-up, a rival’s irrational moves.

Putting scapegoating aside, six patterns of failure have been indentified. Let's focus on two of them, for two reasons:

First, they’re the most common avoidable reasons why start-ups go wrong. We are not interested in clearly doomed ventures with no chance of success or even promising start-ups that were felled by unexpected external forces such as the Covid-19 pandemic. Rather, We've focused on ventures that initially showed promise but subsequently crashed to earth because of errors that could have been averted.
Second, the two patterns are the most applicable to people launching new ventures within larger companies, government agencies, and nonprofits, which makes them especially relevant.

Good Idea, Bad Bedfellows

VCs look for founders with the right stuff: resilience, passion, experience leading start-up teams, and so forth. But even when such rare talent captains a new venture, there are other parties whose contributions are crucial to it. A broad set of stakeholders, including employees, strategic partners, and investors, all can play a role in a venture’s downfall.

Indeed, a great jockey isn’t even necessary for start-up success. Other members of the senior management team can compensate for a founder’s shortcomings, and seasoned investors and advisers can likewise provide guidance and useful connections. A new venture pursuing an amazing opportunity will typically attract such contributors—even if its founder doesn’t walk on water. But if its idea is merely good, a start-up may not become a talent magnet.

Consider the case of Quincy Apparel. In May 2011 two former students of mine, Alexandra Nelson and Christina Wallace, came to me for feedback on their start-up concept. They identified an unmet customer need: Young professional women had a hard time finding affordable and stylish work apparel that fit them well. Nelson and Wallace devised a novel solution: a sizing scheme that allowed customers to specify four separate garment measurements (such as waist-to-hip ratio and bra size)—akin to the approach used for tailoring men’s suits.

Following the lean start-up method, Nelson and Wallace then validated customer demand using a textbook-perfect minimum viable product, or MVP—that is, the simplest possible offering that yields reliable customer feedback. They held six trunk shows at which women could try on sample outfits and place orders. Of the 200 women who attended, 25% made purchases. Buoyed by these results, the cofounders quit their consulting jobs, raised $950,000 in venture capital, recruited a team, and launched Quincy Apparel. They employed a direct-to-consumer business model, selling online rather than through brick-and-mortar stores. At this point I became an early angel investor in the company.

Initial orders were strong, as were reorders: An impressive 39% of customers who bought items from Quincy’s first seasonal collection made repeat purchases. However, robust demand required heavy investment in inventory. Meanwhile, production problems caused garments to fit poorly on some customers, resulting in higher-than-expected returns. Processing returns and correcting production problems put pressure on margins, rapidly depleting Quincy’s cash reserves. After Quincy tried and failed to raise more capital, the team trimmed the product line, aiming to simplify operations and realize efficiencies. However, the business lacked enough funding to prove out the pivot, and Quincy was forced to shut down less than a year after its launch.

So why did Quincy fail?

Quincy’s founders had a good idea. The venture’s value proposition was appealing to target customers, and the business had a sound formula for earning a profit—at least over the long term, after shaking out the bugs in production. The team had credible projections that customers in priority segments, who’d accounted for more than half of Quincy’s sales, would each have a lifetime value of over $1,000—well in excess of the $100 average cost to acquire a new customer. (Quincy’s out-of-pocket marketing costs were kept low by social-network-fueled word of mouth and enthusiastic media coverage.)

Were Wallace and Nelson simply poor jockeys? Temperamentally, their fit with the founder role was good. They were sharp and resourceful and had complementary strengths. Wallace, who was responsible for marketing and fundraising, had a big vision and the charisma to sell it. Nelson, who led operations, was deliberate and disciplined. However, the founder team wobbled in two important ways. First, unwilling to strain their close friendship, Wallace and Nelson shared decision-making authority equally with respect to strategy, product design, and other key choices. This slowed their responses when action was required. Second, neither founder had experience with clothing design and manufacturing.

Apparel production entails many specialized tasks, such as fabric sourcing, pattern making, and quality control. To compensate for their lack of industry know-how, the founders hired a few apparel company veterans, assuming that they’d fill multiple functions—as jack-of-all-trades team members do in most early-stage start-ups. However, accustomed to the high levels of specialization in mature apparel companies, Quincy’s employees weren’t flexible about tackling tasks outside their areas of expertise.

Quincy outsourced manufacturing to third-party factories, which was not unusual in the industry. But the factories were slow to meet production commitments for entrepreneurs who had no industry reputation, required unusual garment sizing, and placed small orders. This meant shipping delays for Quincy.

Investors also played a role in Quincy’s demise. The founders had aimed to raise $1.5 million but managed to secure only $950,000. That was enough to fund operations for two seasonal collections. Before launching, the founders had correctly assumed that at least three seasons would be needed to fine-tune operations. Quincy had some traction after two seasons but not enough to lure new backers, and the venture capital firms that had provided most of its money were too small to commit more funds. Furthermore, the founders were disappointed with the guidance they got from those VCs, who pressured them to grow at full tilt—like the technology start-ups the investors were more familiar with. Doing so forced Quincy to build inventory, burning through cash before it had resolved its production problems.

In summary, Quincy had a good idea but bad bedfellows: Besides the founders, a range of resource providers were culpable in the venture’s collapse, including team members, manufacturing partners, and investors.

Could this outcome have been avoided? Perhaps. The founders’ lack of fashion industry experience was at the root of many problems. It took time for Wallace and Nelson to master the complexities of apparel design and production. Without industry connections, they couldn’t leverage their professional networks to recruit team members or count on past relationships with factory managers to ensure prompt delivery. And without an industry track record, they had difficulty finding investors willing to bet on first-time founders.

An ideal solution would have been to bring in another cofounder with apparel industry experience. Nelson and Wallace tried to do this, without success. They did have some advisers who could offer guidance—but adding more would have helped. In a postmortem analysis, Quincy’s founders also concluded that they could have sidestepped operational problems by outsourcing their entire design and production process to a single factory partner. Likewise, rather than raising funds from venture capital firms, they could have sought financial backing from a clothing factory. A factory with an equity stake in Quincy would have expedited its orders and worked harder to correct production problems. Also, the factory owners would have known how to pace the growth of a new apparel line, in contrast to Quincy’s VCs, who pressured the team for hypergrowth.

Quincy’s troubles shed some light on the attributes that may make start-ups vulnerable to this particular failure pattern. Entrepreneurs’ lack of industry experience will be especially problematic when large, lumpy resource commitments are required, as they are in apparel manufacturing: Quincy’s founders had to design a multistep product process from scratch, and revising such a process is disruptive once it’s in place. Another factor was ever-shifting fashion trends; the founders had to commit to garment designs and then build inventory for an entire collection many months before it went on sale.

With such challenges, learning by doing can result in expensive mistakes. Compounding the pressure, investors prefer to mete out capital one chunk at a time, waiting to see if the business can stay on the rails. If the start-up stumbles or stalls, follow-on financing may not be forthcoming from existing investors, and potential new investors will be scared off. Pivoting to a better solution isn’t feasible when it requires large amounts of capital along with weeks or months to see if new approaches are working. In that situation entrepreneurs have no room for big errors, but a lack of industry experience makes missteps all the more likely.

False Starts

I have long been an apostle of the lean start-up approach. But as I dug deeper into case studies of failure, I concluded that its practices were falling short of their promise. Many entrepreneurs who claim to embrace the lean start-up canon actually adopt only part of it. Specifically, they launch MVPs and iterate on them after getting feedback. By putting an MVP out there and testing how customers respond, founders are supposed to avoid squandering time and money building and marketing a product that no one wants.

Yet by neglecting to research customer needs before commencing their engineering efforts, entrepreneurs end up wasting valuable time and capital on MVPs that are likely to miss their mark. These are false starts. The entrepreneurs are like sprinters who jump the gun: They’re too eager to get a product out there. The rhetoric of the lean start-up movement—for example, “launch early and often” and “fail fast”—actually encourages this “ready, fire, aim” behavior.

The online dating start-up Triangulate experienced this syndrome in 2010. Its founder, Sunil Nagaraj, had originally intended to build a matching engine—software that Triangulate would license to existing dating sites such as eHarmony and Match. The engine would automatically extract consumers’ profile data—with their permission—from social networks and media sites such as Facebook, Twitter, Spotify, and Netflix. The engine would then use algorithms to pair up users whose tastes and habits suggested that they might be romantically compatible. But VCs wouldn’t back the plan. They told Nagaraj, “Come back after you’ve signed a licensing deal.”

To prove to potential licensees that the matching engine worked, Nagaraj decided to use it to power Triangulate’s own dating site, a Facebook app that would also leverage the rich user data available to Facebook’s platform partners. VCs now showed interest: Nagaraj raised $750,000 and launched a dating site called Wings. The site was free to use and earned revenue from small payments made by users who sent digital gifts or messages. Wings soon became Triangulate’s main event; the licensing plan went on the back burner.

Wings automatically populated a user’s profile by connecting to Facebook and other online services. It also encouraged users to invite their friends to the site as “wingmen” who could vouch for them—and provide a viral boost to the site’s growth. Less than a year after launching Wings, however, Nagaraj’s team abandoned both the matching engine and the wingman concept. Users found more value in recommended matches that were based on potential partners’ physical attractiveness, proximity, and responsiveness to messages—criteria routinely employed by existing dating sites. The wingman role, meanwhile, was not delivering hoped-for virality and made the site cumbersome to navigate. Furthermore, many users were uncomfortable making their dating life an open book to their friends.

A year after launch, Wings’ user base was growing, but user engagement was much lower than expected. As a result, revenue per user fell far short of Nagaraj’s original projections. Also, with limited virality, the cost of acquiring a new user was much higher than his forecast. With an unsustainable business model, Nagaraj and his team had to pivot once again—this time, with cash balances running low. They launched a new dating site, DateBuzz, that allowed users to vote on elements of other users’ profiles—before seeing their photos. This addressed one of the biggest pain points in online dating: the impact of photos on messaging. On a typical dating site, physically attractive individuals get too many messages, and other users get too few. DateBuzz redistributed attention in ways that boosted user satisfaction. Less-attractive individuals were contacted more often, and attractive users still got plenty of queries.

Despite this innovation, DateBuzz—like Wings—had to spend far more than it could afford to acquire each new user. Lacking confidence that a network effect would kick in and reduce customer acquisition costs before cash balances were exhausted, Nagaraj shut down Triangulate and returned $120,000 to investors.

So why did Triangulate fail?

The problem was clearly not with the jockey or his bedfellows. Nagaraj had raised funds from a topflight VC and had recruited a very able team—one that could rapidly process user feedback and in response iterate in a creative and nimble manner. Weak founders rarely attract strong teams and smart money. This was not a case of “right opportunity, wrong resources,” as with Quincy’s failure. Rather, Triangulate’s demise followed the opposite pattern: “wrong opportunity, right resources.”

A clue about the cause of Triangulate’s failure lies in its three big pivots in less than two years. On one hand, pivots are foundational for lean start-ups. With each iteration, Nagaraj’s team had heeded the “fail fast” mantra. The team also followed the principle of launching early and often—putting a real product into the hands of real customers as fast as possible.

But there’s more to the lean start-up approach than those practices. Before entrepreneurs begin to build a product, lean start-up guru Steve Blank insists, they must complete a phase called “customer discovery”—a round of interviews with prospective customers. (See “Why the Lean Start-up Changes Everything,” HBR, May 2013.) Those interviews probe for strong, unmet customer needs—problems worth pursuing. In Nagaraj’s postmortem analysis of Triangulate’s failure, he acknowledged skipping this crucial step. He and his team failed to conduct up-front research to validate the demand for a matching engine or the appeal of the wingman concept. Nor did they conduct MVP tests akin to Quincy’s trunk shows. Instead they rushed to launch Wings as a fully functional product.

By giving short shrift to customer discovery and MVPs, Triangulate’s team fell victim to a false start—and turned the “fail fast” mantra into a self-fulfilling prophecy. If the team members had spoken to customers at the outset or tested a true MVP, they could have designed their first product in ways that conformed more closely to market needs. By failing with their first product, they wasted a feedback cycle, and time is an early-stage entrepreneur’s most precious resource. With the clock ticking, one wasted cycle means one less opportunity to pivot before money runs out.

Why do founders like Nagaraj skip up-front customer research? Entrepreneurs have a bias for action; they’re eager to get started. And engineers love to build things. So entrepreneurs who are engineers—like Nagaraj and his teammates—often jump into creating the first version of their product as fast as they can. Furthermore, at the risk of stereotyping, I’d offer that many engineers are simply too introverted to follow Blank’s advice and get out of the building to learn from prospective customers.

Founders without technical training also fall victim to false starts. They hear repeatedly that having a great product is crucial, so they bring engineers on board as soon as they can. Then, feeling pressure to keep those expensive engineers busy, they rush their product into development.

The good news is that false starts can easily be avoided by following a structured, three-step product design process.

Problem definition . Before commencing engineering work, entrepreneurs should conduct rigorous interviews with potential customers—at which they resist the temptation to pitch their solutions. Feedback on possible solutions will come later; instead the focus should be on defining customers’ problems. Also, it’s important to interview both likely early adopters and “mainstream” prospects who may be inclined to purchase later. Success will hinge on attracting both groups, whose needs may differ. If their needs do vary, entrepreneurs will have to take the differences into account when formulating a product road map. In addition, entrepreneurs should conduct a competitive analysis, including user testing of existing solutions, to understand the strengths and shortcomings of rival products. Likewise, surveys can help start-up teams measure customer behaviors and attitudes—helpful data when segmenting and sizing the potential market.
Solution development. Once entrepreneurs have identified priority customer segments and gained a deep understanding of their unmet needs, the team’s next step should be brainstorming a range of solutions. The team should prototype several concepts and get feedback on them through one-on-one sessions with potential customers. Most teams start with crude prototypes, reject some and iterate, and then refine the ones that seem promising, gradually producing “higher fidelity” versions that more closely resemble the future product in functionality and look and feel. Prototype iteration and testing continue until a dominant design emerges.
Solution validation. To evaluate demand for the favored solution, the team then runs a series of MVP tests. Unlike the prototype review sessions during step 2—conducted across the table with a single reviewer—an MVP test puts an actual product in the hands of real customers in a real-world setting to see how they respond. To avoid waste, the best MVPs have the lowest fidelity needed to get reliable input—that is, they provide no more “looks like” polish and “works like” functionality than are strictly necessary. Early MVP tests may take things further, assessing demand for a planned product through a Kickstarter campaign or by soliciting letters of intent to purchase from business-to-business customers.Success with the product design process may require a shift in the founders’ mindset. At a venture’s outset many entrepreneurs have a preconceived notion of the customer problems they’ll address and the solutions. They may fervently believe they’re on the right path. But during the product design process, they should avoid being too emotionally attached to a specific problem-solution pairing. Entrepreneurs should stay open to the possibility that the process will uncover more-pressing problems or better solutions.

Maintaining Balance

Of course, there is no way for founders to know which deadly trap they may face as they launch. Familiarizing oneself with these two dominant failure patterns can help. But so too can understanding why they afflict start-ups so frequently.

Part of the answer is that the behaviors that conventional wisdom holds make a great entrepreneur can paradoxically increase the risk of encountering these failure patterns. It’s important for an entrepreneur to maintain balance. Guidance based on conventional wisdom is good—most of the time—but it shouldn’t be followed blindly. Consider the following advice given to many first-time founders and how it can backfire:

Just do it! Great entrepreneurs make things happen and move fast to capture opportunity. But a bias for action can tempt an entrepreneur to truncate exploration and leap too soon into building and selling a product, as I’ve explained. When that happens, founders may find themselves locked prematurely into a flawed solution.

Be persistent! Entrepreneurs encounter setbacks over and over. True entrepreneurs dust themselves off and go back at it; they must be determined and resilient. However, if persistence turns into stubbornness, founders may have difficulty recognizing a false start for what it is. They likewise may be reluctant to pivot when it should be clear that their solution isn’t working. Delaying a pivot eats up scarce capital, shortening a venture’s runway.

Bring passion! A burning desire to have a world-changing impact can power entrepreneurs through the most daunting challenges. It can also attract employees, investors, and partners who’ll help make their dreams a reality. But in the extreme, passion can translate into overconfidence—and a penchant to skip critical up-front research. Likewise, passion can blind entrepreneurs to the fact that their product isn’t meeting customer needs.

Bootstrap! Because resources are limited, entrepreneurs must conserve them by being frugal and figuring out clever ways to make do with less. True enough, but if a start-up cannot consistently deliver on its value proposition because its team lacks crucial skills, its founders must decide whether to hire employees with those skills. If those candidates demand high compensation, a scrappy, frugal founder might say, “We’ll just have to do without them”—and risk being stuck with bad bedfellows.

Grow! Rapid growth attracts investors and talent and gives a team a great morale boost. This may tempt founders to curtail customer research and prematurely launch their product. Also, fast growth can put heavy demands on team members and partners. If a team has bad bedfellows, growth may exacerbate quality problems and depress profit margins.

It’s fashionable in start-up circles to speak glibly about failure as a badge of honor or a rite of passage—just another phase of an entrepreneur’s journey. Perhaps doing so is a coping mechanism, or perhaps failure’s ubiquity inures those in the business world to its true human and economic costs.

Raw emotions are always on display: anger, guilt, sadness, shame, and resentment. In some cases the founders were in denial; others just seemed depressed. Who could blame them, after having had their dreams dashed and their self-confidence shattered? There’s no way to avoid the fact that it hurts. It also can destroy relationships.

Failure also takes a toll on the economy and society. A doomed venture ties up resources that could be put to better use. And it acts as a deterrent to would-be entrepreneurs who are more risk-averse, have financial obligations that make it hard to forgo a paycheck, or face barriers when raising capital—which is to say, many women and minorities. To be sure, failure will (and should) always be a reality for many entrepreneurs. Doing something new with limited resources is inherently risky. But by recognizing that many failures are avoidable and follow the same trajectory, we can reduce their number and frequency. The payoff will be a more productive, more diverse, and less bruising entrepreneurial economy.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Growth strategies for SMEs

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 12 May 2021 08:54:20 GMT

There are three potential approaches to maximize growth in the post-covid world

In 2021, midsize companies are ready to grow by taking advantage of pent-up demand. But how can they fund the growth they seek? Many of these companies have the solution at hand but don’t know it. Data and research show that middle-market companies can fund much of their growth through efficiency. Often, however, companies look for efficiency by cutting costs in ways that actually defund growth and leave an enterprise anemic and stressed. First, look at the complete productivity equation. Second, manage your capital properly. Finally, take advantage of three unique cost-management opportunities.

A few months into 2021, middle-market companies are eager for growth and sense unique opportunities, including the chance to profit from pent-up demand and the momentum of a rebounding economy — and, for some, to take share from or acquire a weakened rival. The International Monetary Fund is forecasting U.S. economic growth at 6.4% this year ; companies would be foolish not to position themselves to catch that kind of tailwind.

But how can they fund the growth they seek? Even in normal times, middle-market companies are reluctant to dilute equity and anxious about getting out over their skis by taking on debt. Today, almost half of middle-market executives say that coping with Covid-19 has made them more risk averse than before . Many drew on lines of credit last year or got federal Payroll Protection Plan support; they would prefer not to call on their banks again. But without capital, they risk missing out on a historic growth opportunity.

Many of these companies have the solution at hand but don’t know it. Data and researches show that middle-market companies can fund much of their growth through efficiency. Indeed, increasing productivity and efficiency, our 2021 Sentiment Study shows, is the number-two strategic priority for middle-market CFOs, just after growth. The trick is to make them work together, rather than have the pursuit of one handicap the other. Ours and members’ experience shows that growth and efficiency can be allies, not antagonists. Properly managed, the push for efficiency can free up capital and direct it to where it will create revenue, generate strategic growth options, and increase enterprise value.

CFO Alliance faced that challenge from their position overseeing finance for a midsize trucking company with a fleet of more than 500 tractors and upwards of 1,500 trailers. As an essential business with broad diversification of customers across industries, it grew moderately in 2020 and entered 2021 with ambitious plans, supported by strong demand. The company had enough capital, but not enough human capital thanks to a shortage of drivers — a problem which affects the whole industry and has been intensified by increased regulation and the introduction of the Drug and Alcohol Clearinghouse. But the impact and imperative are the same: The company cannot reach its growth goals without big gains in productivity and efficiency. Every dollar lost to inefficiency is a dollar that could be used for growth.

Often, however, companies look for efficiency by cutting costs in ways that actually defund growth and leave an enterprise anemic and stressed. This experience has revealed three approaches that feed efficiency and growth at the same time.

Attack all the variables in the productivity equation.

In many companies, the search for productivity growth becomes a cost-cutting exercise. But productivity is simply outputs divided by inputs. And inputs aren’t just labor inputs, but also capital equipment, inventory, technology investments, materials, and more. Looking at the complete productivity equation encourages leaders to discover opportunities to maximize outputs, as well as reduce inputs, and to get more from all their assets, not just the workforce.

One way the trucking company gets more from less is to focus on profitable customers. Marginal customers syphon off a surprisingly large amount of time, energy, and assets and deliver less to the top line. The company now separates customers into “strategic” and “transactional” groups and makes sure the former gets first call on its resources. By linking sophisticated load-planning software with its ledger of customer commitments, the company is able to ensure that customer service teams don’t inadvertently tie up capacity that could create more value if it were used for a different opportunity.

Raise your working capital game.

The cheapest capital you can get is money that’s tied up in bills you pay too soon, receivables you collect too slowly, and inventory you don’t need. Middle-market companies underestimate how much working capital they use. Data for publicly held middle-market companies reveals a four-times performance difference between the 25th and 75th percentiles in payables, receivables, and inventories. For example, a $100-million-in-revenue materials company that moves from the median to the 75th percentile would free up more than $17 million in capital every year — interest-free money to use for expansion or any other purpose.

Mismanaged working capital can starve a company. We know one company that decided to pay its bills in 25 days instead of its usual 40 as a favor to pandemic-stressed suppliers. They soon discovered that they were running out of cash. Middle-market executives often fear that they’re at the mercy of trading partners — forced to accept discounts in return for timely payment from big-company customers, for example. In fact, middle market companies have more leverage than they think, especially if they’re providing critical components or raw materials.

Understanding how these levers work may stretch the skills and knowledge of CFOs and their teams. It’s no wonder CFOs in the AchieveNEXT 2021 Sentiment Study told that financial planning and analysis is their number-two skills gap in their departments, just after critical thinking. Global enterprises often have teams of hundreds doing this kind of analysis; in the middle market, the job might fall to the CFO and an overtaxed analyst or two. In addition, if the finance team lacks the right capabilities and tools, it can spend a significant amount of time on fixing data issues, creating reports, undertaking strategic planning, and explaining variances after the fact instead of driving real-time analytics for stronger insights and more informed business decisions.

Seize the moment to get creative about costs.

This year offers three unique cost-management opportunities. First, take a look at all the improvisation and adaptation the pandemic provoked (“pivoting,” in the jargon du jour) to see what to keep, what to toss, and what to clean up. (Does anybody know how many Zoom accounts your company has?) Sales teams chafed at pandemic travel restrictions, but many have adapted brilliantly to virtual selling and new modes of lead generation, realizing both sales gains and reduced customer acquisition costs. Among clients of Delancey Street Partners, an investment bank focused on the middle market, many CFOs are having serious discussions with sales and marketing departments to determine if these new practices can be retained or expanded in an attempt to capture a meaningful portion of these savings permanently.

Second, rethink real estate needs and act fast on what you learn. According to the McKinsey Global Institute, in the third quarter of 2020, office vacancy rates in New York were 32% higher than they had been a year before; they were 23% higher in Chicago and 12% higher in Los Angeles. Now is the time to negotiate lower rents, but, more important, it’s a chance to examine long-term real estate needs — before the market rebounds in landlords’ favor and your employees settle into a less-than-productive “old normal.”

Third, get more strategic about digitalization. Many middle-market companies are stuck in what we call “first-generation digitalization,” such as automating routine work. But there are at least two other stages. One is digital integration across functions, allowing companies to optimize operations, HR, logistics, marketing, and other systems as a whole. For example, by tying together IT stacks that once were separate, the trucking company is now able to coordinate customer commitments, load planning, and route optimization — and even buy commodity hedges on fuel costs. Optimizing the whole system generates productivity gains far above what can be attained by improving each element alone. Not long ago, tools like these were out of reach for middle-market companies; today, one out of seven middle-market CFOs see cross-functional IT integration as the biggest challenge and opportunity they face.

Even bigger productivity gains can be found by imagining how digitalization can transform your business model and balance sheet as well as your income statement — for example, by offloading assets like warehouses and fleets, IT servers, and swing production capacity.

Each of these approaches to productivity improvement can feed and fund growth more easily than mere cost cutting. They have an additional advantage: They significantly enhance enterprise value, which will put your company in a better position if you choose to fund expansion with outside capital, or if you choose to enter the M&A market as a buyer or seller. Capturing productivity gains, working capital management efficiencies, and other operational improvements are best done well in advance of approaching the capital markets. In Delancey Street Partners’ experience, clients that are able to show several quarters of enhanced profitability due to material changes in productivity, working capital efficiencies, etc., are more likely to get credit for these improvements during a sales process or capital raise. The ability to evidence two to three quarters of improvement gives potential investors and buyers confidence that these efficiencies are sustainable and not short-term practices put in place as transaction-related window dressing.

None of these things can be done in a vacuum or by functional leaders acting by themselves. Whether managing working capital, reshaping cost structures, or driving digitalization, CEOs and CFOs from middle-market enterprises must overcome misalignment among leadership, build buy-in across their organizations, and organize this effort for growth by focusing on change management. Achieving buy-in is not a one-time exercise for them, either. They must communicate to all stakeholders the growth strategy, the value drivers of change, the phases of the digital journey, and the expected outcomes. They must clearly demonstrate and communicate success through validation points along this productivity journey. The long-term and ongoing culture change activities and interventions that will come will help shape a culture that celebrates cost management and growth simultaneously.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How the first round of funding shapes a startup’s future

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 03 May 2021 12:54:27 GMT

Entrepreneurs rarely consider who will ultimately own their startups—and what that means for founders—when they court venture capitalists. New research suggests they should.

A startup funded by VCs who tend to work with the same group of partners are more likely to seek a faster exit by selling the company to a larger one. In contrast, startups funded by a VC syndicate with less familiar co-investors are most likely to exit through a potentially splashy IPO that could let founders retain more control, says Harvard Business School professor Rory McDonald , who evaluated more than 42,000 new ventures in a new study.

For founders just trying to keep a fledgling business going, a cash injection—from any source—can make all the difference in launching a critical product. However, founders who don’t scrutinize investors’ relationships might later find themselves relinquishing their ventures to a larger acquirer or facing the glare of earnings expectations as a publicly traded company. They should understand what’s at stake with both outcomes before it’s too late.

When I talk to entrepreneurs about this, the lightbulb goes on,” says McDonald, the Thai-Hi T. Lee Associate Professor. “They’re thinking ‘I would rather have money than not have money,’ right? Or ‘We'd rather have a high-status venture capitalist that everybody has heard of versus not.’ But there’s this other thing lurking out there that people are not really paying attention to. It turns out from our research, it matters a lot.”

The Analysis

McDonald, along with Columbia University’s Dan Wang and Emily Cox Pahnke from the University of Washington, examined 71,624 funding rounds for 42,027 new ventures and 20,142 investors between 1982 and July 2014 to identify relationships among venture capitalists during a startup’s first round of funding. They concentrated on the first round because that relationship is crucial in setting the direction a startup will take.

The team started by combing almost 11,000 listings in Crunchbase, a database of startups and investors, to analyze collaboration patterns. The authors also sorted through press releases, news articles, and regulatory filings to augment and verify the data.

Of the Crunchbase startups examined, 16 percent—1,689 companies—were acquired. VC firms that frequently collaborate with like-minded investors to ensure stable returns sold their holdings 3.4 years after the first funding round, on average.

The researchers called these lower-profile exits “focused successes” because they deliver a venture to a well-resourced owner while mostly recouping backers’ investments. However, they’re not without complication: A startup’s founders might feel pressure to yield to the plans of an aligned investor group. Founders will also likely give up control of the startup’s vision or step back altogether after a larger company takes possession. In 144 of these transactions, the deal effectively closed a failing business.

Another 2.9 percent of the companies examined, or 317 ventures, went public through “broadcast successes.” Venture capitalists who haven’t worked closely together tend to hold on to startups longer—four and a half years, on average—before moving toward a high-profile exit. While becoming a public company can bring a startup more attention and retain the original management team, the risk of failure is higher for ventures backed by such syndicates, the study says. Public companies also typically require more infrastructure and legal support to handle shareholder and regulatory demands.

“For a lot of entrepreneurs, it's just about the term sheet and how much equity they're retaining, and how much funding they're getting to survive to the next year,” Wang says. “But the structure, this kind of hidden variable, has huge implications. And it's something that's so easy to overlook and ignore, from the entrepreneur’s perspective.”

The researchers detailed their findings in The Past Is Prologue? Venture-Capital Syndicates’ Collaborative Experience and Start-Up Exits , a forthcoming study in the Academy of Management Journal.

Startup founders, pick your path

Past research about the degree of collaboration among investors offers mixed messages. On one hand, investors who have worked together might pool resources, provide more connections within an industry, and know how to steer a venture to a promising opportunity. On the other hand, a more diverse investor group might give founders more leeway in decision-making.

In either case, founders should understand the pros and cons of each scenario and know where they want to take their companies before committing to VC funds.

“Whether you go into this agreement with the intention of going for a big IPO, or looking for a quick sale, or through a niche opportunity, things can happen along the way,” Wang says. “What's more important than that is the structure of the relationships of your most valued advisors, which, in many ways, are the VCs.”

McDonald puts it another way: Consider the 2019 Academy Award winner for sound editing in 2019, Ford v Ferrari. Only judges from the sound editing branch of the Academy of Motion Picture Arts and Sciences vote in the category. Interest is confined to a small group of experts, much like a group of VCs used to talking to each other.

For the best picture category, though, all academy members vote. And far more people will remember that Parasite won for best picture. Sound editing is like a sale, whereas best picture is akin to an IPO.

“We asked a lot of VCs how they thought about acquisitions and IPOs, and a quote that stuck with me the most was: ‘Hey, the best outcome is money,’” Cox Pahnke says, noting most VC investments don’t yield any return. “‘So, if we get an acquisition that returns any money, that's actually amazing.’ Maybe we don't talk about it as much, but that's really important. Often, that's a better outcome for a founder.”

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Surprisingly shareholders are asking more sustainability

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 23 Apr 2021 13:05:48 GMT

Investors value higher transparency with respect to climate change risks and that disclosure tends to benefit disclosing companies. Put differently: Investors dislike uncertainty and are willing to pay a premium for less opaque companies.

Does shareholder activism induce firms to voluntarily disclose climate change risks? And how do markets respond to these disclosures? New research finds that the extent of climate-risk disclosure increases by approximately 4.6% for each environment-related proposal that is submitted, and that the effect rises to 6.8% when environmental shareholder activism is more effective is initiated by institutional shareholders with a long-term holding horizon. It also found that the stock market responds favorably to such disclosures, with a disclosing firm’s stock price increasing by 1.21% on average in the days following a disclosure.

Shareholders are becoming more vocal in demanding companies disclose the risks of climate change. In May 2019, the shareholders of BP voted overwhelmingly in favor of disclosure, and similar proposals have been accepted by the shareholders of Exxon Mobil , Occidental Petroleum , and PPL Corporation . This year, the proxy advisory firm ISS (Institutional Shareholder Services) expects a record-high number of these proposals. Despite their rising frequency, most shareholder proposals still receive little support at annual meetings.

Apart from the occasional news story about individual companies, little is known about whether this kind of shareholder pressure actually triggers greater disclosure of a company’s exposure to climate change risks. Does it work? And is there an impact on the markets?

To find out, a recent analysis studie d the effects of such activism on S&P 500 companies between 2010 and 2016. We analyzed a proprietary dataset from CDP (formerly, the Carbon Disclosure Project), which asks large public companies to disclose information about the risks and opportunities posed by climate change, their strategies to address them, and other environment-related information. Furthermore they studied a database from ISS that compiles information on shareholder proposals that were submitted to S&P 1,500 companies.

The analysis shows that shareholder activism — measured by the number of environment-related shareholder proposals submitted to a company — does induce firms to voluntarily disclose climate change risks. On average, the extent of climate-risk disclosure increases by approximately 4.6% for each environment-related proposal that is submitted. They also found that environmental shareholder activism is more effective when initiated by institutional shareholders with a long-term holding horizon: The effect rises from 4.6% to 6.8%.

We also documented that the stock market responds favorably to such disclosures. In the days following a shareholder-induced disclosure of climate-change risks, the disclosing firm’s stock price increases by 1.21% on average (on a market-adjusted basis). This suggests that investors value higher transparency with respect to climate change risks and that disclosure tends to benefit disclosing companies. Put differently: Investors dislike uncertainty and are willing to pay a premium for less opaque companies.

These findings have important implications. Understanding risk is of utmost importance for companies and shareholders. Yet, in many countries, including the U.S., the law does not compel companies to disclose their exposure to climate change risks. For example, the U.S. Securities and Exchange Commission (SEC) merely recommends that companies disclose and offers no guidance about what information should be provided. As a result, shareholders are often in the dark — they know little about their portfolio companies’ exposure to climate change risks or how those risks are being managed. The findings suggest that shareholders can elicit greater corporate transparency with respect to climate change risks.

More transparency might be on its way. Demands for climate disclosure are becoming increasingly sophisticated. The Task Force on Climate-related Financial Disclosures ( TCFD ) has developed recommendations (around four areas: governance, strategy, risk management, and metrics and targets) for helping businesses disclose climate-related financial information. And the SEC recently launched a review to assess whether regulations might be needed to provide investors with “more consistent, comparable, and reliable information” about climate change risks. Such mandatory disclosure would likely help improve the quantity and quality of disclosure, standardize it, and enable greater progress in the fight against climate change. To support such regulatory efforts, shareholders seeking information about the climate risk exposure of their portfolio companies can engage with government regulators to encourage mandatory disclosure , while also actively engage with their portfolio companies, file resolutions calling for greater transparency, and vote in favor of such proposals.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How SMEs are adapting to the new competitive scenario

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 14 Apr 2021 08:10:56 GMT

The forward-thinking midsize companies must understand the change in demand patterns and adapt through innovation.

The pandemic has accelerated change that will significantly affect demand trends, and forward-thinking midsize companies must understand the change in demand patterns and adapt through innovation. One midsize company, Kern Oil & Refining Co., has an impressive track record of adapting to major changes in demand. They’ve been successful because of three main capabilities: investments in innovation, extensive external information-gathering, and a culture of collaboration. Midsize companies in every industry can have a big role in making the future better. But their work must begin now, fueled by systematic investment in those three areas.

Because they’re smaller than large companies and less encumbered by bureaucracy, midsize companies have an opportunity to take advantage of the market changes that will persist post-Covid (some permanently). The ones that delay risk permanently losing their market position. Big companies can inadvertently stymie innovation , whereas midsize firms have an ideal mix of sufficient financial strength, diverse customer bases, and nimbleness.

To be successful in a changed landscape, midsize companies must harness those advantages and commit to these three capabilities:

Continual investments in innovation (systems, people, and capital)
Extensive information gathering throughout the marketplace to discover opportunities and threats
Innovation managed as a team sport — not as eureka moments of brilliant individuals

Here’s how these capabilities have allowed one midsize company to pivot in the face of new and changing demands.

1) Investing in Innovation

Kern Oil & Refining Co. , founded in 1934, is an independent oil refinery in Bakersfield, California. With 155 employees, it’s a quarter of the size of the average refining company in the U.S. What’s more, it doesn’t extract oil from the ground or distribute it. It only processes it in its refinery.

While it pales in comparison to the likes of Chevron or Exxon, Kern uses its size and agility to its advantage. It purchases oil mostly from local producers in California’s Central Valley (Kern County). It refines and sells fuel at wholesale, mostly for use in the Central Valley. And it has become adept at developing and producing clean transportation fuels, such as renewable diesel and blended biodiesel.

On Sept 23, 2020, California Governor Gavin Newsom ordered that all new cars and passenger trucks in the state be zero-emission by 2035. One thing was clear: Refiners like Kern would have to evolve quickly to meet changing consumer patterns and an aggressive regulatory environment.

Kern had been here before. In 2007, when the U.S. Environmental Protection Agency issued new renewable fuel standards, company engineers had an idea. Could they make renewable diesel by co-processing tallow (i.e., animal fat)?

There was no guidance from the American Petroleum Institute on how to process tallow — nobody knew how to do it. And it would be risky to outsource the creation of an entirely new refining system on an unproven feedstock.

But due to the company’s size, its technical experts were close enough to ownership to get quick approval to experiment. So, Kern invested $75,000 in basic filtration equipment and a few tanks, and it used a spare pump to run a test batch. Samples from this initial batch were run through test engines to check emissions and gain EPA approvals. Kern then developed a more complex test protocol, processed a larger batch, and repeated the emissions testing, eventually ramping up into regular production at the end of 2009.

In the end, nine months after they started, Kern had invested about 1,000 hours of time into making this work, and it became the second U.S. refinery to make renewable diesel by co-processing tallow. Ongoing investment over the next few years represented approximately 30% of Kern’s typical year’s capital budget; co-processing tallow has substantially increased the cost of production. However, the resulting value of the renewable diesel justifies that additional ongoing expenditure.

Disruptive innovation means pursuing potential marketplace changes before they’re known to be feasible and encouraging experiments that fail more often than they succeed. It means spending hundreds of hours thinking and working on initiatives that may or may not blossom. It’s very hard to mix this sort of work with the urgencies of daily execution.

Midsize companies have money to invest, but not frivolously. At midsize scale, innovation must be a process whereby opportunities are discovered, evaluated, and incrementally de-risked. This is often called an “innovation funnel.” Opportunities can be categorized, analyzed, and valued. The progress of innovation efforts overall can be measured and assessed. With a systemic approach, budgets can be used to direct capital toward the right objectives. Midsize companies often struggle to spend on innovation, feeling uncomfortable spending capital on projects that have no clear return on investment. But on-schedule investment is required to adapt while unmet demand exists.

Kern’s projected path toward that adaptation includes opening up two positions to support a faster pace of innovation: A process engineer to assist in evaluating new feedstocks and technologies, and a fuels and climate change specialist to navigate the regulatory process. They’re developing budget detail to allocate dedicated funds toward carbon footprint reductions, which can have clearer payback periods, and more disruptive innovation focused on negative carbon intensity fuel development via feedstocks like agricultural or municipal waste.

2) Gathering Valuable Marketplace Intelligence

Too many smart people think that the best ideas start within their mind or within their companies. But that’s one of the most inefficient approaches to innovation. The best way to start is with what the market needs but can’t yet find. Curtis R. Carlson, former CEO of SRI International, a famous research center, developed and popularized an approach called NABC ; N, the first and most important step, stands for “need.” Identifying shifting demand and new needs comes from externally facing activity, such as talking with customers, suppliers, and other players in industry and scrutinizing what moves competitors are making.

Typically, most midsize companies’ external activities have to do with selling and marketing, telling the world about their wares. Innovation’s external activities are much more about listening and looking for early signs of change, then homing in to listen even more carefully. Many innovations are the result of partnering with other organizations with different core competencies, each of whom deliver part of the value chain.

Kern is considering several partnering opportunities focused on innovative equipment that converts wet organic wastes, forest and agricultural residues, and even municipal solid wastes into renewables. Kern brings value as the catalyst and incubator, helping pilot test technologies that can become “the next big thing” in the effort to produce cleaner-burning fuels. They network within the industry and with R&D organizations. Kern has assembled Team Yosemite, a group of six led by its head of renewables. The team is staffed by two process engineers, a supply-side expert, a fuels market expert, a regulatory specialist, and the senior VP of operations. Together, they rapidly prioritize projects on a renewables roadmap.

3) Prioritizing Collaboration

Innovation isn’t something that happens in isolation in some R&D lab — that’s basic research. For the most part, midsize companies are using existing know-how in new ways, which means cross-functional innovation teams are a must. Marketing is involved in understanding the market need; R&D tests; engineering builds the pilot test; manufacturing ensures feasibility; finance validates potential profitability; and sales collects customer feedback. For the early visioning stage of innovation, this team should be a handful of people who understand their department’s perspectives. For those innovations nearing execution, the teams will grow significantly.

But teamwork isn’t limited to those inside the company. More people, and likely different people, will be required. The existing teams in midsize companies are always busy and have ongoing job duties. While some companies employ people eager to think differently, there must be significant time dedicated to the innovation effort that’s walled off from normal day-to-day activities.

Teamwork between partners up and down the supply chain is critical. Few midsize companies solve for demand shifts on their own. Collaboration between separate entities requires negotiating how costs and benefits will be shared, role definitions, and timelines. They must be formalized in writing to justify investment and ensure alignment. In Kern’s case, any changes involve R&D, raw materials sourcing, plant and equipment modifications, government compliance, and sales. Kern prioritized collaboration by creating an oversight team to drive change and opened new positions dedicated to the effort, all with executive sponsorship from the CEO.

The pandemic has accelerated change that will significantly affect demand trends. Despite the step-up in urgent needs during the pandemic, such as safety and managing remote employees, forward-thinking midsize companies must understand the change in demand patterns and adapt through innovation.

Midsize companies in every industry can have a big role in making the future better. But their work must begin now, fueled by systematic investment in innovation, information-gathering activities, and teaming.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

A Collaborative framework between Sales and Finance is possible

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 31 Mar 2021 13:42:35 GMT

There are four strategies for focusing on customers and the customer experience by reducing friction between the CFO and sales leader.

The CFO and sales leader have significant impact on their company’s ability to deliver on its strategies through processes. Unfortunately, the relationship between finance and sales is often more confrontational than collaborative. Given CFOs’ expressed interest in spending more time understanding customers — and sales teams’ concerns about re-engaging and re-energizing relationships strained by the pandemic — now is the ideal time for these reluctant partners to get together to improve their ability to measure and grow the “relationship capital” on which sales and enterprise growth depend.

As highlighted by Ed Wallace (AchieveNEXT ) asking to CFOs " ... how many of you reduce your sales leader’s forecast 25% or more each month?", without exception, every hand went up, with many pointing to the sky, indicating that they need to discount the forecasts even more. After many sets of eyes stopped rolling and comments faded, next question was, “Why?” Here’s what I heard:

Our sales leader’s forecasting track record is inaccurate.
Our CRM system gives incomplete data or doesn’t connect with our other data, so we can’t really see what’s going on.
The pandemic has changed customer buying patterns, calling all forecasts into question.
Our goals aren’t aligned. The CFO is focused on cost and efficiency, the sales leader on revenue and growth.
We’re just different people. CFOs are trained skeptics, whereas sales leaders have to believe the next call will produce a deal.

Unfortunately, the experiences and the researches support the above, the result is that the relationship between finance and sales is often more confrontational than collaborative. The consequence: Internal friction that wastes energy that could produce the profitable growth both parties (and the CEO) want. Intramural friction is especially damaging for middle-market companies, whose processes are informal and rely on personal interaction.

Nonetheless we are convinced that both parties have nothing but the best intentions toward each other. I’ve also seen how dramatic the benefits of collaboration can be. For example, the CEO of a heavy equipment company in the Southwest of USA that grew sales from $325 million to over $600 million in two years said that the relationship and shared goals between the CFO and sales leader was the number-one reason for that extraordinary growth.

If ever there was a perfect time to reduce the friction between the CFO and sales leader, it’s now. After a year of caution and retrenchment, CFOs are as hungry for growth as their sales leaders — our studies show that, across the middle market, CFOs pick top-line growth over profitability by a nearly four-to-one margin . The straightest line to growth goes through the sales leader. A study by the National Center for the Middle Market shows that for midsize companies, the combination of sales force effectiveness and retaining profitable customers has more impact on growth than any other capability — 45% more than operating efficiency, 60% more than entering new markets, and more than two-and-a-half times more than innovation. It’s no wonder that more than 87% of CFOs tell us that they expect to spend much more time focusing on customers and the customer experience.

Now is the time, but what’s the path? To get meaningful results, CFOs and sales leaders should consider the following four strategies.

Invest in sales training and development. Training salespeople pays off quicker than almost any other expansion activity and is especially important now, because customer buying behaviors and the demographics of buyers are changing. Investment should be directed toward creating a “hybrid” sales force that’s as skilled at virtual selling as it is at face-to-face selling. Yet when CFOs are asked where they would put an extra dollar of sales force investment, they would allocate almost twice as much to putting more feet on the street than to training current staff and upgrading CRM software. This even though three out of five CFOs say their sales teams are concerned that Covid restrictions on meetings and travel will hurt their ability to meet targets — which again suggests that upskilling to hybrid sales models and tools might be the better investment.
Improve forecast accuracy and transparency by measuring the strength and value of customer relationships through business relationship assessments and CRM tools. Sixty percent of middle-market and emerging enterprise CFOs say they lack data to measure the value of customer relationships. Sales executives say much the same: 88% of sales executives agree that strong relationships have a significant impact on business, but only 24% say they use a formal and consistent process to create, sustain, and improve relationships, according to an internal study we commissioned from the Candice Bennett market research firm. Therefore, both parties have an incentive to develop shared measurements of relationship value and a process to increase it.
Focus on key strategic relationships. As a rule, a company’s best customer relationships are also its most profitable accounts. This makes them a natural focus for CFO/sales leader collaboration. Our research shows that even the best relationships function at less than half their potential, as measured by indicators like whether the buyer and seller talk to each other about strategy, share personal confidences, or spend extra time together. Together, CFOs and sales leaders can identify, measure, and support each other in getting more of the potential from these great customers.
Close the cultural gap between finance and sales by developing common data and — more importantly — common goals. Often, sales team incentives and targets are only loosely connected to the profitability goals CFOs pursue. Connecting tech stacks, agreeing on shared KPIs, and creating common dashboards would help finance and sales move from finger-pointing to handshaking. This is where upgrading or replacing CRM systems can help.

It’s important to do all of the above together. Each strategy can help on its own, but each one multiplies the benefits of the others; in particular, the impact of CRM investments will be much greater if accompanied by investments into relationships and sales training.

However, the most important strategy to reduce forecast friction and unlock growth potential can be found in the example I cited earlier, and it’s actually very simple: The CFO and sales leader should focus on and prioritize their own relationship first before the relationship between the finance and sales functions. Businesses are designed and driven by well-defined organization charts and processes, but when they stumble, it’s usually because of people, not processes. Weakness or lack of intention in relationships will blur the sharp lines of processes and hamper performance — again, especially in the middle market, where teams are small and a few people can make an enormous difference.

The CFO and sales leader have significant impact on their company’s ability to deliver on its strategies through processes. Given CFOs’ expressed interest in spending more time understanding customers — and sales teams’ concerns about re-engaging and re-energizing relationships strained by the pandemic — now is the ideal time for these reluctant partners to get together to improve their ability to measure and grow the “relationship capital” on which sales and enterprise growth depend. Therefore, it all starts with the relationship between the CFO and sales leader. The guiding principle should always be to value people before processes.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The midsize companies and the market capital

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 25 Mar 2021 19:06:40 GMT

OTC markets and technology can fix the historical issue

In UE, the midsize companies are the backbones of the ecoomic system, but when it comes to accessing capital — whether they’re looking for debt, an equity investment, an acquisition, or a company exit — they’ve been mostly left wandering in the dark. They struggle to get the attention of banks, investment banks, and other financial services providers — and worse, when they do make a connection, they often pay outsized fees compared to large companies. So how can middle-market companies and the many deep-pocketed capital providers overcome this capital transaction chaos? In my experience, supporting companies in developing growth strategy projects, US OTC capital market can be a real effective and efficent solution but further development in ICT technology can give an additional boost. The key to improving the velocity of successful capital transactions in the middle market is new tools that facilitate access to secure information exchange so that the parties on each side of a transaction can 1) get to know each other and 2) expeditiously gather, organize, and exchange critical information on a confidential basis.

Large companies have never had a problem with capital transactions. As long as they’re not on the verge of insolvency, there’s a relatively small, well-known universe of banks, private equity firms, conglomerates, and other institutional investors available to look at their deals, whether it’s an injection of new capital, a loan, or an outright sale of their company. Moreover, large companies have sophisticated senior financial executives on board, and they have access to the best third-party advisors that money can buy. Everyone wants a piece of a big deal.

On the other hand, the smaller cousins of these large corporations, middle-market companies, are not so fortunate. When it comes to capital markets, they’ve been mostly left wandering in the dark. Whether they’re looking for debt, an equity investment, an acquisition, or a company exit, midsize companies have, for the most part, been ignored. They struggle to get the attention of banks, investment banks, and other financial services providers — and worse, when they do make a connection, they often pay outsized fees compared to large companies. The internet made it easy for an individual to get a mortgage approved in 15 minutes or less, so why is it so hard to harness the same potential for the middle market?

Midsize Companies Are Underserved and Overcharged

With so many middle-market companies in play looking for capital and wanting to do deals, why aren’t capital providers and deal makers all over them? After all, there’s no shortage of capital available. At the end of 2020, it’s estimated that almost $750 billion of funding was available from middle-market investment sponsors — plenty of dry powder for loans, equity, and acquisitions — and that number is expected to climb to $1.7 trillion in 2021 . Yet only about $200 billion was spent on middle-market M&A in 2020 , leaving $500 billion on the table. A recent article in The Wall Street Journal reported that big banks across the country were loaded with cash and looking for more lending opportunities. So, there’s no shortage of money available to support loans, deals, and investments.

On the other side of the ledger, there are hundreds of thousands of middle-market companies with pent-up demand for loans, new equity investments, acquisitions, and company exits. However, unlike big companies, most middle-market firms tend to be sedentary when it comes to banking relationships. Historically, they’ve picked a lender and stuck with them, mostly out of inertia but often out of fear. Lack of financial sophistication, preoccupation with operating demands, and concern about “word getting out” if they shop other local banks has created for many an atmosphere of fear that they might be left with no good alternatives to their long-time lending source.

That fear was realized in 2020 when the pandemic hit. Many companies had to completely close down, while others suddenly had to shop for alternative sources of cash . Of course, many turned to PPP and EIDL government-backed loans. In just the latest round of PPP loans, the SBA reports that, as of February 28h, there have been 2.2 million loans totaling $156 billion . Importantly, all these loans have been consummated through an online lending process.

These government programs have led many borrowers to new SBA-backed lenders they’ve never worked with before. This is part of a gradual awakening that middle-market companies can do something about being “underserved.” Well before Covid-19 arrived, the Deloitte Center for Financial Services reported that 20% of middle-market companies were prepared to actively shop for new lenders. Although the pandemic led to many new middle-market banking relationships, it exacerbated the feelings of many that they’ve been “overcharged.” This is due in no small part to most banks implementing a pandemic-inspired review of their middle-market relationships in 2020. Not surprisingly, these reviews led to more stringent loan terms (i.e., higher interest rates and lower loan sizes) and tighter covenants (restrictions on asset sales, financial reporting requirements, use of cash, and accounts receivable, etc.).

In addition to having historically limited borrowing choices compared to larger companies, middle-market companies tend to pay higher rates for their loans. Many lenders not only require personal guarantees from company owners, but they also charge higher interest rates. (If you own a business, consider the rate on your personal credit card and compare it to the rate on your company card.) Another factor is that many middle-market companies will engage a commercial loan broker to seek out the best terms and conditions. These brokers will get a 1% or 2% commission on the loan amount at closing. Those fees are passed along to the borrower in most cases.

Now let’s look at middle-market company sales and acquisitions. About 10,000 middle-market companies are sold each year. That number is expected to grow as business-owning baby boomers retire from family-owned companies. According to a study by the National Center for the Middle Market , at any point in time, about 70% of business owners are thinking about selling their business, and 50% are expecting to acquire a company. Middle-market leaders say that finding the right buyer for their company or the best target to acquire is one of the most confusing aspects of M&A. Nevertheless, they don’t seek much help with the process. Both buyers and sellers of companies tend to rely heavily on their internal executives and top managers when searching for companies to buy or sell to. During the search process, about a third of buyers consulted an external law firm, and even fewer talked to consultants or investment bankers. Sellers were even less likely to bring in external advisors as part of their search for the right buyer. Only one in five sellers uses an investment banker.

Why is this? It’s not because these company owners are so experienced that they don’t need help. Only half of sellers have ever sold a company before. On the buy side, only 10% have multiple acquisitions under their belts. It’s therefore not surprising that two-thirds of middle-market companies are dissatisfied with the results of their M&A experiences. With this background, why don’t they seek professional assistance from the investment banking community that their bigger siblings use?

First, most bankers aren’t interested in these smaller deals. And worse, the bankers who will work with middle-market companies charge fees that are outrageously high in the view of their clients. In addition to monthly retainers, bankers will ask for a “success fee” ranging from 5% to as much as 10% of the sale price. This is also the fee structure bankers apply when raising equity capital for middle-market companies. For an “exiting” owner of a $50 million company, that could amount to $5 million or more off their ultimate reward for building up a business over the years. Moreover, bankers are thought of as “glorified real estate brokers.” As one owner said to me, “All they want to do is flip my company and get on to the next deal.” Company owners object to the pressure put on them by outside advisors to hurry up and get a deal done, fear leakage of confidential company information, and dislike the loss of control over the process. Most sellers and acquirers get a case of cold feet at some point along the way.

The historical track record of success for capital transaction deals in the middle market is grim — only one in ten deals get done .

What Needs to Happen

How can middle-market companies and the many deep-pocketed capital providers overcome this capital transaction chaos? First, they have to use available and forthcoming technology to get to know each other. The use of online application techniques in the PPP program shows that company executives can adapt to the use of internet deal making. Think again about fintech in the consumer sector: An overwhelming majority of working-age Americans use apps to access money, pay bills, apply for loans, and exchange information over the internet. The key to improving the velocity of successful capital transactions in the middle market is new tools that facilitate access to secure information exchange so that the parties on each side of a transaction can 1) get to know each other and 2) expeditiously gather, organize, and exchange critical information on a confidential basis.

Fortunately, the market is showing signs of moving in this direction, albeit belatedly. The dramatic dislocations created by the pandemic have many companies shopping for loans, some desperately seeking buyers, others opportunistically seeking acquisitions, and more comparing alternative costs of capital to foster post-Covid growth. Middle-market business leaders are ready for this shift to digital — 49% of them report that they’ve implemented new ways of utilizing technology during the pandemic.

In addition to the SBA programs, there are many innovative companies creating new tools for middle-market capital transactions, including:

Cerebro Capital : A middle-market lending platform
NepFin : Another lending platform
ValuSource : An online company valuation product
BizEquity : Another set of online valuation tools
Opus Connect : Equity fundraising networking
Axial : Another equity fundraising network
RealAtom : An online commercial property financing service
iBorrow : Online bridge financing for commercial property

Time will tell how deeply these and other applications will affect their respective middle-market capital segments. In any case, there will continue to be an extremely large number of middle-market companies, and they will continue to have a real impact on the U.S. economy. Extant marketplace inefficiencies and the need for rationalization is equally obvious. It’s time for innovators to more comprehensively emulate fintech in consumer markets and empower increasingly ready middle-market executives. Whether they’re capital seekers or capital providers, management teams can handle capital transaction apps. In the coming years, our rebounding economy will be boosted by new tools and expertise that reduce capital chaos, simplify and streamline debt, equity, company exits, and acquisitions in the middle market.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How SMEs can fight and win during the recovery phase

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 19 Mar 2021 09:31:56 GMT

The findings show that companies that increased investments during the recession showed improvement in return on equity, sales growth, and market values in the recovery phase following the recession.

Recessions are hard on midsize companies, who tend to play defense by implementing across-the-board cuts during tough times. But it’s not the only strategy, and likely not the best one. The authors examine three categories of investments — capital expenditures, economic competencies, and talent — midsize companies made before, during, and after the 2007–2009 recession. Their findings show that companies that increased investments during the recession showed improvement in return on equity, sales growth, and market values in the recovery phase following the recession. This presents opportunities to attract new talent, deploy new technologies, and lock in long-term financing, among others.

Despite the roaring stock market and $3 trillion of relief money provided by the U.S. government, 2020 will count as a recession year . While the economy isn’t yet back to normal, it is showing signs of recovery .

Recessions are tough for most companies, especially midsize ones. Such companies lack the financial muscle that allows large companies to make brave moves during a recession. For mid-size companies, demand dries up, cash flows are blocked, credit is difficult to come by, and almost every financial metric turns red. What should such firms do during a recession, particularly as they rethink their strategy midway through it?

In our work with midsize companies, we usually find that the natural response is to play defense: Cut costs and preserve the remaining cash to last through the tough phase. But is playing defense the only strategy? Is it the best strategy? We provide data-based evidence of the optimal strategy for the midway point of a recession. What we found shows that playing offense could be a better choice for midsize firms than playing defense, provided they can pull it off.

A previous article examined the best strategy during the 1980 crisis (which lasted from 1980 to 1982), the 1990 slowdown (1990 to 1991), and the 2000 dotcom bust (2000 to 2002). It concluded that companies that master the delicate balance between cutting costs and making forward-looking investments do well after a recession. The article also acknowledges that this prescription is not easy to follow. Not many companies are ambidextrous — that is, able to make long-term investments while achieving cost efficiencies and rationalizing their workforce.

We examined a more recent recession, 2007 to 2009, to revisit the question of optimal strategy. Instead of focusing on all companies, we focused on the industries that are typically most affected by a recession, such as aircraft and shipbuilding, automobiles and trucks, meals and restaurants, petroleum and natural gas, mining, shipping containers, metals, construction, transportation, coal, and steel. These are also the industries that have been most affected by the current pandemic . We didn’t examine R&D–intensive industries — such as electronics, software, and biotechnology — for which continual innovation is not a luxury but a survival strategy. In addition, we examined only midsize companies that were in the middle 40% by market value just before the start of the recession (that is, at the end of 2006).

They examined periods of three years before the recession (2004 to 2006), during the recession (2007 to 2009), and the period of recovery (2010 to 2012). They contrasted the changes in recovery-phase performance of the companies that increased investments during the recession with those that decreased them. Because financial numbers are not comparable across time, they adjusted them for inflation. They ignored companies with small changes in investments. They identified companies that were in the highest quartile (that is, the top 25%) in terms of percentage change in investments and compared them to those in the lowest quartile. Thus, they contrasted only those companies that made significant changes to their investment plans.

We examined three categories of investments:

Capital expenditures, such as land, buildings, machines, warehouses, equipment, and infrastructure;
Economic competencies, such as in innovation, patents, brands, strategy, peer and supplier networks, customer acquisition and relationships, and training;
Talent as measured by the number of people employed.

They examined three measures of success:

Improvements in return on equity;
revenue growth;
market value of company stock.

Since numbers may not be comparable across periods of recession and recovery and the sample of composition of firms changes over time, we converted our measures into percentile ranks. For example, a company was considered successful if its market value improved to 40th-percentile rank in the recovery phase from 55th-percentile rank during the recession.

Figures 1 through 3 show the findings, which are stark and instructive. The firms that increased investments during the recession showed improvement in return on equity, sales growth, and market values in the recovery phase. Companies that decreased investment showed deterioration on all three counts.

Clearly, playing offense dominates playing defense. What could be the reason? The principal reason is that recessions are inevitably followed by expansions, which typically last longer than the recessions. (The expansions after the 1980–1981 crisis and the 1990–1991 slowdown lasted eight years, the one after the 2000–2002 bust lasted six years, and the one after the great recession of 2007–2009 lasted 10 years.) Recessions are fertile ground for creative destruction and catapulting new winners.

So, despite the undeniable adversity, our prescription is that a CEO must consider recessions as opportunities . For some companies, they’re advantageous anyway. For example, Dollar Tree, Walmart, and Ross stores did very well during the 2008 recession when customers turned toward budget sellers. Similarly, 2020 has been a great year for Amazon. Yet even for industries battered by recession, it’s the best time to take advantage of the following opportunities for the forthcoming expansion:

Companies can attract ambitious employees who would thrive in a forward-looking, growth-oriented environment. Talent is not only available at a lower salary but is also more accessible.
It’s an opportune time to conduct mergers and acquisitions and consolidate when valuations are low and competitors are willing to hive off divisions.
It’s an ideal time to deploy new technologies , whose deployment during a boom phase would slow down the firm’s profit engine.
There’s an opportunity to attract dissatisfied customers from competitors by offering superior products and services .
To the extent that the government infuses liquidity into the economy and interest rates go down, it could be an advantageous time to lock in long-term financing .

A notable example of playing offense during a recession is Samsung. It increased its R&D and marketing expenses and hired the best brand managers during the 2008–2009 financial crisis and emerged as a formidable player in mobile phones market .

Why don’t most firms follow this strategy? Because their long-term vision gets fogged up by the day-to-day exigencies: Cancelled contracts, customer defaults, idled capacity, banks’ refusal to provide additional working capital, and cash-starved suppliers’ insistence on cash payments instead of extending credit. Firms come under intense pressure from investors to do something about deteriorating financial performance. CEOs cave and announce policies aimed at pleasing stock markets, such as reducing operating costs; shrinking discretionary expenditures like R&D, employee training, and advertising; eliminating frills such as offsite meetings and team-building exercises; delaying brand launches; rationalizing business portfolios; postponing buying assets like plants and machinery; firing contract workers; and lowering head count. They often resort to zero-based budgeting to start questioning every expenditure.

But restructuring and laying off workers might be the worst actions firms can take during recessions. According to Dave Cote of Honeywell, if you lay off employees when you’re halfway through a recession, you still have to pay six-month severance pay, which means you only realize those savings after six months. Suppose the recession lasts for another 12 months. In order to gear up production, you need to hire new workers six months ahead of time. So, by firing workers, you really don’t save much, but you do destroy morale and incur additional rehiring costs. Workers with low morale are unlikely to offer innovative solutions to problems, which causes product and service quality to suffer and leads to more unsatisfied customers. Notably, finance departments usually do “across-the-board” cuts, instead of a more judicious rationalizing of investments and saving at least a few future-oriented projects from the universal axe.

In sum, we believe that playing defense, particularly late in a recession, is not the optimal strategy for midsize companies . On the contrary, it could be the best time to prepare for a forthcoming expansion . And given the lower costs of forward-looking plans during a recession, such a strategy would provide better return on investments in capital, competencies, customers, and talent than it would during the expansionary phase.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The future of Client-Referral Strategy

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 17 Mar 2021 19:32:04 GMT

Nothing builds new business more reliably than a sustained effort in gaining referrals from existing clients.

There is no better source of leads and revenue than referrals that come from a company’s clients. New clients that come from referrals advance through the sales process faster, have more forgiving negotiations and healthier margins, and tend towards greater loyalty. Why? Because they are already qualified and you begin with the credibility of a trusted peer.

Yet most companies leave securing referrals from existing clients to chance or engage passively, if at all. The companies that successfully harvest this crop do so with intention and a clear strategy to leverage their current client relationships to drive new business. Here is a no-fail approach to accelerating revenue through referrals that I’ve implemented with companies ranging from the Fortune 500 to mid-market businesses, and even other consulting firms.

A) Make referral business a central part of your go-to-market strategy: t he Sales Organization has to execute your strategy. So, in addition to your ideal client profile, competitive advantages, and precision about how your sales experience creates value, your strategy should clarify how you intend to attract new opportunities. Make it clear that proactive pursuit of referrals is a primary driver to increase pipeline growth and accelerate new business. Along with trade shows, advertising campaigns, and content marketing, all of which likely have detailed execution plans, ensure that proactive referrals are included as a priority initiative.

B) Manage the acquisition of referrals as a process: m any companies have a sales process that helps guide the stages and actions required to advance an opportunity from contact to contract. In order to operationalize referral business, you’ll want to do the same with discreet stages and actions for each stage. A simple process could look like this:

Identification. The first stage is similar to prospecting in the sales process. It is entirely about determining which existing customers could refer you to another potential customer. While it’s easy to say everyone can provide a referral (and they can), it’s important to determine who is most likely to provide you with quality referrals. What are quality referrals? Those that match your ideal client profile and are at a level they can buy from you. Each of your sellers should have a target list of contacts they can begin reaching out to request referrals.
Request. This stage consists of conversations with current clients asking them for an introduction to a new potential client. Let them know you have a favor to ask and set aside a time to talk. This will carve out a moment for your request so that it doesn’t get lost amidst other conversation points. Let them know that your best clients usually come from your best clients. I recommend that you make two promises for anyone a client introduces you to. The first is that the conversation will be valuable and provide insight and expertise that will be useful to the referral, whether or not they do business with you. The second is that there will be zero sales pressure to buy anything at all. This demonstrates your commitment to the importance of relationships that are based on value, especially since you are asking someone to share their professional network with you. Your clients may not immediately know who else they can refer you to so expect a few interactions before there is a bona fide connection is made.
Preparation. Make it easy for your clients to refer you to others by providing them with a sample email to use to make the introduction. Less tends to be more here as the goal here is to ensure your clients have an easy way to make the introduction.
Introduction. Follow up is key in this stage as it may take a number of conversations or reminders before your client makes the introduction. Until you have been introduced, the follow up is with your client and like managing a sales cycle, there is a balance between professional and persistent and being a nudge. I suggest following up 3–4 times, and if no introduction is made, let it go and come back to it 6–9 months later. Referral business is a long game.
Appreciation. Once you’ve connected with the new referral, close out this process with a thank you to your client. It can be a simple handwritten note or something more elaborate, but don’t forget this important task. After all, you want to recognize the trust they’ve put in you and encourage them to do it again. Some clients may make many referrals for you over time and you can continue to go back to them. If you’ve concluded the process properly, you lay the foundation to make the request again in the future.

C) Focus sales talent on execution of the process: When revenue, net income, and business measures dominate discussions, it’s easy to lose sight of other disciplines that create positive results. To create a sustainable stream of referral opportunities, make each stage of the referral process a priority in your sales organization. The skills of consultative selling are directly transferable to executing the referral process. All that’s needed is leadership attention to keep it front and center. Individual coaching sessions will help to focus your sales team on the process, discuss any challenges sellers are having, and let them know how progress will be measured. When considering metrics, use the scientific approach posited by Daniel Stufflebeam: measure to improve — not to prove . Too much emphasis on metrics can yield lower quality referrals. If you overdo the drive to achieve a specific number of introductions or a certain level of activity, you’ll probably get it — but it may not have the impact you wish for. Make your metrics a guideline for managing the activity but not an end unto themselves. Use them to coach areas of improvement in using the referral process and as a diagnostic to discover issues your team may be struggling with.

Companies are always looking for approaches to drive the development of new business. Nothing builds new business more reliably than a sustained effort in gaining referrals from existing clients. What’s more, as a marketing approach to fill the “top of the funnel,” it is more likely to succeed than other campaigns and carries virtually no incremental costs. What it does require is a commitment to the strategy of leveraging existing client relationships and developing and managing referral acquisition as a prioritized process.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The M&A wave is coming

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 11 Mar 2021 19:16:05 GMT

Well-capitalized companies will soon face a once-in-a-generation opportunity to make acquisitions and consolidate power.

As the Covid-19 crisis continues and the full brunt of its economic impact becomes clear, merger and acquisition activity is expected to ramp up. Weaker players that had relied on government support or cash reserves are likely to experience more financial stress, leaving them ripe for takeover, while stronger firms will find new avenues to bolster their positions, acquire new or complementary skills, technologies, and products, or simply grab some valuable market share. But research shows that most M&A deals fail. To ensure that your organization capitalizes on this opportunity, avoid five traps: favoring strategic while ignoring the financials, walking away due to cultural differences, using bankers for valuation, not focusing on integration as you negotiate, and moving too slowly.

Well-capitalized companies will soon face a once-in-a-generation opportunity to make acquisitions and consolidate power. As the Covid-19 crisis continues and the full brunt of its economic impact becomes clear, weaker players that had been given some breathing room by temporary government support programs or by falling back on their cash reserves, are likely to experience much more financial stress, leaving them ripe for takeover. Stronger firms might therefore find new avenues to bolster their positions; become more efficient; acquire new or complementary skills, technologies, and products; or simply grab some valuable market share.

However, if history repeats itself, many CEOs will pursue deals that are more likely to hurt their companies. The evidence is clear: most mergers and acquisitions fail . They destroy shareholder value and cost companies billions. There are recurring, unsuccessful patterns found across industries and geographies.

Why does this happen? One factor is inexperience. Many senior executives work on one, maybe two, highly consequential deals in their professional lives. These are often the most challenging and risky endeavors they encounter. However, there are ways for leaders to increase their chances of M&A success. This starts with avoiding common traps. Here are five I’ve identified in my research :

Trap 1 : Favoring “strategic” over financially savvy. Too often “strategic” is an empty catch-all word used to glam up a deal that doesn’t make financial sense. Overpaying is the single biggest predictor of M&A disaster. Consider the AOL-Time Warner merger, valued at $350 billion when it happened in 2000. Billed as a transaction that would create the media company of the future, it is now universally derided as the worst deal in history. Within two years, the merged entity was forced to write-off $99 billion. After 10 years, the combined value of the two companies (which had by then separated) was about one-seventh of their worth on the date of the merger. Other well-known examples include of “strategic deals” that ended up in disaster includes the mergers of Vodafone-Mannesmann, and Hewlett-Packard (HP)-Autonomy. How can you avoid making a similar mistake? First, ask yourself four key questions: Why merge? Why this company? Why now? What is a fair value? Then act like a financial investor: set a “walk-away price” and stick to it. Do not fall in love with the deal. A team composed of cross-functional managers can help to put a check on confirmation biases, impulsiveness, and even hubris.

Trap 2: Walking away because of cultural differences. Culture is often blamed, ex-post, when integration fails and massive write-downs have to be booked. But the problem is not the cultural differences per se; it is being unprepared to deal with them. In the preparation phase of a deal, it is important to assess firms’ respective cultures, and design the M&A processes to bridge any gaps. However, there is not a one-size-fits-all approach. Many acquirers succeed by imposing their culture on the companies they’ve taken in. “I don’t need free beer. I can buy my own,” said InBev’s CEO, Carlos Brito, when addressing the employees of Anheuser-Busch (AB) in the aftermath of his $52 billion acquisition of the American brewer in 2008. This was a signal that the InBev culture of efficiency, meritocracy, and a war on waste would dominate the combined firm. Other acquirers choose to incorporate some elements of the acquiree’s culture in the combined entity or allow the purchased company to operate independently. For example, when Disney and Pixar came together in 2006, it was not obvious that the deal would end in success. The idea was to combine Disney’s merchandising and distribution expertise with Pixar’s technology and creativity, but the two cultures were very different. Robert Iger, then CEO of Disney, knew that what made Pixar special had to be protected, so Disney agreed to a list of things that would not change, including not using employment contracts, maintaining generous health benefits, and not changing the Pixar sign at the gate of its headquarters. Cultural differences are not the problem. Not being aware of or planning for those differences is the real value killer. During a well-managed M&A, the majority of cultural issues will be identified – and solutions prepared – before the deal closes.

Trap 3: Using investment bankers for valuation. Company valuations are always part of investment banks’ offerings. But while investment banks are good for roadshows and financing, they should never be used for valuing or negotiating the deal because there are perverse incentives at play. They get paid (and ranked and rated) when deals close and their fees rise with the purchase price so they’re always on the side of the deal, not the company. Whenever possible, companies should develop valuations in house (by those who really understand the sources of business value that will result from the potential merger) or with the aid of third-party advisers who are less likely to be biased. All employees involved in providing estimates for this exercise should understand the principles of M&A value creation, as well as basic valuation skills. And CEOs, CFOs and other top executives should be involved at the earliest possible stage and understand how valuations are made so they can help identify and assess opportunities and risks and ultimately realize the benefits that emerge from the deal should it happen.

Trap 4: Not linking deal-making with integration . Companies must have a continuous process that links the pre-deal phase with the transaction period and the post-deal phase. The firms most likely to destroy value are those that fail to assign detailed responsibilities and strict accountability to the teams in charge of researching, planning, negotiating and implementing the acquisition. Too often, the people making promises about merger synergies are not the same ones in charge of putting those synergies into place. Instead, the same team members should be assigned to every phase of the transaction. These were the principles used when First Gulf Bank (FGB), the largest publicly traded bank in the United Arab Emirates, acquired Dubai First (DF), a financial player focused on consumer finance and credit cards. The bank assembled an “A Team” of about two dozen people from across its businesses and functions to evaluate the target, identify synergies in their areas, and prepare a binding bid. Many of these same team members went on to lead the integration effort and were therefore accountable for achieving the gains they’d outlined when recommending the merger.

Trap 5: Moving too slowly and with poor communication . The uncertainty generated by potential mergers takes a terrible toll on employees and customers. Bad news is often better than no news, so managers should be ready to answer questions even before the final answers are known. Along with transparent communication, speed is key. Even while waiting for regulators’ green light, get to work on integration challenges without neglecting the day-to-day operations. For example, when Royal Dutch Shell announced in April 2015 that it was acquiring BG Group, integration planning began immediately. Management teams from both companies began working together at a neutral location to map out how business units would come together while retaining talent. They also held a town hall meeting with BG employees of BG’s headquarters and visited key locations across the world to discuss the combined company’s plans, answer questions and address concerns. It is important to remember that your customers are not going to stand still, nor will your competitors, who often use these dormant periods to steal business from either company (acquirer or target). You need to integrate fast and communicate transparently while ensuring that operations continue to run smoothly and communicate transparently.

M&A will never be easy. However, that is not a reason to avoid them. As the global economy recovers from the Covid-19 pandemic, we will see a wave of deal activity. Leaders who prepare themselves well and avoid these common traps will be able to create more value from these opportunities.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

When US startups expand in Europe

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 14 Feb 2021 19:30:49 GMT

Why do so many companies get it wrong?

Expanding into the European market can be a significant boon for U.S. tech companies, on par with a new type of customer or a new product line. Why then, do so many companies get it wrong? Most make four common mistakes: mis-timing of the expansion, forgetting the root causes of domestic success, hiring the wrong leader, and over-delegation by the CEO.The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

U.S. software firms are the most successful companies of the 21st century. From software-as-a-service pioneers like Salesforce and Zoom to ads-powered giants such as Google and Facebook, software firms represent close to 30 percent of the S&P-500 index. But despite their ascendancy, these companies have an Achilles’ heel: they make costly and avoidable mistakes when they venture overseas.

Europe is the default first stop for U.S. companies for good reason: a well-run SaaS company at IPO derives about 30 percent of its global revenue from the region. In 2019, SaaS companies went public with a median global ARR of $278 million , implying more than $80 million from Europe. Revenue opportunities of this magnitude are rare. To re-create something comparable in the U.S. would usually require a new type of customer or a new product line — both of which are heavy lifts.

By contrast, transporting an already-successful U.S. model to Europe should be relatively straightforward. Nonetheless, even strong software companies make an awful mess of it. One now-public SaaS company initially located its European headquarters on the beautiful Portuguese island of Madeira based on tax advice. Now Madeira has many charms, but as a location to build a sales and support team, it was an unworkable choice. Although the company rectified its mistake, it lost a year doing so.

A blunder like Madeira might be an extreme example, but software companies regularly waste effort and forego tens of millions in revenue. Most do so by making four common mistakes: mis-timing of the expansion, forgetting the root causes of domestic success, hiring the wrong leader, and over-delegation by the CEO. If companies get these big things right, they can afford to get many smaller things wrong. Here are some practical steps that executives can take to overcome each of them.

Timing

Moving too late leads to foregone revenues and risks the emergence of copycat competitors. Moving too early stretches an already thin organization. So, what’s the right time? Having watched the movie of European expansion play a few times — with happy and tragic endings! — Frontline developed a simple checklist. If a CEO can answer “yes” to these questions, it’s time to expand to Europe:

Is your U.S. business humming? The way the product is sold in the home market should be repeatable without heroics.
Is there demand? Early customer wins can be turned into local case studies that kick-start the region.
Is your executive team strong and deep? International expansion is a cross-functional effort that must be shouldered by several members of the CEO’s executive team.
Is globalizing the company a personal priority? The most successful expansions are passion projects of the CEO.
Are you well-funded? Successful expansions come with significant up-front investment, including senior people on the ground.

Note that capital is not the only readiness requirement. Our research shows that companies expanding overseas in 2021 have raised more than three times more capital than their predecessors from a decade ago. It turns out that company maturity — as indicated by the first four checklist items — is not dependent on capital alone.

Success amnesia

Companies are on average six years old by the time they open their first office in Europe — a lifetime in a startup! Over those few years, organizations tend to fall prey to “success amnesia,” forgetting the dirty work that was required in the early days. Even the best products don’t just take off on their own. They are more often dragged towards success by the whole company.

A lot of this comes down to how startups work in the early days, when walls between functions are porous: engineers talk to customers, salespeople suggest product changes, customer support does market research, everybody recruits. This kind of cross-functional effort can produce amazing results. Yet when companies expand to Europe, many view it as a straightforward Sales effort — and that sets them up to fail. Poor expansions are often caused by a brand-building or partnerships or product issue. The hidden culprit of a poor go-to-market expansion is more often Marketing (or lack thereof) than Sales. Fifty percent of companies don’t have a single marketer on the ground in Europe a year after landing. Sales can only win once those other functions create a tailwind that Sales can ride.

First senior hire

Although getting the first senior hire right is crucial, 46 percent of companies end up replacing that person within two years. This is largely because startups and venture capital firms rely on curated talent networks — their little black book of high performers who have delivered in the past — which rarely extend across the Atlantic.

One now-public SaaS company transferred an early employee from HQ to become its first leader on the ground in Europe. On its face this was not a bad decision — exporting the company’s unique office culture from San Francisco was rightly a priority. But the expat had limited European experience and personal networks. Believing the company’s brand was strong enough to overcome language barriers, the person hired English-speaking sales reps to sell to French and German customers. While that might have worked in the Netherlands or Sweden, it was a mistake in France and Germany and it led to millions of dollars in foregone revenue.

Companies can increase their chances of hiring success in a couple of ways:

Find a Sherpa. In the first year, companies need someone locally who knows the market and has a pre-built network. This could be a Europe-based investor or consultant or executive at another software company. Just make sure their network is relevant and they are incentivized to help.
Visit. A surprising number of CEOs pick a European HQ location without attempting to get to know the city — or the talent pool that lives there. A physical visit is essential, even in a world of remote work, and it raises the likelihood of a good first hire. With a little sourcing in advance, it’s easy to set up 10 informal meetings with potential candidates and begin to build a talent network.

Hiring the right person is the first step; setting them up for success is the next. CEOs can radically enhance the new leader’s prospects by designing two further things well:

Org structure. Functional org structures work at HQ because the CEO aligns functions around the company goals and resolves disputes between them. But when Sales and Marketing bicker eight time zones from HQ, the issue can fester without resolution. One solution is a European General Manager, who directly manages the customer-facing parts of the organization (e.g., Sales, Marketing, Support, Partnerships) with dotted line responsibility for other functions such as Engineering.
Landing team. A winning combination is to kickstart Europe with a senior local hire and some high-performing employees on long-term assignment from HQ. While this approach is used by fewer than 10 percent of companies, it is a proven way to encode cultural DNA in the new operation and mitigate the risks of mis-hiring the European GM.

CEO mindset

International success doesn’t start “over there”; it starts at HQ. Without the CEO’s interest and support, the expansion is doomed. European expansion can seem like a project that only requires CEO attention for a few months until the right people are hired. However, treating it as a short-term project or over-delegating to subordinates is a missed opportunity. Consider it instead to be the first step on a decade-long journey of globalizing the company to fulfill its true potential.

What does a global company look like? The product is engineered from the ground up to be usable worldwide. More than half the company’s revenue comes from outside the U.S., providing an additional growth engine and diversification. Executives based outside the U.S. run global parts of the company. The CEO and executive team at HQ allocate their time to domestic and overseas issues in proportion to the growth opportunities of each. They are well informed about the global business and frequently meet international customers and employees. “International” is not delegated to a VP International or VP Sales. The board and executive team include people who have lived and worked overseas and have run truly global businesses.

As companies emerge from pandemic-enforced retrenchment, CEOs have an unheralded opportunity before them. Until 2020, most American startups were not only U.S.-centric, they were HQ-centric. Decision-making and communication depended on being close to the CEO and executive team at HQ. There were few processes, practices and systems in place to overcome physical distance. One of the reasons they failed overseas had nothing to do with customers or cultures or languages or regulations. It was a failure to operate as a distributed team.

Covid-19 changed all that. Companies had a few weeks to figure out how to work remotely and they have been perfecting their approach ever since. Engineers and salespeople have scattered around the country. The San Francisco Bay Area, which had been a hiring bottleneck for years, started to lose its monopoly on talent. Even C-suite executives that left for lifestyle reasons may stay where they are when the pandemic is over.

For young companies whose cultures are malleable, this shift will be transformational. A huge obstacle to international expansion has been removed. Companies will discover, often to their surprise, that they are much better placed to become truly global companies than they ever were before. The best CEOs will take advantage, because globalizing a company doesn’t really happen overseas. It happens at the CEO’s desk.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The European Commission's report about business sustainability puts the business at risk

alessio.defilippis@gmail.com (Alessio De Filippis) — Tue, 02 Feb 2021 14:13:47 GMT

Not only does the report fail to show that EU businesses are misgoverned, it also makes proposals that would actually put these businesses at risk.

A recent report by the EU claims that investor-driven short-termism is encouraging firms to return cash rather than invest it, which reduces capital available for investment in growth. Jesse M. Fried and Charles C.Y. Wang show ina a recent article publishe on Les Echos that the data behind the report do not support its claims and argue that if the EU implements the recommendations of the report EU-listed firms will struggle to compete as decision-making slows up and capital gets allocated to questionable ventures.

The Report

The European Commission recently released a sustainable corporate governance report claiming to find a problem of investor-driven short-termism, and proposing as a solution that power be shifted in EU-listed firms to other stakeholders. But the report’s findings are deeply flawed. And its proposed policies would, perversely, reduce business sustainability in the EU.

As supposed proof of short-termism, the report points to rising levels of gross shareholder payouts — dividends and repurchases — and declining levels of investment. The claim: firms are increasingly showering cash on shareholders, stripping them of assets that could be used for long-term value creation. But the report mischaracterizes capital flows, mismeasures investment, and fails to consider firms’ cash balances. The actual data paint a very different picture.

Start with capital flows. Oddly, the Commission’s report fails to account for equity issuances in measuring capital flows between firms and shareholders, focusing exclusively on flows in the other direction — dividends and repurchases. But as we have shown in a recent paper , stock issuances in the EU are substantial, far exceeding repurchases. During 2010-2019, for example, gross shareholder payouts represented 63% of net income. But equity issuances were almost half as large: 27% of net income. Thus, the ratio of net shareholder payouts to net income was 36%, a figure very similar to U.S. public firms.

The report’s conclusion that investment by EU-listed firms is falling is also deeply flawed, but for a different reason: it relies on a cherry-picked sample of public firms. An analysis of all EU-listed firms reveals that both capital expenditures (CAPEX) and research and development (R&D) increased during the period covered by the report, both in absolute terms and relative to revenues (so-called “investment intensity”). Moreover, CAPEX and R&D both increased over the last decade, when shareholder activism has been most intense.

The report implies that investment might be higher had shareholder payouts been lower. But cash balances grew by nearly 40% over the last decade, from €712 to €973 billion. This would suggest that investment by EU public firms is limited by the lack of additional opportunity, not by a lack of available cash. Moreover, even if a particular public firm lacked cash today, the firm could simply issue more equity. That, after all, is why firms go public in the first place. In fact, in each year during the last three decades, smaller EU public firms have absorbed more equity capital from investors than they have distributed: their equity issuances have exceeded dividends plus repurchases.

The Risks

Not only does the report fail to show that EU businesses are misgoverned, it also makes proposals that would actually put these businesses at risk. Most importantly, the report recommends an EU-wide reformulation of directors’ duties to include a broad and ill-defined range of considerations, including representing the interests of the “global environment” and “society at large.” These duties would be enforced by non-investor stakeholders bringing suits in court.

The effect of implementing such proposals would be corporate paralysis. Almost any board decision could be legally challenged by some stakeholder claiming a violation of directors’ almost boundary-less duties. Concerned about personal liability, or even just the embarrassment of being named defendant in a lawsuit, directors will refrain from major decisions without getting buy-in from every stakeholder that might sue them. How will these firms compete with nimble U.S. and Chinese firms? Conducting business through an EU-listed firm will simply no longer be sustainable. Firms will go private, or seek to avoid these rules by domiciling and listing elsewhere.

In fact, the sustainability of Europe’s entire business eco-system would be put at risk. Directors of large listed EU firms would feel pressured to cut back on dividends and repurchases and invest more internally, even if such investments make little sense from investors’ perspective. Capital would be trapped in cash-rich firms and mis-spent. The flow of capital from larger public firms to smaller public and private firms would dry up. Firms looking to raise cash would find it more difficult. After all, why would investors hand funds over to directors whose EU-mandated fiduciary duties now require them to deploy the funds to benefit the global environment and society at large? The question answers itself.

If the European Commission really wishes to increase business sustainability, it should take steps to make it easier, not harder, for European firms to raise, deploy, and return equity capital. It should turn its back on the report’s proposals, which are as poorly-grounded as the findings of short-termism trotted out to justify them.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

A small hedge found is trying to push ExxonMobil towards sustainability

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 22 Jan 2021 18:06:46 GMT

If it succeeds in pushing through a new slate of directors, the hedge fund will embolden a new generation of activist investors who can push corporations to embrace more progressive ESG policies.

The hedge fund is taking on ExxonMobil, accusing the energy giant of dragging its feet on climate change, which has led to disappointing financial returns. Amazingly, the fund’s campaign has received the support of one of the largest asset-owners in the world, the California State Teachers’ Retirement System (CalSTRS). The fund wants four changes at Exxon:

refresh the board;
impose greater long-term capital allocation discipline;
implement a strategic plan for sustainable value creation in a changing world;
realign management incentives.

Something interesting is happening among shareholders of the energy giant ExxonMobil, which may usher in a new era in activist investing. A new activist hedge fund, Engine No. 1 , is pushing widespread reform at Exxon through its “ Reenergize Exxon ” campaign. What makes this campaign extraordinary is Engine No. 1’s tiny size — it holds only a $40 million stake in the multibillion dollar company. Yet there are strong signals that Engine No. 1 is in position to reorganize and reform Exxon from the very top of the company.

Exxon’s financial situation certainly lends itself to an activist hedge fund campaign. As of December, 2020, Exxon’s market cap hovered around $175 billion, down from its peak of $528 billion on December 24, 2007, but up from its trough of $139 billion on October 26, 2020. In August 2020, it’s 92-year membership in the Dow Jones Industrial Average ended . And in an open letter of December 7, 2020, addressed to Exxon’s board of directors, Engine No.1 notes that Exxon’s Return on Capital Employed (ROCE) for Upstream projects (which have historically accounted for over 75% of total capital expenditures) has fallen from an average of around 35% from 2001-2010 to around 6% from 2015-2019.

The company’s poor capital allocation decisions are based on decades of denial about climate change on the company’s strategy. Greenpeace has documented over 50 years of climate change denial and Exxon’s actions to thwart efforts to deal with climate change. Exxon counters that it “has supported development of climate science in partnership with governments and academic institutions for nearly 40 years.”

This research certainly hasn’t influenced the company’s strategy. Carbon Tracker notes in an October 2020 report by Paul Spedding, “ How the Mighty Are Fallen—How Chasing Growth Destroyed Value in ExxonMobil, ” that a major reason behind this poor performance was that Exxon overinvested in high-cost assets in order to chase growth. “The consequential ballooning in its capital base and its operating costs were a major factor behind the collapse in its return on capital. Its shareholder returns followed suit.”

So, what chance does Engine No. 1 have to succeed with its campaign? We think it has a good chance for three reasons. We have already discussed the first — the company’s continued abysmal financial performance with no reason to believe it will improve under its current leadership and strategy. It is important to note that Engine No. 1’s campaign is not based on ExxonMobil’s irresponsible approach to climate change; it is based on the financial consequences of this approach. That’s a message that the broader shareholder community can easily get behind.

The second is that Engine No. 1 is making four sensible and reasonable recommendations:

refresh the board;
impose greater long-term capital allocation discipline;
implement a strategic plan for sustainable value creation in a changing world;
realign management incentives.

Spedding would likely agree with (2) and (3): “In an energy transition, a low-cost strategy with capital discipline will be more beneficial for shareholders than chasing growth. Matching its volume ambitions to a Paris-accord demand profile would be a step in the right direction,” he wrote in the Carbon Tracker report. The executive compensation system must then be changed to provide the right incentives for a different and better strategy.

Given the company’s track record, it is unlikely the last three recommendations can be implemented without the first. Towards that end, Engine No. 1 has proposed an alternative slate of four independent directors: Gregory J. Goff, Kaisa Hietala, Alexander Karsner, and Anders Runevad. Goff was the CEO of Andeavor, a leading petroleum and marketing company, and was named in 2018 by the Harvard Business Review as one of the “ Best Performing CEOs in the World .” Hietala served as the EVP of Renewable Products at Neste, a petroleum refining and marketing company, named in 2019 by Innosight as one of the “Top 20 Business Transformations of the Past Decade.” Karsner is Senior Strategist at X (formerly Google X) and is a Precourt Energy Scholar at Stanford University’s School of Civil and Environmental Engineering. Runevad was the CEO of Vesta Wind Systems, a wind turbine, manufacturing, installation, and servicing company, and was included in Fortune’s “Businessperson of the Year” list in 2016. All of these directors bring valuable energy experience to the Exxon board.

Getting these four candidates elected to the board will require a majority of the shares voted at the company’s annual shareholder meeting on May 27, 2020. Which gets us to the third reason we think this campaign can succeed — there is a good chance Engine No. 1 can get the necessary votes. Proxy voting is playing hardball, but this is a skill activist hedge funds have honed over many years of practice, and this is what Engine No. 1 brings to the party. They have made the case for change and now they must round up the necessary votes to enact it.

This campaign is already gaining steam. For starters, on December 7, 2020, the California State Teachers’ Retirement System (CalSTRS), the second largest public pension fund in the United States with $280 billion in assets under management and owning $300 million of Exxon’s shares, announced it would support this alternative slate . Further, Exxon’s top three shareholders own nearly 18% of the shares (Vanguard with 7.75, State Street Global Advisors with 5.17, and BlackRock with 4.99). The top 10 own about 25 percent of the company’s shares . The company has a large retail shareholder base which historically has not showed up at the annual meeting in large numbers. This means that if Engine No. 1 can marshal the votes of the largest shareholders, they have a good chance to prevail.

We asked Aeisha Mastagni, a Portfolio Manager in the Sustainable Investment & Stewardship Strategies Unit at CalSTRS, why they were supporting what some might think is a quixotic campaign. Her response: “At CalSTRS we are developing the idea of ‘Activist Stewardship.’ The idea is to combine our role as a constructive, engaged shareholder with deep financial analysis, while utilizing the full suite of activist tools available to address companies that are failing their shareholders and other stakeholders. ExxonMobil is our first example and it’s hard to think of a better one given the company’s financial performance and decades of indifference to its shareholders.”

In other words, Engine No. 1 is part of what might become a major shift in equity markets. Traditionally, activist funds have been regarded as the thorn in the side of management pursuing nothing other than short-term shareholder returns. However, some funds are now recognizing that environmental and social issues are major constraints on the financial performance of their investments. Pure long-term financial considerations are therefore dictating a greater focus on issues that activist investors have previously shunned. If the Little Engine’s “Reenergize Exxon” campaign succeeds, it could be the prelude to many similar ones by other funds to the benefit of shareholders and society at large.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Coopetition: Compete and Collaborate

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 20 Jan 2021 17:34:31 GMT

When Should You Collaborate with the Competition?

Graham Kenny (Harvard Business Review) "Companies facing small markets or resistance to their value proposition could benefit from working with their competitors to raise awareness of their sector or product category. The experiences of Australia’s wine growers and avocado farmers provide instructive examples.Why don’t Australians drink much whiskey? They’re hardly known for being abstemious. Part of the answer is that they tend to drink beer and wine. But another part of the answer is that whiskey brands haven’t made a concerted effort to get them to really try whiskey. Perhaps they should, because Australians have been lured into changing their drinking habits in the past."

Rewind to the 1960s and Australian wine consumption was way down on today’s level. So, wine producers got together and educated the public on the nuances of fine wine. Now Australians are drinking four times the amount of wine they drank in 1961 and are among the largest consumers of wine in the world on a per capita basis.

This is not a one-off. Back in 1998 real men didn’t eat avocados. At that time Australians were eating less than 1kg of avocados per capita per year. After a series of industry-sponsored campaigns, this has now grown to 3.88kg and is above the figure for the U.S .

In business after business you find companies in the same position today that the CEOs and marketing managers of wine and avocado producers were in back in the day.

Natalie is the marketing manager of a well-known brand of American bourbon whiskey. She and her sales manager face a lot of competition within the spirits category as there’s a wide range of competing spirit drinks — gin, vodka, rum, and so on. And within the whiskey category there’s Scotch whisky, Irish whiskey, and bourbon to choose from. Looking at bourbon’s market share of the liquor category is like peering down a microscope.

When I asked Natalie what her company was doing about noncustomers (people who have never even considered trying the product) her reply was terse: “We only target ‘spirit’ category buyers. There’s no difference in our approach to non-whiskey buyers.” She described the company’s approach as “short-termism.”

Hilary, CEO of one of Australia’s leading businesses in natural medicines , also has a lot of noncustomers. Many Australians won’t touch natural medicines because they’re afraid that the products don’t work and could even be harmful. Hilary admitted to me: “There’s a job yet to be done just with raising the credibility of natural medicine to reach the industry’s nonconsumers.”

I asked Tim, head of an IT recruitment company, if he faced noncustomers. “Sure do,” he said. “A noncustomer for us is an organization that has made a decision to build their own talent team and have come to a firm position that they will not use recruiters. They want to do it themselves. They develop an in-house mindset. In the past, we’ve put them in the too-hard basket.” Again, these noncustomers have set their minds against the whole recruitment industry.

So how did the wine and avocado producers fix their problems?

They invested in “coopetition” — simultaneously collaborating and competing with rivals.

This required them to cooperate at the industry level to grow and improve the industry while at the same time compete at the firm level for market share. Organizations such as Wine Australia were formed to “promote the sale and consumption of Australian wine internationally and domestically,” which included large-scale wine exhibitions with producer-hosted wine tastings.

Coopetition requires taking a long-term view. The wine industry educated the consumer — fully — on grape varieties, wine types, and styles. Before the 1960s Australian wine consumers were relatively unsophisticated, having grown accustomed to sweeter, lower-quality wines. Producers also took the lead in introducing industry regulation on the quality and consistency of products and established a symbiotic relationship between the improving palate of Australian wine consumers and the improving quality of Australian wine.

The Australian avocado industry did much the same. Part of their success is down to an industry body, now called Avocados Australia , whose role is to “provide a range of services to our members and the broader industry to foster growth and development.” Coopetition is clear: “By working together we seek to continually improve our growers’ ability to provide a healthy, profitable and safe product for all consumers.”

Avocados Australia’s activities are funded by grower levies . As Ray, the Chairman of a growers’ cooperative and himself an avocado grower, pointed “industry leaders have always been able to persuade growers to support levies which go towards research and promotion.”

Of course, coopetition at scale in a developed market requires serious and sustained effort. No wine or avocado producer achieved the noncustomer shift alone — it required organization and combined effort. So start by sizing up the opportunity. Is it worth chasing? If your answer is “yes” then reach out to your competitors and other industry participants, such as suppliers, and get organized. Coopetition could benefit everyone involved.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

Creative Disruption and Regulation in Innovation Driven Economy

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 06 Jan 2021 20:04:25 GMT

The increasing turnover in the companies that dominate the tech sector is proof that regulators are missing the point

I have already explainde in previous articles my point of view about the issues regarding the fight against anitrust and tech industries not only in USA ( The antitrust in the digital industry and Big Tech: from ‘Too Big to Fail’ to ‘Too Complex to Break Up’ ) but recent a research by Bain & Co. have given an interesting contribution to the dialogue abut the topic.

The Research

Research by Bain & Co shows increasing turnover among the companies that dominate the tech sector over time. Although we think of tech companies as disruptors, in fact they are more likely to be disrupted than firms in retail or financial services, two other industries that have seen their share of churn. The researchers identify two forces that increasingly drive tech companies’ ability to create value: their ability to create a dominant platform , or their ability to reposition the core business or extend into new areas.

The landscape for big tech companies is growing more challenging. Recent U.S. congressional reports have signaled bipartisan agreement on some new antitrust regulations —a development that, while no sure thing, some industry observers thin k could end up strengthening antitrust enforcement in possibly narrow but consequential ways. But the fact is, regulation isn’t likely to dethrone America’s top tech companies. Let’s remember: rapid change at the top is the way it works in the technology industry.

Just look at the sheer magnitude of the turnover among the highest-valued technology companies over the past two decades. In 2009, newcomers had replaced about half of the 1999 list of the 15 highest-valued technology companies worldwide. From 2009 to 2019, 40% of the top 15 turned over again. Only four companies remained on the list through the entire 20 years: Microsoft, Intel, Cisco, and Oracle.

There’s a reasonable argument that those fast-paced dynamics aren’t going to stop, regardless of what happens in the regulatory landscape. While technology executives are rightly paying attention to regulatory developments, they shouldn’t lose focus on their most enduring challenge: staying ahead of the competition. That’s particularly important in the context of the industry’s eternal disruptions and shifts in leadership. The technology sector’s vibrancy and capacity for innovation leads directly to its rate of creative destruction, with or without regulation. And this dynamic nature isn’t par for the course in most industries.

Bain & Company research conducted last year of more than 1,300 companies’ performance from 1996 through 2018 shows that technology companies are 12% more likely to be disrupted than companies in retail and 25% more likely than those in financial services — two other industries that have historically gone through disruptions. Although the term “disruption” has become something of a cliché, here we’re specifically referring to companies whose annual market capitalization growth lags their sector’s average by 2% or more for at least three years in a row. Only advanced manufacturing and services companies have a higher likelihood of being disrupted than technology companies.

Disruption isn’t just more common in tech — it also can be more damaging and permanent. Once a technology company falls behind, it can be difficult for it to catch up. The same Bain research suggests that a technology company that has been disrupted is 12% less likely to return to sector-average market capitalization growth or higher than companies in retail and 17% less likely than those in healthcare, for example. Even more striking, the data shows that once a technology company trails its sector for three years, its chances of turning things around drop below 20% and continue to decline as time goes on.

Why is it so hard to maintain leadership in the technology industry?

There are three important factors:

The speed at which technology transitions occur,
The sector’s “winner-takes-most” effect ,
A remarkably mobile work force .

Thanks to abundant cloud-computing infrastructure and mobile connectivity worldwide, a technology platform can reach a massive audience at unprecedented rates. And of course, increasing scale digitally is nearly frictionless — it doesn’t require the time or costs involved in producing or distributing physical products. Ultimately, scale accrues so quickly to the winners that once other companies miss a transition, it’s difficult for them to get in the game. Lastly, since these dynamics are well understood, the industry’s talent appears to be uniquely mobile – leading to irreversible declines in competitiveness after trailing for several years.

Still, leading technology companies have managed to stay ahead of the curve, both creating and maintaining value. Those success stories almost invariably involve one of two playbooks for accelerating growth: extending a platform’s capabilities into new domains or repositioning the core business platform during a technology transition.

Choosing the right playbook depends on where a technology company falls in its life cycle: disruptor or incumbent. When a company’s platforms are still growing, the formula for value creation more often involves extending them into new domains. When a company already has a mature platform-based business model, the formula for value creation typically involves repositioning the core platform as the technology transition occurs. This approach is particularly potent if the market has concluded such a repositioning is unlikely. Examples include Microsoft’s and Adobe’s repositioning around the cloud, and Nvidia’s repositioning from mobile devices to graphics and data centers as artificial intelligence growth exploded.

These transitions can be tricky. The new platform can pose conflicts for the old by cannibalizing the core business and alienating existing partners or customers. The better today’s business is performing, the greater the resistance to change. But waiting until growth slows is often too late. Many companies have missed the opportunity to refocus their businesses and ended up worse off. Nokia, for example, was unable to adapt its feature phone business for the smartphone era. In the years following the introduction of the Apple iPhone, Nokia lost billions of dollars in market value and ended up selling its handset unit.

Technology companies in a high-growth phase face a different set of challenges when trying to extend their capabilities to create new businesses. The trick is to find the sweet spot where the organization’s strengths match a promising new market opportunity. Examples include Amazon’s extension into cloud-computing services with Amazon Web Services; Apple’s extension into mobile media devices, smartphones, and media content; and Alibaba Group’s extension into payments, cloud computing, and digital transformation of businesses. These companies built on their core strengths to reach new customers, deepen their relationships with existing ones, or accomplish both.

Government regulation is an increasingly important variable to keep in mind when crafting a company’s value-creation strategy. Even before the recent antitrust efforts, scrutiny of technology mergers and acquisitions was intensifying — notably in Europe, but also in the U.S. and other regions. Regulatory oversight is evolving beyond issues of market concentration to include consumer data and privacy, national interest and security, and future competition. All of this places greater demands on technology executives to prepare for consultations with regulators and broader stakeholder communications during, for example, the diligence and negotiation phases of deal making.

Whether a company needs to reposition or extend its core business, the essential job of management is to deeply understand the transition occurring in its sector and create a plan built around the firm’s strengths. The next generation of technology leaders are hard at work doing that, even if you haven’t heard of them yet.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

The antitrust in the digital industry

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 26 Nov 2020 16:48:18 GMT

After recent complaints against Big Techs, probably it's time to rethink some basic concepts like the monopoly.

According to my previous article Big Tech: from ‘Too Big to Fail’ to ‘Too Complex to Break Up ’ I'm starting to think that authorities around the world are missing the point: the standard concept of monolpoly is not applicable to digital industry and probably to other economic sectors heavily driven by competitive advantages based on Schumpeter's creative desctrution.

This idea doesn't mean that antitrust is no longer relevant, but probaly not in every economic sectors and not always in the same traditional ways. Probably we need a more multi-dimensional approach.

As pointed out by James Heskett " ... In 1997, a federal judge blocked the first proposed merger of Office Depot and Staples, ending a marriage that would have reduced the number of big box office supply specialty chains to two, the other being Office Max.

In seeking to stop the merger, the Federal Trade Commission argued that “office supply superstores constitute a unique market segment” and that other office-supply competitors such as Walmart, Sam’s Warehouse, and Costco were outside the segment. As a result, the merger “would give the (new) company near-monopoly pricing power.”

Probably the Federal Judge was right.

"The nature of the monopoly? At the time, the company resulting from the merger would have had about $10 billion in sales and a dominant share of the office supply superstore industry—but only a 6 to 8 percent share of the overall office products market. I remember it well. I was the member of Office Depot’s board acting as the liaison to the Staples board on the merger matter. The case cost the two companies slightly more than $20 million to defend. (I didn’t have to recall these details; I Googled them at no cost.) Even then, however, market definition and monopoly pricing power proved to be complicated issues for regulators to address."

Fast forward

The US Department of Justice’s recent complaint charging that Google has suppressed competition in the search market through its deals with cell phone handset manufacturers, especially Apple, to ensure that Google is the default search app installed on new iphones.

That may be one reason why a private dinner at a Palo Alto restaurant between Apple CEO Tim Cook and Google CEO Sundar Pichai in March 2020 attracted so much attention. You’d have to be pretty naïve not to assume that somewhere on their menu was negotiation around Google’s annual payment to Apple for including Google’s search engine into Apple products—a payment estimated to be as much as $12 billion, or 21 percent of Apple’s profits. The larger point is that Google pays Apple large heaps of money to help it preserve its 92 percent share of the global internet search market.

The government vs. Google case will probably drag out over several years, costing American taxpayers a bundle, but a fraction of what Google can easily afford to spend to defend itself and, if necessary, provide restitution. After all, a recent European Union judgment against Google of more than $9 billion was hardly noticed by the Company’s shareholders.

Will consumers evidence even less interest in this case than in antitrust cases of the past? Unlike communications companies whose practices generate consumer ire, Google’s services, like many on the internet, are perceived as both “free” (although Shoshanna Zuboff in her recent book argued persuasively that they are not) and a great convenience. It’s the advertisers who pay the freight for Google’s search services.

Defining markets, identifying consumers, and tracing the impact of high tech intercompany “deals” on competition and resulting costs for consumers may be much more difficult now than even 20 years ago. In an information age in which the “product” is readily shared, can’t be hoarded, and is a result of a network, how will it be feasible for regulators and courts to prohibit business relationships or “break up” business strategies in which one part of the business (advertising) pays for another (search) which, in turn, generates value for advertisers? And if the end result is only a fine, how large does that fine have to be to matter?

It’s clear that antitrust is not what it used to be, at least not under current laws. Is new legislation required? If so, how likely are the appropriate committees of Congress to come forward with recommended changes? What chance do they have of becoming law?

Is antitrust just a quaint notion in the internet/Cloud age? What do you think?

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

Tech companies are the greatest value destroyers and creators

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 02 Nov 2020 14:40:26 GMT

According to LIVA index there are great surprises lying beneath the myth of tech companies

Manager use short-term measures, shareholders ask long-term value.

It’s a well-known paradox that Nicolaj Siggelkow is trying to fix with LIVA (Long-term Investor Value Appropriation).

Most executives are committed to the idea of maximizing long-term shareholder value, but when they want to track and improve performance, they focus on a dizzying variety of short-term measures (and acronyms). ROA. ROC. TSR. EBIT. EBITDA. CAR. EPS. We could go on.

Why this focus on short-term measures? In large part, because they’re easy to obtain, easy to use, and have been widely used in the past. The problem, as studies have long made clear, is that o ptimizing short-term accounting measures and ratios often doesn’t maximize long-term value . To think clearly and effectively about long-term value, companies need a better measure — and, as we write in a recently published article in the Strategic Management Journal, we’ve devised one that we call LIVA, short for Long-term Investor Value Appropriation.

The idea

The idea behind LIVA is simple: Add up the net present value of all the investments a firm has engaged in over a long period of time. The key insight from our analysis is that this can be done by using publicly available stock-market data. LIVA uses historical share-price data to calculate the value a company has either created or destroyed for its entire investor base over a long time period.

To see what LIVA can do, consider the case of Apple. Imagine you were fortunate enough to have bought 100 shares of the company’s stock in 1999. If you had reinvested any dividends and sold your shares 20 years later, you would have made an annualized return of 27%, well above the market average of 6%. That’s a healthy return, but hardly spectacular. In terms of total shareholder returns (TSR), it ranks 3,175th among companies worldwide over this time period — a ranking that would seem to suggest that Apple’s success hasn’t been very exceptional. One might derive the same conclusion from the most recent “Value Creators Rankings,” published annually by the Boston Consulting Group, which is based on five-year TSR and ranks Apple #34.

The problem is that TSR doesn’t actually measure long-term value creation for the entire shareholder base over a particular time period, but only the returns for those who held (a certain number of) shares over the entire period. LIVA, however, takes into account that the capital base of a company can change over time. In doing so, it provides a very different sense of the value Apple has created. If in 1999 you had bought the entire company at its then-market price, accounted for any cash received through dividends or share buybacks, and gone on to sell it 20 years later at its much-improved market price, you would have been more than a trillion dollars richer than if you had invested the same amount of money in an index fund. Apple’s LIVA over this period, in other words, was more than $1 trillion. That’s truly dominant performance that makes Apple the world’s number-one company in our ranking, with a LIVA 57% higher than that of Amazon, our number-two company.

Those rankings come from a global LIVA database that Nicolaj Siggel and his team created to help managers and researchers identify the best- and worst-performing companies by country, region, and industry. The database tracks the performance during the past 20 years of more than 45,000 companies that had at least $100 million of initial market capitalization and for which data was available for at least five years. It allows analysis of value creation at the level of individual companies, industries, and countries.

A comparison with traditional performance measures shows how much more revealing LIVA is, as becomes clear when you look at the top and bottom 10 performers as determined by each method .

The top five companies on our LIVA list are tech companies that together have created more than $2.6 trillion in shareholder value in the past 20 years. If you look at the top companies on the excess-return list, you’ll see that although successful, they haven’t generated anything like that kind of value. They were able to achieve very high returns, in fact, only because they started out small, with an initial average market capitalization of $332M. For example, the market capitalization of the number-one company on the list, Pharmasset, was $187M in 2007 and shot up to $11.2B in 2011, when it was acquired by Gilead. (We’ve excluded companies with less than $100M in market capitalization; their inclusion would certainly lead to even higher potential figures of excess returns.)

The top performers on the ROA list are also relatively small companies. That’s because an extremely high ROA often does not reflect very high profits but rather very low balance-sheet assets. The top performer on the ROA list is BP Prudhoe Bay Royalty Trust, a company that distributes profits from oil- and gas-royalty rights, which have not been capitalized on the balance sheet to their economic value. The company’s apparently high performance is thus as much a reflection of accounting conventions as of good underlying economic performance.

These examples show some of the potential advantages of using LIVA when assessing long-term firm performance. Unlike measures based on ratios such as ROA, LIVA determines the absolute size of economic performance without favoring initially small companies. Unlike accounting-based return measures, it values both profits and growth. And it never hinges on accounting definitions.

If you were to look at just the 10 best LIVA performers, you might conclude that tech is the place to create value. But one of the great advantages of LIVA is that it provides revealing information not only at the top but also at the bottom.

If you look at the worst performers on our list, you’ll notice several tech companies: Lucent, MCI/WorldCom, and AOL/Time Warner. These companies have destroyed a remarkable amount of value in the past 20 years. In fact, in the aggregate the companies in the category Technology Hardware & Equipment in our database had a LIVA of negative $2.2 trillion. The only category that has destroyed more value is Telecommunication Services. Other measures don’t reveal the extreme distribution of long-term performance in the tech industry that our LIVA measure reveals — nor, by extension, do they prompt questioning about why that distribution exists in the form it does. A LIVA analysis reveals that a majority of companies in the tech industry actually destroy value. Only those companies that dominate their respective sectors are able to create enormous shareholder value — a consequence of network effects.

LIVA also provides a more meaningful way to think about bankruptcy than total shareholder return and excess return do. In bankruptcy, after all, unless there is residual value to claim for shareholders, those measures are always the same: −100%.

LIVA details the magnitude of the value destroyed in each case and therefore can serve as a meaningful measure with which to analyze corporate decline. In this context, a striking fact emerges when one uses LIVA to study the companies in our database: Only one of our top 20 value destroyers actually went bankrupt. It turns out that 42% of the value destroyed from 1999 to 2018 was destroyed by companies that were still active in 2018, and another 33% was destroyed by companies that were acquired. Companies that were once successful often have a much longer time to destroy value than firms that go bankrupt. If value destruction is a form of “failure,” then most of the failure is happening at firms that still exist. This suggests that LIVA might be a valuable addition to the bankruptcy and dissolution measures used regularly in industry-life-cycle and population-ecology studies.

To create long-term value in the future, managers need to understand what strategies have been successful in the past. LIVA provides a robust tool for knowing which companies in various countries and industries have been able to create the most value and which have destroyed it. With internal financial data, managers can use LIVA to identify the strategies in their own companies that have paid off the most — an indispensable lesson that can help them lead their companies and hopefully join the ranks of the global top LIVA creators themselves.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

To Read More:

LIVA Index: https://onlinelibrary.wiley.com/doi/full/10.1002/smj.3114

Big Tech: from ‘Too Big to Fail’ to ‘Too Complex to Break Up’

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 14 Oct 2020 12:40:25 GMT

Turn-of-the-last-century-style antitrust laws and enforcement measures have failed

The long-awaited tech antitrust report that the US Congress released on October 6 presents a remarkably flimsy case for action against the nation’s most innovative and competitive companies.

The report’s main recommendations would do very little to solve real social problems caused by technology, like misinformation and election interference, because these problems aren’t related to competition. And by narrowing its focus to the technology sector, the House Antitrust Subcommittee missed an opportunity to look at parts of the economy— hospitals , insurance providers , food producers —where consolidation and competition are genuine concerns.

In the 451-page report (pdf) , more than a year in the making, legislators attempted to answer a seemingly straightforward question: Are Amazon, Apple, Facebook, and Google engaging in anticompetitive practices that government agencies aren’t able to punish under current laws? And if so, what changes should Congress make?

While the report describes a few genuine cases of unfair conduct by the platforms, many of the “problems” it identifies are merely complaints from companies that have been outcompeted. But harming competitors to benefit consumers (by lowering prices, for example) is the very nature of competition .

Most important, the report does not contradict these key facts about the US tech industry : prices are falling, productivity is rising, new competitors are flourishing, employment is outperforming other sectors, and most Americans really like these companies.

Disappointingly, the much-ballyhooed document is riddled with factual errors . For example, it claims that “a decade into the future, 30% of the world’s gross economic output may lie with [Amazon, Apple, Facebook, and Google] and just a handful of others.” But the source for that statistic, a study by McKinsey, actually said that by 2025 (not 2030), revenues from all digital commerce (not just by the Big Four and a few others) might reach 30% of global revenues.

To put in perspective how misleading the report’s original claim was, consider that the combined annual revenue last year of Amazon , Apple , Facebook , and Google represented only about half a percent of global economic output. Such a blatant error is conceivable only in a piece of work that first assumed its conclusion (“Big Tech is taking over the world”) and worked backward from there. There are dozens of other examples like this.

The good

Let’s start with what’s good about the report. It calls for increasing the budgets of the Federal Trade Commission (FTC) and the antitrust division of the Department of Justice , which is long overdue considering that their combined budgets have fallen by 18% (pdf) , in real terms, since 2010. If regulators do not have the resources to properly enforce the laws on the books, it’s no wonder that some lawmakers will start calling for changes to those laws.

The report also recommends requiring the FTC to collect more data and report on the state of competition in various sectors. And it says the FTC should conduct retrospectives to study whether its past decisions to approve or block mergers were correct. These kinds of studies are also long overdue and would make enforcement officials better at their jobs.

The FTC is currently engaged in a special review of every acquisition by the Big Five tech companies (those listed above, plus Microsoft) over the last decade. That process should be extended to other sectors and repeated on a regular basis.

Lastly, the report’s proposals for how to increase data portability might work very well for simple forms of data (such as a user’s social graph ), which are easier to standardize. If consumers can easily take their data along with them, it will be easier for them to switch to new platforms, giving startups more incentive to enter the market.

The bad

Unfortunately, the report’s primary recommendations would do far more harm than good. The signature proposal is to force dominant platforms to separate their business lines. Chairman David Cicilline, a Rhode Island Democrat, has called this a “Glass-Steagall for the internet,” referring to the 1933 US law (repealed in 1999) that divided commercial from investment banking.

In effect, this proposal would break up tech companies by separating the underlying platform from the products and services sold on it:

Google could no longer own Android and offer apps like Gmail, Maps, and Chrome. Amazon could no longer own the Amazon Marketplace and sell its own private-label goods;
Apple could no longer own iOS and offer products like Safari, Siri, or Find My iPhone;
Facebook could no longer own social-media platforms and use personal data to target ads to users.

The upshot is that these moves would destroy tech companies’ carefully constructed ecosystems and make their current business models unviable .

Of course, if this proposal is adopted, there will be many edge cases. Is the iPhone’s flashlight feature part of the operating system or is it more akin to an app? At this point, a flashlight feels like a standard feature of any phone. But not long ago, users had to download third-party apps to achieve that functionality.

As research from Wen Wen and Feng Zhu shows, when an operating system owner like Apple enters a product vertical (such as flashlight apps), third-party developers shift their efforts to other, more difficult-to-replicate app categories. So is adding a flashlight to the OS really anticompetitive behavior from a dominant platform, or is it pro-consumer innovation that leads to better allocation of developers’ time?

The consumer

To justify its proposals, the report would have needed to find a smoking gun (or two). It didn’t. In general, the leading tech companies produce enormous benefits for consumers.

Prices for digital ads have fallen by more than 40% over the last decade, and those savings flow through to consumers in the form of lower prices for goods and services. Prices for books have fallen by more than 40% since Amazon’s IPO in 1997. And Apple’s App Store takes the exact same cut (30%) as other platforms, including PlayStation, Xbox, and Nintendo. In fact, once you account for free apps, effective commission rates in the App Store are in the range of 4% to 7% .

The report’s authors massage the statistics to make tech companies look like monopolies even though they’re not by conventional measures (defined as having greater than two-thirds market share, according to the Department of Justice). They’re all very large businesses, but generally accepted data shows they don’t meet that standard. Amazon has 38% of the e-commerce market . Fewer than half of new smartphones sold in the US are iPhones. In the digital ad market, Google has a 29% share, Facebook has 23% , and Amazon has 10% .

What’s more, consumers themselves say they benefit greatly from the products and services that these companies build. Research in the Proceedings of the National Academy of Sciences has shown that, on average, consumers would need to be paid $17,530 per year to give up search engines, $8,414 per year to give up email, and $3,648 per year to give up digital maps. Meanwhile, the price to access these services is typically zero.

The competition

One of the main themes of the report is that these platforms have become so powerful no new companies dare to challenge them (and no venture capitalists dare to fund potential competitors). Several recent examples belie that notion.

Shopify, which is mentioned only in passing, is a $130 billion e-commerce company that powers more than one million online businesses. The company was founded in 2006, and the stock has risen roughly 1,000% over the last three years. Its most recent earnings report (pdf) showed that total gross merchandise volume on the platform is more than doubling year over year. (By contrast, Amazon’s GMV is growing by about 20% annually.)

To show Facebook’s dominance in the social-media market, the report includes an outdated chart ( on page 93 ) comparing global monthly active users across the leading platforms. The chart puts TikTok at around 300 million monthly active users. But TikTok is a much more formidable competitor to Facebook than the report’s authors seem willing to admit: it recently announced that as of July, it had nearly 700 million monthly active users worldwide. On the same day the report was published, the investment bank Piper Sandler released a study showing that TikTok had surpassed Instagram as US teenagers’ second-favorite social-media app (behind Snapchat).

Zoom is another competitor that’s glossed over in the report. The subscription-based company faced an uphill battle against incumbents such as Google that offer videoconferencing for free (or bundle it with other productivity software). The report notes that in response to Zoom, Google tried to boost its own videoconferencing product, Meet, by introducing a new Meet widget inside Gmail and adding a prompt for Google Calendar users to “Add Google Meet video conferencing” to their appointments.

How have these moves affected Zoom? The company increased its number of daily meeting participants from 10 million in December 2019 to 300 million in April 2020, and its stock is now seven times higher than it was last year (reaching a market valuation of almost $140 billion).

Those aren’t just a few outliers. As Scott Kupor, a venture capitalist at Andreessen Horowitz, pointed out , startups have been booming over the last 15 years in the US. According to data (pdf) from PitchBook, the total annual number of VC deals increased from 3,390 to 12,211 between 2006 and 2019. Deal value increased from $29.4 billion to $135.8 billion. The number of deals at the earliest stage of investment—angel and seed rounds—rose by about a factor of 10 over the same time period (to 5,107 deals worth $10 billion in total value in 2019).

They missed the point, so, what's next?

Granted, all the data presented here doesn’t rule out future antitrust cases against the tech companies. The Justice Department and some state attorneys general plan to launch an antitrust case against Google in the coming weeks. The FTC is likely to file suit against Facebook before the end of the year.

If those cases go to court, more sophisticated economic modeling based on non-public data might show that prices would have fallen even faster—or there would have been an even bigger startup boom—had the tech giants in question not been so dominant. But such an outcome would only prove that even if these companies really do harm competition, we don’t need major changes to our antitrust laws to hold them accountable.

To be sure, the scale and scope of tech platforms have created novel problems that our society needs to address , including issues related to privacy, misinformation, radicalization, counterfeit goods, child pornography, the decline of local news, and foreign interference in our elections . But instead of wasting taxpayer resources on a misguided crusade to break up our most innovative companies, Congress should consider passing measures like these:

Comprehensive federal privacy legislation that addresses the gaps in our current sector-based approach (and avoids the pitfalls of the EU’s General Data Protection Regulation and California’s Consumer Privacy Act).
Sunshine laws like the Honest Ads Act that help prevent foreign interference in future elections and make digital political ads more transparent.
Reform for the intellectual-property dispute process to reduce the prevalence of counterfeit goods online and prevent tech giants from copying genuinely innovative products.
Direct subsidies for the provision of local news, funded via broad-based taxes.

Unfortunately, changing our antitrust laws as the House Judiciary Committee recommends would fix none of the social issues caused by Big Tech. Each problem needs a targeted regulatory solution, not the big stick approach of "break them up."

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

Collaboration as a strategic tool in a downturn period

alessio.defilippis@gmail.com (Alessio De Filippis) — Sun, 11 Oct 2020 16:53:18 GMT

Openess can be the main weapon in time of war.

As an epigram often attributed to Vladimir Lenin goes, “There are decades where nothing happens, and there are weeks where decades happen.” In 2020 we have lived through a ridiculous number of those weeks. Yet periods of disruption can be rich in opportunity. A strategic examination of your current joint ventures and partnerships and the thoughtful creation of new ones can strengthen your position as you come out of the crisis and help you tap opportunities for growth during the coming rebound.

Downturn and partnership in recent years.

Companies will need every tool they’ve got to survive the downturn and rev up their businesses as the economy rights itself. They’ll have to rewire operations, reallocate resources, and in some cases reinvent business models.

At many firms, joint ventures and partnerships will play an outsize role in those efforts, both as a vehicle for sharing costs and reducing capital needs during the crisis and as a way to position themselves for growth once it ends. After all, in industries experiencing great pressure—like automotive, retail, and upstream oil and gas—joint ventures are quite common. GM and Volkswagen, for example, each have several dozen, and JVs account for almost 80% of the upstream production of the largest international oil and gas companies. At these and other energy businesses, joint ventures are also key to managing the transition from fossil fuels to renewables. More than 50% of the largest assets in offshore wind and solar are structured as joint ventures—and such investments are a critical way for companies like Royal Dutch Shell, BP, Total, and Equinor to share risks, build capabilities, and meet ambitious targets to reduce greenhouse gas emissions.

In health care and life sciences, joint ventures and partnerships are crucial to innovation: More than two-thirds of new health insurance products in the United States are built on cobranded or JV offerings, while life sciences companies depend on such ventures to accelerate time to market and broaden distribution of lifesaving products. In March 2020, for instance, Pfizer and BioNTech announced they were teaming up to bring out a Covid-19 vaccine. Other partnerships aimed at developing Covid vaccines have been announced by Sanofi and GSK, and by Hoth Therapeutics, Voltron Therapeutics, and Mass General Hospital.

JVs now drive a material share of companies’ profits as well. In 2019 Airbus, Celanese, Engie, Vodafone, and Volkswagen relied on noncontrolled JVs for more than 20% of their earnings, while at Coca-Cola, GM, and many others that figure was above 10%.

Moving forward, we expect the impact of JVs and partnerships to remain significant and, in some sectors and geographies, to increase. We recently analyzed trends related to joint ventures across the past 35 years. Our analysis showed that in most industries, terminations of them didn’t always increase during downturns—and often fell. Use of JVs also tended to rise on the eve of a recovery. This may be partly due to the time it takes to negotiate a restructuring or an exit, as well as corporate management’s tendency to look first to wholly owned operations when cutting costs. In addition, JVs’ returns on assets have been climbing recently—and are higher than those of wholly owned companies in the same industries. That means the number of terminations during this economic dip is likely to be even lower. Meanwhile, our analysis also showed that new joint venture and partnership transactions tend to increase during a downturn and to accelerate during a recovery, because they allow companies to get off to a much quicker start than organic growth does and are less risky than M&A.

During this period of retrenchment firms might stabilize their existing joint ventures by raising cash, cutting operating costs, reducing capital spending, managing risk, and restructuring. These are commonsense moves for the most part, but they require sustained focus; JVs are hard to restructure even in the best of times, owing to differing owner-company agendas, politicized processes, and general inertia. However, a crisis can serve as a catalyst for change. In addition, we’ll look at how companies might enter into new, “counterdownturn” JVs and partnerships, both to manage the challenging economic environment and to tap into growth opportunities in capital-light ways. But How?

According to Harvard Business Teview there are many ways:

Shoring Up Existing JVs.

Joint ventures are facing many of the same financial challenges—severe revenue shortfalls from fractured supply chains, curtailed operations, evaporating market demand, and frozen credit markets—as their owners and wholly owned peers. These new economic realities require both short- and long-term responses.

Efforts to reduce working capital, cut costs, tap additional credit lines, and take advantage of government subsidies and relief programs are already well under way in most joint ventures. To help pull off these near-term interventions, their boards will need to get far more involved than usual. Under normal conditions, joint ventures’ board directors spend an average of 5% to 10% of their time on governance. But during economic storms, an effective board can be the factor that determines whether a JV thrives, stagnates, or dies an untimely death. Working with managers, directors are convening special board and committee meetings and fast-tracking decisions.

JV partners, boards, and management teams will also need to evaluate opportunities to more fundamentally reset their businesses. Because of their shared ownership, joint ventures can use restructuring tools that aren’t available to wholly owned businesses. These approaches may benefit a venture, its parents, or both. They come in various forms.

Raising capital in unconventional ways.

Some joint ventures will have opportunities to secure low- or interest-free loans or capital from their cash-rich owners—such as state-owned companies, sovereign wealth funds, private equity firms, and multinationals with strong balance sheets. In exchange, those owners might get additional interest in the venture, preferred returns, or increased control. In 2015, when Russian automobile sales collapsed amid wider economic problems, Ford Sollers, a 50:50 joint venture between Ford and Sollers PJSC, received additional funding from Ford, which in return got preferred shares that gave it majority voting rights.

To free up cash, improve future liquidity, or open up new markets, joint ventures may also want to bring in new owners, such as PE firms, pension funds, other financial institutions, or strategic industry partners. Earnouts for the current owners could be pegged to the future performance of the business to make adding more owners attractive. Many investors, like PE firms, can bring in capabilities that give ventures a boost, including a better understanding of value creation, a sharp focus on cost reduction and talent management, governance discipline, M&A experience, and a portfolio that can double as an ecosystem of customers, suppliers, or partners.

Structuring creative commercial arrangements with suppliers, customers, lenders, and other business partners is another option for JV owners. In the past we’ve seen an ownership interest or option sold to a major supplier or customer in exchange for better commercial terms, including cash advances. If one of the owners is a major supplier to the JV, the parties might renegotiate their agreement (to, say, narrow the band of prices). Similarly, a joint venture might negotiate with a lender to convert debt to equity, making the creditor a direct owner.

Reducing costs through synergies and new operating models.

While JVs can cut costs on their own, much greater savings may come from consolidating or otherwise optimizing activities and assets with their owners. Ventures and owners might make joint purchases, integrate their supply chains, or combine some infrastructure, logistics, warehouses, or other operating assets. In 2003, Vodafone entered an agreement with SFR, its French mobile-telecommunications JV with Vivendi, to collaborate to improve economies of scale in operational areas like the development and rollout of new offerings and procurement, especially of technology.

Joint ventures might also save money by insourcing certain functions (such as legal, HR, IT, or finance) currently being provided by an owner. Our analysis has shown that although owner companies rarely profit from providing administrative services to joint ventures, their cost structures are often 10% to 30% higher than those of the JVs or of third-party providers they might contract with. Conversely, a joint venture that lacks scale might benefit from outsourcing certain functions to an owner or a third party.

In some cases cost cutting may lead to more-fundamental changes to the operating model. Those that reduce operating expenses or increase strategic and financial flexibility are especially popular during downturns. A lower-cost partner in a joint venture or a third party might become the controlling partner or operator, which can open up synergies with that organization. In our view many joint ventures should aggressively pursue this option, which allows greater nimbleness and offers more potential for performance improvement than do JV models in which control is shared by the partners and the joint venture’s management doesn’t have much authority.

In response to the Asian financial crisis in the late 1990s, BASF and Mitsubishi creatively restructured their equally owned and controlled joint venture in Japan, Mitsubishi Chemical BASF. It was split into two joint ventures, one focused on dispersants and the other on foam products—the business’s two core segments. Each was placed under the operational leadership of the parent best positioned to strengthen it—with BASF responsible for the dispersants venture, and Mitsubishi responsible for the foam products one.

Regearing financial ratios.

Joint venture boards might also consider authorizing or compelling management to increase external borrowing, especially if the entity is underleveraged, as JVs tend to be. Conversely, if a venture has excess cash, the board might seek to repatriate it to fund other, pressing corporate needs. During the 2008 financial crisis, the board of a large liquefied-natural-gas JV found that it had almost $500 million in cash on hand—enough to cover six months of operating costs. The board immediately approved a $300 million dividend, giving the owners cash to weather the storm elsewhere in their businesses.

Assisting owners through buyouts and other means.

Downturns tend to expose strategy and performance differences among partners. While the data doesn’t suggest they cause buyouts to spike, inevitably there will be some buyouts and sellouts, and some JVs will be terminated or liquidated.

In the current environment, routine decisions about budgets and capital expenditures may become deadlocked, triggering buy-sell options in about a third of joint venture contracts. In other cases the potential synergies will be greater if a single partner has full ownership, and the one for whom the JV is more core—or already more integrated—will buy out the other partners. Or one owner might acquire and integrate parts of the joint venture or sell out to a third party. After the 2008–2009 financial crisis forced the Canadian company Nortel to file for bankruptcy protection, for example, it sold its controlling stake in a high-performing Korean joint venture with LG to Ericsson. Alternatively, all the owners might sell the venture to a consortium of financial investors.

Creating New Joint Ventures and Partnerships.

New JVs and partnerships can also help companies navigate the economic crisis. They can be used to raise cash, secure cost synergies, or pursue lower-risk and more-capital-efficient growth. When funding is tight, such benefits make joint ventures and partnerships a popular alternative to mergers and acquisitions or organic investments. Our analysis shows that JVs and partnerships tend to increase in the late stages of a downturn, signaling a recovery and outpacing M&A. Right after the 1990–1992 and 2001–2002 downturns, for instance, the number of new joint ventures and partnership transactions was 20% above normal levels.

Partial divestments.

For companies that need more liquidity, a joint venture can be a good alternative to a full divestiture. One approach is to use it as the first step in a planned exit: A seller puts a noncore business into a joint venture with a potential buyer and negotiates to sell the full business over time, typically within three to five years. This kind of deal is especially worthwhile for sellers when prospective buyers don’t recognize the full potential of the business or its assets or may not be able to buy or operate the business on day one. IBM used this staged-exit structure when it sold its personal computer business to Lenovo, and so did Lanxess when it folded its specialty plastics business into a joint venture with Ineos, in 2007. In that case, Lanxess received one payment when the joint venture was set up and a second after two years, when it fully exited the JV. The second payment was based on the performance of the business—a useful method when it’s difficult to arrive at a valuation.

Another approach is to sell a partial interest in a business unit to a third party, effectively converting the business into a joint venture. Dow Chemical famously pursued such a game-changing structure in 2008, when it tried to sell key elements of its commodity-chemical business to Kuwait’s state-owned Petrochemicals Industry Company to raise cash and reduce exposure to the cyclical commodity-chemical business. But it was left at the altar when the Kuwaiti parliament rejected the deal at the last minute. During the Asian financial crisis, Doosan agreed to sell a 50% interest in its Oriental Brewery unit to the global player Interbrew to raise cash. The deal gave Oriental Brewery access to new technologies, marketing networks, and cost management capabilities that lifted its performance. In a similar move, from 2008 to 2010, Chesapeake Energy raised more than $8 billion by selling a partial interest in its U.S. shale gas assets to BP, Equinor, Total, and others.

A creative third alternative is an asset sale with a leaseback. Through such deals, companies typically divest certain (noncore) assets but tie them to a joint venture. For instance, as part of an aggressive corporate restructuring program it ran from 2005 to 2007, Sony sold its chip-manufacturing facilities to Toshiba for more than $800 million; those assets were then leased back to a new joint venture between the companies, which produced chips for the PlayStation and other Sony consumer electronics.

Business consolidations.

Synergies from this kind of joint venture can be substantial. There is a range of options here. At the narrower end of the spectrum, companies (especially those in the natural resources sector) consolidate a set of adjacent assets into a single joint venture to align all the parties’ incentives better and save money on infrastructure. Extending that logic more broadly to an entire region, country, or business unit, companies can consolidate similar operations with those of an industry peer or competitor to capture additional scale or cost synergies. In 2009 Morgan Stanley and Citibank consolidated their retail brokerage and wealth management businesses into a 51:49 joint venture in which Citibank also received an up-front cash payment of $2.7 billion. In a similar vein, in 2013 Bertelsmann and Pearson combined their trade-book publishing businesses, which were facing headwinds, into a 53:47 joint venture, Penguin Random House.

Companies might also team up with industry peers to consolidate back-office, sales, or purchasing functions into joint ventures and realize greater economies of scale. The big three U.S. automakers have done this in forming global purchasing joint ventures. Oil and gas companies have also pursued purchasing cooperatives, logistics pooling, shared maintenance and inventory management ventures, and other collaborative structures. So have telecom companies. For instance, Deutsche Telekom and France Télécom-Orange formed BuyIn, a purchasing joint venture in which the companies pooled procurement activities in an effort to save more than a billion dollars annually.

Partnerships for capital-light growth.

Companies looking for growth but seeking lower investment risk can consider a range of transaction structures. Some firms may enter global strategic partnerships with cash-rich players—including state-owned companies, sovereign wealth funds, or PE firms—to identify and develop a portfolio of opportunities within a sector or a market. The global oil corporation BP, the European chemicals maker Borealis, the Brazilian state-owned oil company Petrobras, and the French automaker Renault are among the dozens of companies that have pursued such arrangements over the years.

Alternatively, companies might acquire a partial stake in troubled business units of their peers operating in attractive markets. In 2003 the French oil giant Total took a 50% position in Samsung Chemicals through its chemicals unit Atofina, creating Samsung Atofina, to which it transferred technology, operating capabilities, and marketing expertise that jump-started the business’s growth. In some cases firms might jointly acquire third parties, as competitors Votorantim and Suzano did when they bought majority voting control in the Brazilian pulp and paper maker Ripasa during a market downturn. That transaction was structured to maintain the market independence of the two owners, converting Ripasa into a jointly controlled production unit. The agreement also provided the purchasers with the option to acquire additional preferred and common stock of Ripasa within six years.

A similar strategy is to invest in or partner with innovative suppliers and technology companies. Toyota’s investments and deep partnerships with core parts suppliers in the 1990s were credited with compressing the time necessary to go from concept to production, reducing manufacturing costs, and lowering defects. Similarly, in the 1990s, Samsung Electronics developed a program that nurtured suppliers with financial support and help building their technical capabilities, which led to technology improvements, savings on materials, and shorter order lead times.

Today power, chemical, mining, and petroleum companies could set up similar deals, making minority investments in clean-tech, renewable energy, recycling, or autonomous vehicle firms and agreeing to pilot those firms’ technologies in their operations. Such arrangements would allow large incumbents with lower PE ratios to participate in firms with much higher growth prospects and valuations and to speed up their transition to a carbonless future.

Yet another approach is to team up with industry peers and adjacent players to create and commercialize new products. Within the chemical sector, companies have been forming consortia and small partnerships to establish standards for, develop, and sell new sustainable technologies. Similar patterns will play out in other sectors; partnerships are especially valuable in disruptive markets and on technology frontiers. Many of these will start as simple nonequity collaborations with an option to convert to a full-scale joint venture once certain technology or financial milestones are passed, as a way to hedge bets and reduce up-front investments.

David Ernst , Your Alliances Are Too Stable .

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT

The future of Silicon Valley

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 05 Oct 2020 08:47:13 GMT

After 20 years, things are going to change.

The once-in-a-lifetime alignment of advantages of Silicon Valley has been amazing:

The ubiquity of cheap capital; T
The arrival of game changing technolgies like the smartphone (among other widely adopted tech innovations);
American primacy;
The benign regulatory environment.

A long list of features have all conspired to create a historic concentration of wealth and power.

The titans of the Valley and their heirs have been free to roam far ahead of lawmakers, watchdogs, and tax codes.

And Now?

That might not be true for much longer. Despite the fact that many public tech companies saw their valuations skyrocket during the lockdown and that the Covid-19 pandemic has accelerated mass adoption in e-commerce, online payments, telemedicine, and video conferencing, t here are signs that the gilded age for consumer internet businesses may be drawing to a close .

There are four main driving forces behind this.

Size vs. Innovation . First, the near total dominance of the top tech giants — Facebook, Amazon, Alphabet (Google), Apple, and Microsoft — has become stifling. These companies not only hoover up top talent, but have grown to such a size and expanded into adjacent markets to such an extent that they are starving all but the best new tech businesses of oxygen. Smaller companies who compete in one of the markets that Big Tech considers as strategic — an ever-expanding list — risk becoming a target of full financial power of one of the giants, who aim to crush or buy possible contenders before they grows beyond a certain size. This hegemony impacts innovation and centralizes capital allocation.
Exponential Growth vs. Long term profitability . Second, triggered in part by the poor post-IPO performances of Uber and Lyft — as well as smaller companies like Casper, SmileDirectClub, Super League Gaming, YayYo, and the WeWork/SoftBank debacle — investors, both private and institutional, are calibrating their approach. They are tightening requirements for additional financing to reflect the fact that a clear path to profitability, and not just exponential growth or “ blitzscaling ,” is now considered key. This, combined with the pandemic hitting certain sectors especially hard, has exposed some startups as having suspect business models. In the absence of easy access to funding, whether because of the pandemic or because of pre-crisis problems, a number of them have seen their investors withdraw and were forced to close .
Broken Promises vs. Public Opinion . Third, regulators, the media, and the public at large are now far more familiar with the downside of tech and the multiple ways the promises made to consumers have been broken. Mass privacy breaches , voter manipulation , disinformation , more precarious working arrangements , life-threatening products , or the outlandish behavior of certain founders were largely tolerated five years ago, mainly because of public ignorance and faith in tech bro mantras such as “ Move fast and break things ” and “ We’re making the world a better place .” Today, the tech industry receives much more critical scrutiny, as the cost of the industry’s unfettered reach and toxic side-effects — such as how social media and personalized search results make us more skeptical about science and more hardened in our opinions , or how short-term rentals drive rent increases — becomes increasingly clear.
Expectations vs. Moral Obligations . Fourth, similarly, the public mood has decidedly shifted and expectations for tech to be accountable for their impact on society have grown. As the tech giants have reached market caps equivalent to midsize national economies, expectations and moral obligations have grown, too. Facebook has a market cap of more than $700 billion, up from $240 billion just five years ago , while Apple, Amazon, Microsoft, and Alphabet are now trillion dollar-plus companies. Even the Business Roundtable, America’s most influential group of corporate bosses, has taken to cheerleading “capitalism with a conscience” with their 2019 statement on the purpose of a corporation asserting a “modern standard for corporate responsibility.”

All of these trends point to a reckoning on the horizon. In September, according to Pew Research , 73% of Americans said they were not very confident or not at all confident in the ability of tech giants like Facebook, Twitter, and Google to prevent the misuse of their platforms to influence the 2020 presidential election. Separate research found that 85% of respondents felt Big Tech has too much power. Meanwhile, there’s a growing expectation on both sides of the Atlantic for tech companies to pay their taxes fairly and in full , rather than play the tax minimization game they’ve been able to get away with for so long; researchers at Fair Tax Mark , a U.K. nonprofit that campaigns for tax transparency and justice, identified a gap of $155.3 billion between the expected rate of tax and the cash taxes actually paid by Facebook, Amazon, Netflix, Google, Apple, and Microsoft between 2010 and 2019.

Against this backdrop, it’s clear the typical tech business templates of the past couple of decades are no longer going to cut it — from either a business or societal perspective — for companies who plan to be around, let alone thrive, 20 years from now. The ready availability of investor cash coupled with sky-high revenue growth expectations that incentivized the use of predatory pricing (where VC billions are used to keep prices of, say, an Uber ride or a DoorDash delivery artificially low to undercut competitors), the exploitation of independent contractors in the on-demand economy, the algorithms that fuel outrage to increase time spent on social media platforms, and the advertising optimization encouraging privacy shredding micro-targeting are under threat and unsustainable.

What then will the tech business models of the future look like? Given the changing conditions outlined above, thriving in the next era of tech will likely involve meeting a different set of goals. While it is something of a taboo in the Valley (and on Sand Hill road in particular) to say so, tech’s new era will very likely see slower, yet more sustainable growth and reduced profitability.

All these changes in funding, regulation, and public sentiment will likely alter key aspects of the scale-first current business models described — and disrupt existing sources of revenue.

The Future

There may be new opportunities for a different kind of tech company:

The micro-targeted advertising model will increasingly be under attack and will weaken: Due to growing concerns around privacy invasion the dissemination of conspiracy theories and voter manipulation, look for companies to move away from the micro-targeting approach used by Twitter, Facebook, and Google/YouTube. The value of this model has been contested , and scandals related to hate speech, privacy violations , data breaches and more, have flourished. Web-based advertising platforms will likely limit micro-targeting to a very narrow subset of categories and advertisers, while moving towards some kind of “freemium” model, more acceptable to regulators and users.
More rights for gig workers and the end of “zero hours” contracts: Because of changing attitudes and user and customer pressure , “disruptors” of physical consumer businesses such as Uber, Lyft, Airbnb, and DoorDash will likely be forced to offer protections to full-time equivalent workers. Ultimately this will result in these companies being somewhat smaller and less profitable than the FANGAM-style tech giants their investors envisioned, which may well make this model less attractive to VCs seeking outsized returns. But new companies in this space, freed from the bottomless brunch of venture cash, have an opportunity to become genuinely profitable and sustainable from the get-go.
There will be big winners and many failures in the direct to consumer (D2C) and online product subscription model: Over the past decade there has been much buzz around D2C businesses for physical goods online, such as Dollar Shave Club, Harry’s, The Honest Company, and Casper. In theory by cutting out the middleman (i.e. the retailer), D2C companies can sell their products at lower price points than legacy brands, and double-down on product. But it turns out that D2C isn’t all it was cracked up to be. It’s not that the model is unviable per se, just that most D2C businesses haven’t really built any domain expertise: Their products aren’t necessarily better, they haven’t mastered digital marketing, and their unit economics are less attractive than at first glance because of acquisition costs and lack of scale. There are also too many of them, which is why only the biggest and best run D2C companies will win, and many will fail. Yet out of these ashes, new business opportunities will emerge. As consumer demand continues to shift online, I believe we are going to see a new generation of platform infrastructure businesses that will help any consumer brand become a D2C player. These platform infrastructure businesses (e.g., Stripe and Shopify) will benefit from the aggregation they create — you can’t be a master of digital marketing with one small D2C brand, but you can if you have 100. This trend will also power legacy brands to transition faster to D2C.
Companies that focus on “conscious capitalism” and empathetic tech will have an advantage: In an era where consumers demand higher ethical standards from all brands, all of the leading tech companies will increasingly be expected to exercise their power with far greater responsibility and will be held accountable by regulators, users, boards/investors, and even their own staffs (something we’re seeing more and more of ) to make the right trade-offs. These decisions include:
Whether to benefit from high user engagement from outrage and right-wing populism on platforms or provide a universal communication platform free from disinformation, bullying, and hate through stricter standards (Twitter, Facebook, and YouTube).
Whether to offer consumers lower prices with less vetting or limit inventory by cracking down on bogus or potentially dangerous products or situations (Amazon, Airbnb).
How to support legitimate security efforts of democratic governments without enabling surveillance, profiling and government overreach (Google, Microsoft, Apple).

All startup founders/legacy CEOs should turn to social entrepreneurship and build B Corps, value-based and empathy-driven companies make business sense: A study of exceptionally conscious firms demonstrated that they outperformed the S&P 500 index by a factor of 10.5 between 1996 and 2011.

While the convergence of these trends means some businesses will disappear altogether or be significantly downsized, others will thrive, albeit with different business models and economics, alongside reduced expectations and growth trajectories.

Still, there is a wild card in all this: the regulators . As businesses are pushed towards monopoly and aggregation to achieve the profitability and competitive advantage required by the markets, increasingly hawkish regulators will likely turn towards antitrust and hands-on oversight and interventions — and the big question is how far they might go. The uncertainty around future regulators’ actions on both sides of the Atlantic is particularly high given how the looming economic crisis renders protectionism and the defense of national champions increasingly tempting. My hope would be that the combination of industry-led initiatives, increased consumer scrutiny, and balanced regulation will help tech goes back to its original aspirations of being a force for good and of progress for humanity.

Interestingly this shifting landscape creates a unique opportunity for legacy businesses — not just for them to pivot digitally, but to become the Platforms 3.0. Legacy businesses have a huge advantage in that they know how to operate in the physical world, they have marketing teams, who know how to build and sustain brands, and crucially they know how to operate profitably in multiple territories within the law. It won’t be easy — most of them will fail — but the ones that succeed will be richly rewarded.

To read More:

Maëlle Gave t: https://hbr.org/search?term=ma%EBlle%20gavet&search_type=search-all

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How European firms can efficiently access US investors

alessio.defilippis@gmail.com (Alessio De Filippis) — Wed, 30 Sep 2020 14:47:27 GMT

An Introduction to cross-border trading

Accessing US investors is a top aim for many reasons:

The access to a varied pool of capital;
Its robust regulatory landscape;
The spread of a company’s reputation to an international scale.

NYSE or Nasdaq mean expensive, time-consuming and bureaucratic process, mainly without the aid of a good partner.

On the other side the number of public companies in North America has roughly halved during the last 20 years. There are several reasons for this: the cost of reporting and compliance, access to greater private capital than ever before, concerns around the public markets driving short-termism and M&A activity shrinking the pool of companies that could potentially be publicly traded. This means there is something of an opportunity for European companies looking to access new pools of capital – particularly those in the US, the world’s deepest – that many firms cannot afford to ignore.

The opportunities afforded by trading on markets like OTCQX, the highest tier of OTC Markets, offer a more cost-effective way to reach that capital. OTCQX has a raft of measures included to ensure that high financial standards and qualifications are maintained at all times.

At a time when the world is reeling from the impact of the Covid-19 pandemic, the importance of having a diversified, robust shareholder base has never been greater. Having a solid base of retail, institutional and high-net-worth investors tends to be even more important in a time of uncertainty. And no other country has a richer investment culture and more diversified pool of shareholder types than the US.

Though there are many ways in which European listed firms can be traded by US investors, issuers can face several challenges in being seen by the retail investors who make up a significant part of the US’ large pool of investable capital.

Cross-trading on markets like OTCQX is a cost-effective alternative to being freely traded in US dollars, during US trading hours and by a range of US investors while conforming to all necessary regulatory requirements. Independent analysis also shows that being traded on OTCQX can dramatically improve a security’s liquidity and share value, both at home and abroad. At a time when global uncertainty means the capital markets are poised for a reversion to mean, enabling trading activity has never been more important.

OTC Markets Group’s goal is to provide a public market for companies to have public equity in a way that is easy and efficient. Operating domestically and globally with some of the largest companies in the world provides US investors with easy access to foreign investments, driven by the mission to create better informed and more efficient financial markets.

More than 10,000 US and global securities – including 5,300 international securities, 550 banks, 3,400 non-penny stocks and 2,300 SEC reporting companies – trade on the OTCQX Best Market, the OTCQB Venture Market and the Pink Open Market.

The main topics we are going to discuss are:

The value of trading your stock in the US – why companies should do it, and why broadening your shareholder base at a time of uncertainty can be a powerful tool;
The myths of cross-trading – common misconceptions you may have about regulation, disclosure requirements and the mechanics of being quoted on OTC Markets;
How companies are doing it – best-in-class examples of OTCQX-traded firms that have used cross-border trading to their advantage.

Cross-border trading: The myths

It’s not expensive

Despite the opportunities offered to firms, there are still several persistent myths about cross-border trading enabled through over-the-counter markets. Even the IROs whose shares are now traded on OTCQX were confronted by their own preconceptions.

For Dr Alex Sokolowski, lead for institutional investors in North America at German chemicals firm BASF: ‘at first glance the cost of trading on a new market and the problems of negotiating local regulations were prohibitive, until discovering that trading via OTCQX ‘dampened the blow’ for corporates’.

Stephen Nightingale, director of investor relations at UK drinks maker Britvic, was concerned about getting ‘caught up in the wash’ with so many other EU and UK-based firms, and not attracting the capital he needed,’But working with OTC Markets and taking a long view on the firm’s success in the US have yielded great results for Britvic’, he explains.

The most persistent and over-arching misconception is that cross-border trading is complicated, rooted in regulatory back and forth and, ultimately, not a value-adding process. But Stefan Spath, managing director at MCAP Markets, says that as a market-maker, OTCQX has ‘simplified’ the entire process of buying non-US securities. ‘It allows for an easy conduit between US investors and non-US securities that are traded in non-US markets,’ he says.

The most persistent and over-arching misconception is that cross-border trading is complicated, rooted in regulatory back and forth and, ultimately, not a value-adding process. But Stefan Spath, managing director at MCAP Markets, says that ‘as a market-maker, OTCQX has simplified the entire process of buying non-US securities.’ … ‘It allows for an easy conduit between US investors and non-US securities that are traded in non-US markets,’ he says.

Here are a few more of the recurring misconceptions about the process, its requirements and its limits.

Surely any institution in the US can buy equities if the company is publicly listed ?

A commonly held belief is that simply being publicly listed in your home market is enough for your stock ticker to start showing up on every US investor’s screen, and that meeting local regulatory requirements is enough to get sign-off for American audiences.

Other than the largest institutions, most US investors cannot invest directly outside the US, and even when they can it is often prohibitively expensive. Investors can also be restricted by a need to trade US ticker symbols, priced in US dollars (or dollar-denominated securities) and during US trading hours.

‘If there’s no broker publicly quoting you on the OTC Market, there’s no US dollar-denominated facility for investors to see on their terminal and therefore no public market for that security,’ explains Chris King, OTC Markets’ senior vice president. ‘Equally, just because you have US investors on your shareholder list it does not follow that you’re publicly available.’

Aside from trading in less well-established areas like the Pink or Grey market – more of which later – shares in a non-US company are generally available only to the largest institutions that already have cross-border trading capabilities. Trading successfully on a US national stock market is also contingent on a company being a well-known name in the market, something that is hard to guarantee.

Though these smaller investors – investment banks, family offices, high-net-worth individuals and private investors – may manage fewer assets individually than the Vanguards and BlackRocks of this world, they form a large part of the deployable capital in the US and should not be ignored. Additionally, as they tend to be long-term holders, they are important for reducing volatility in your stock during uncertain times.

Having a US ticker solves the problem

In order to be able to recommend, solicit and distribute research to their clients, brokers must ensure the security in question complies with the Blue Sky laws in the state in which their client resides. Not being covered by these exemptions means broker-dealers cannot recommend, solicit or even discuss that security with investors.

Blue Sky laws are the individual securities laws of each US state and, for securities not listed on a US exchange, they must be followed to allow trading in that non-US security by residents of that state. ‘They are unique laws in each state designed to protect its citizens,’ explains King. ‘Even if a company is listed in a major EU market or is a household name, if it is not trading on an established US market to meet Blue Sky compliance in each state, independent financial advisers cannot trade in its shares or even talk about the firm.’

Being compliant across a growing number of states’ Blue Sky regulations means the OTCQX Market is the closest alternative to a national stock exchange. Going through OTCQX is one of the most efficient ways to gain this access on a large scale, without significant costs or administrative headaches. It also means research covering your company can be distributed freely – remember: if your stock does not meet Blue Sky requirements, brokers cannot promote it in any way.

I’m already traded via an ADR/F-share – isn’tthat enough for my firm?

While trading via an ADR/F-share allows your company’s stock to be traded in US dollars, thus dampening the currency and economic risks for US investors (including any currency conversions for dividend payments and foreign taxes), there are a few hurdles that can hamper how freely your stock is traded.

Your company’s ADR/F-share may be traded via the Pink or the Grey market, which consist of a wide range of securities: foreign companies that limit their disclosure in the US, penny stocks and shells, as well as distressed, delinquent, dark and bankrupt companies that may not be willing or able to provide information to investors.

Whatever instrument might suit your company best, an ADR/ F-share trading on OTCQX negates much of the additional regulatory burden, cost and time spent dealing with red tape. Trading on the established OTCQX Market means your securities can reach a broader audience of investors, without any significant extra costs for the issuer, says King, at a ‘fraction of the cost’ of a full US listing. ‘And you don’t need a full listing on, for example, the NYSE for your securities to be successfully traded,’ he adds.

King points to the possible exemption to the SEC Rule 12g3- 2(b), a rule under the Exchange Act that allows foreign private issuers to be quoted and traded in US dollars, in US trading hours on the OTC Market as long as they are already traded on a qualified foreign exchange, publish their financials in English and meet certain other criteria.

Trading on markets like OTCQX won’t make any difference to my stock’s value or liquidity

Being exposed to a wider audience of potential investors can only be a good thing. Independent research commissioned by OTC Markets and carried out by Oxford Metrica finds that companies traded on the OTCQX Market reaped rewards not just in terms of US ownership, but also in their home markets.

An analysis of more than 500 issuers that joined OTCQX between 2007 and 2018 shows that US ownership grew substantially after joining the market. Interestingly, trading volumes also jumped by 28 percent within a company’s home market, as well as growing 37 percent within the OTC Market. These increases were even more pronounced for companies in the lower half of the distribution in terms of market cap.

Positive liquidity reactions were recorded for companies around the world – including in Canada, Asia-Pacific and Europe – and resulted in creating value equivalent to an average 1.5 percent for each surveyed company in its home market after joining.

‘The evidence reveals a clear and consistent picture that value and liquidity are enhanced in the issuing firm’s home market on joining OTCQX,’ the Oxford Metrica researchers write. ‘In addition, there is a significant increase in US ownership once a company is traded on OTCQX.’

The best way to disprove these commonly held misconceptions about trading on OTC Markets is by looking at some examples of companies that are successfully cross-traded there.

Case studies

Britvic - If you’re more accessible, there’s more opportunity for your stock to be traded

Britvic, a mid-cap soft drinks maker based in the UK, has been working with OTC Markets since 2010 following its IPO on the London Stock Exchange in 2005.

For Britvic’s director of IR Stephen Nightingale, working with an over-the-counter trading provider was about extending the reach of the firm’s ADR program and cementing its messaging in the US. Britvic worked with its ADR sponsoring bank and OTC Markets to broaden the range of targets it could approach. ‘We had a broad market-engagement program that was multi-market, part of which was driven by the fact that many people were aware of us as a Pepsi bottler,’ Nightingale says. ‘We hoped to attract the attention of investors on a more global basis.’

He adds that it’s crucial to ‘think about the US more broadly’ and recognize that a lot of money moves outside of Tier 1 money centers such as New York or Boston. Working alongside OTC Markets and Britvic’s ADR sponsor meant the firm could access money away from these traditional investment centers.

The result was access to ‘big pots of money’ that Britvic may never have encountered otherwise due to its market cap, says Nightingale. He has also observed an indirect benefit: Britvic’s investment story is distributed more widely as a result of it being traded on OTCQX, and he has seen a corresponding increase in interest in the company’s stock. ‘We have a top 10 shareholder that became aware of us through the ADR program, and ultimately took a position through ordinary shares,’ Nightingale reveals. He adds that patience when building Britvic’s position in the US was crucial. ‘Investors are familiar with some of our brands, but explaining the concept of some of our more specialized goods – such as fruit squash – to foreign holders can be challenging,’ he says. ‘If you don’t have a global name, you need patience when building awareness of your firm and products.’

His advice for firms considering a similar move is to keep this patient approach in mind. ‘Work with a supportive sponsor and Market,’ Nightingale recommends. ‘It’s about going beyond what you’d do traditionally – whether that’s virtual conferences to reach more distant holders, or something else – to make your life easier. If you’re more accessible, there’s more opportunity for your stock to be traded.’

Orexo - The main reason to upgrade and trade on OTCQX was to improve our US investor baseNuovo paragrafo

Orexo is a mid-cap pharmaceuticals company based in Uppsala, Sweden that specializes in treatments and digital therapies, especially in the growing addiction space

Lena Wange, investor relations and communications manager at Orexo, says the drugmaker’s journey to cross-border trading started as part of a broader build-up of the firm’s US commercial operations, when Danish investor Novo Holdings was brought in as its largest individual shareholder. A switch of strategy and a renewed focus on products in the US meant that, by 2013, making Orexo’s stock available to American investors was of paramount importance.

‘The main reason to upgrade and trade on OTCQX was to improve our US investor base,’ confirms Wange. ‘It also related to a policy-stamp for our company: you have to go through a narrow regulatory process to trade cross-border. Having evidence of doing that, that we had that quality as a company, helped us a lot in our efforts to reach out to US investors.’

Today, with the company’s renewed focus on digital therapies for alcohol and opioid abuse, Wange says being traded on the OTCQX will be more important than ever as she relays Orexo’s story to the market. ‘I think that will attract more US investors to Orexo,’ she explains. ‘We see that there is a mix of potential shareholders and interest from investors in the US.’

Wange’s future focus is on getting two members of Orexo’s management team – who currently live in the US – in front of investors: ‘We will start working with our partners to map out where these investors are located and do what we can to actively reach out to them.’

As the firm is already being traded on OTCQX, Wange says the path to picking up this US activity once more has been simplified. In addition to that ‘quality stamp’ that investors see, the ease of access is particularly important, she explains.

‘I think we are in a very good situation at Orexo now to accelerate our outreach to US investors,’ Wange concludes. ‘We have some exciting products in the pipeline and, company-wise, we are in very good shape.’

BASF - The cost blow of overseas trading has been dampened by our inclusion on OTCQXNuovo paragrafo

Being available to US investors was always a priority at BASF, says Dr Alex Sokolowski, North American institutional investor lead at BASF. ‘Having a diversified shareholder base, and having access to the world’s largest equity market, is important and attractive to us,’ he explains. ‘We have about 20,000 employees in the US, too. Providing a means for investors to acquire our shares and receive dividends in US-denominated currency was critical.’

The German chemicals producer was listed on the NYSE until its listing requirements became prohibitively expensive, says Sokolowski, and some larger institutions had created F-shares to enable trading of BASF’s stock. But the desire ‘to have some insight and control’ of that trading led BASF to opt for a sponsored ADR program backed up by trading on OTCQX. The latter, Sokolowski says, is a ‘little seal of approval’ for US investors that shows regulatory requirements have been met and marks BASF as a high-value stock.

The mission to get the firm’s equity story in front of investors has been made easier by aid from OTC Markets and BASF’s depositary bank, Sokolowski says. ‘OTC Markets is great at making videos on YouTube where I explain our equity story, or bringing me in to speak to journalists, or just having a presence in the elements of the whole process,’ he explains. Another part has been getting BASF in front of retail investors, whether through specialized events and roadshows or trips to see smaller retail markets – particularly in the ‘three Ms’, says Sokolowski: Minneapolis, Madison and Milwaukee.

He adds that the main misconceptions he had about the market were quickly disproved. ‘For the company, the cost blow of overseas trading has been dampened by our inclusion on OTCQX,’ he explains. Regulation, too, was a concern but working alongside BASF’s sponsoring bank and OTC Markets has meant much of the regulatory burden was taken off the issuer’s shoulders.

‘OTC Markets handles whatever it needs to, so we get less of that burden put on us as a firm, beyond what we already do in Europe’ says Sokolowski. ‘It’s one reason we’re not on the NYSE anymore.’

The investor perspective: How it works

MCAP Markets is a financial technology developer and electronic market-maker that deals largely in ADRs, foreign ordinary shares and fixed-income assets for customers in the US, Asia and Europe. Managing director Stefan Spath has had stints as an investment analyst and as a currency trader, but just prior to his work at MCAP he was managing director of the corporate advisory division at INTL FCStone Securities.

Spath describes the advent of more sophisticated OTC Markets as arriving in the wake of a sweep of corporate regulatory reforms – more familiarly known as the Sarbanes-Oxley (Sox) Act – put in place by the SEC in 2002. Born out of the regulatory environment that resulted from a few large-scale corporate scandals, such as those surrounding Tyco and Enron, Sox required far more of companies and their boards with regards to financial reporting.

‘The SEC decided it was time to knuckle down and create more stringent rules related to governance and reporting requirements,’ explains Spath. ‘The unintended consequence was that perfectly good companies – global, blue-chip firms – were really required to duplicate their efforts in the US market if they wanted to retain a US listing. Many thought it would not bring them enough value to justify the duplicated work, so they delisted from the NYSE and Nasdaq.’

If Sox’s stringent measures could not be met, trading of these equities was restricted to ‘over-the-counter’ markets. At the time, these markets were largely composed of thousands of small or micro-cap firms, some of which were going through reorganization or bankruptcy. It meant that, suddenly, securities in multibillion-dollar European corporates might be trading side by side with, say, a bankrupt junior Canadian mining firm.

Some of those companies include household European names, like insurer AXA or food products manufacturer Danone, which delisted from the NYSE in 2010 and 2007, respectively, citing low trading volumes and the fact that US regulators did not recognize their European accounting practices in favor of the Gaap system.

In light of this, OTC Markets Group realized there was still a large investor appetite for firms that could distinguish themselves as assets worth having. The OTCQX Market emerged in the following years, with an application process whereby firms that wished to be traded would have to meet additional financial requirements and ongoing obligations. For OTCQX, each equity also requires a third-party broker-dealer or US securities attorney to attest that the company in question qualifies to be traded. AXA and Danone, which began trading on OTCQX in March 2010 and May 2011, respectively, immediately saw the benefits.

Investment conduit

Firms like MCAP make markets in several thousand different securities – most of which are non-US based – for their clients. Spath describes his firm’s role as ‘a conduit between US investors and non-US securities that are traded in non-US markets’ and says the role of markets like OTCQX has ‘hugely simplified’ the post-2002 trading environment. He illustrates the point with an example of a Danish technology company that wishes to access a pool of US investors, but cannot contend with the large regulatory burden of listing on the NYSE.

‘Let’s say the firm has appointed a sponsor – a US broker-dealer or attorney,’ Spath explains. ‘Once we get a few regulatory steps out of the way, the market-maker will publish a quotation with the security, typically based on where the security is quoted in its home market. This includes information like the security’s individual volatility, or any fluctuations between the US dollar and the security’s local currency, for example. Then it is quoted with a bid and an offer price so brokers can get in touch to buy it.’

Orders are constantly coming in from investors throughout the day, before stockbrokers aggregate them and route the buy or sell commands through to market-makers quoting the securities – all during US Market hours and in US-dollar amounts. These brokers facilitate a flow of deals from the largest institutional investors through to everyday retail buyers, who make up a significant proportion of US capital pools.

‘If the order came through to MCAP, we would fulfil it by selling the securities to the end-investor,’ Spath says. ‘If we don’t have inventory of that stock, we have created a short position on our book. Then the following morning, the trader responsible for that name will go to Copenhagen and buy those shares from the local exchange.’

Indeed, the stock symbol visible on markets like OTCQX would indicate that while – in this example – there is a Danish company’s stock traded on the market, the instrument in question is a synthetic symbol that represents actual ownership of that actual share that is traded locally.

Legal constraints

There are also the Blue Sky Laws to contend with, which, among other things, require share issuers to register and provide detailed financial information on any offerings they make. Spath says the regime can be ‘inefficient’, as it requires a board of state-appointed regulators to pass any share issue, and their decision is often based on factors like a firm’s business plan, which can be misleading.

OTCQX has brought compliance for Blue Sky regulations in-house for a number of states, so companies can have their securities traded and even recommended by brokers where applicable. It means trading can be a ‘two-way street,’ says Spath, so brokers can recommend to their clients what they perceive to be a must-buy security.

‘OTC Markets Group is trying to centralize, organize and evolve the over-the-counter space,’ he continues. ‘It wants to foster more activity and raise the profile of the market, to make it equivalent to a national stock exchange. It’s a sign of modernity: we don’t need to have a physical exchange anymore.’

This means the market-maker is ‘simply a conduit that allows a US investor to buy that foreign share seamlessly,’ adds Spath, thanks to ‘all the hurdles being removed.’ The exchange serves as a ‘three-way’ process, he explains: the company itself gets more visibility with investors; investors can get further information about an overseas stock to make a better-informed decision about what to buy; and market participants can freely enable this trading activity.

And, at times like this, when the capital markets have come to be defined by a period of unprecedented uncertainty, being prepared for easy trading could make all the difference, adds Spath: ‘As things revert to a mean, we will see a rebound in the market. When it comes to international securities traded over the counter that would like to be on OTCQX, if they are waiting for the rebound to occur, there’s going to be a tremendous bottleneck to the regulatory hurdles to get on the market.

‘I’ve been telling companies that in order to get through them, it makes sense to start now, so they’re ready and visible when the rebound inevitably occurs.’

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

How to re-engage with the changed customers

alessio.defilippis@gmail.com (Alessio De Filippis) — Sat, 26 Sep 2020 18:25:55 GMT

Downturn period and covid make your customers less able and less willing to spend than before.

The coronavirus shock has disrupted more than jobs, supply chains, and financial markets. Your customer has changed fundamentally, too.

The number one task for many companies now is discovering where their B2C and B2B customers have moved to and re-engaging with them.

COVID-19 is a different beast than recent economic crises and recessions such as the Great Recession of 2008 and the Mideast oil crisis, whose causes were financially driven. The fundamental driver of the pandemic is health and safety concerns and hence customer driven. Customers’ immobility and desire to be safe in the current environment has resulted in volatility in purchases and productivity across idiosyncratic product categories, resulting in a net economic crisis of a type that has not been witnessed by anyone alive today.

Government-imposed quarantines, self-isolation, and closures of stores and offices have further forced changes to customers and hence firm-based behaviors. The outcome of customers’ health and fears has resulted not in a traditional recession but a “deaccession,” where supply and demand exist, but customer-access to products and services has been significantly shut off.

All in all, this set of circumstances and stricter budget constraints make customers less able and less willing to spend compared to past recessions. How will you find them? How will you engage them?

What is clear in the COVID-deaccession is that this change in customer behavior is pushing firms into a new “directional reality.” Firms need to adapt to shifting customer wants by engaging a more customer-centric philosophy. Rather than expecting their customers to come to them, they need to go to their customers.

Past research demonstrates that firms who maintain or accelerate customer-centric philosophies consistently outperform firms that do not. In fact, they gain market share from competitors who cut back on customer-centric investments.

During this COVID-deaccession, it is even more critical for firms to become more customer centric by researching and understanding their customers’ new problems caused by fear, isolation, physical distancing, and financial constraints, and attempt to structure their offerings to meet these new unmet wants and needs.

The velocity or rate of adaption that firms need to adjust to a new directional reality will depend on customer demand. Industries with decreasing customer demand—offline entertainment, hospitality, real estate, industrial commodities, and suppliers to these industries—need to adjust rapidly to give them a better chance of surviving.

In contrast, industries with increasing customer demand—grocery stores, online entertainment, teleconference providers, and their suppliers—need to adjust to this directional reality at a slower, yet definitely needed, pace to help sustain growth for the longer term.

Whether industries are experiencing decreases or increases in demand, all firms and organizations need to take a step back or forward and ask themselves: What should be my minimally viable strategy to get through these unprecedented times?

To adapt to a new customer-centric directional reality, Rohit Deshpandé from Harvard proposes an alternative to Ansoff's (1965) growth strategy matrix (see table below). The proposed 2 x 2 matrix is categorized by whether the firm is competing with existing versus new or modified products and services, and whether it is competing in current or new markets (i.e., new customers and/or new geographies).

First Quadrant: Firms stay in the status quo or pre-COVID situation. As discussed earlier, times have changed, and business cannot be run as usual. Firms must go to their customers instead of just relying on their customers coming to them. Thus, maintaining the status quo or first quadrant behavior is not advised. We need to go beyond status quo in the new abnormal.

Second Quadrant: Firms create new products or services. Firms may consider adding new services or tiers of products that meet customers’ deaccession-based basic unmet needs. Walgreens allowed customers to purchase a number of products at their drive-through because of their fundamental utilitarian-based health and safety needs. TechSee is providing European organizations free access to their artificial reality (AR) annotation products on mobile phones. AT&T, Cisco, and Zoom have enhanced their network capabilities for increased demand in bandwidth. In addition, numerous small businesses like restaurants and home goods retailers try to match increased demand by allowing customers to purchase by email, messaging services, or phone orders. While the first quadrant, or status-quo, is dead, the new normal is the second quadrant.

Third Quadrant: Firms expand into new customer markets with their existing products or services. For many companies, demand in the first quadrant has dropped sharply—they must find new markets to grow. Hence, American, Delta, and United Airlines are now employing airplanes previously targeted for passengers to fulfill cargo deliveries. For some firms, their products or services are now useful and in demand by new customer bases. Cintas is expanding its business-cleaning offerings to new markets to match new demands and unmet needs. Zoom removed time limits from basic accounts for primary school educators who now need to use its teleconferencing software for teaching. Fan Interactive Marketing, which provides customer relationship management and digital marketing tools for entertainment venues and sports teams (largely unused during the pandemic), switched to targeting small- and medium-sized traditional businesses struggling to survive.

Fourth Quadrant: Firms diversify simultaneously into both new markets and new products and services. Firms whose customer demand for their core products and services has decreased need to find new customers for new products and services in segments experiencing steep increases in demand. Thus, Dyson, GM, Ford, Volkswagen, and Tesla attempted to produce ventilators for hospitals, British Honey Company is making hand sanitizers, and Louis Vuitton, Nivea, and Zara are making surgical masks, disinfectants, and other medical-related devices.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

The economic downturn is pushing entrepreneurs to accelerate "go to market"?

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 25 Sep 2020 18:58:20 GMT

Analysis of past downturns suggests that timing errors will happen again.

Research by Maria Roch e highlights that entrepreneurial success may depend on more than a great idea, plenty of connections, and financial backing, new research suggests. The entrepreneur’s job security may also play a significant role:

Uncertain job prospects, such as those caused in financial downturns, sometimes convince entrepreneurs to improve their financial situation by rushing new ventures to market. In doing so, they sacrifice quality standards and ultimately underperform their peers;
This is particularly problematic in high tech, where entrants need strong intellectual capital, talented specialists, and access to significant financial resources, prerequisites that are hardly conducive to spur-of-the-moment actions.

The reasearch studied 3,025 US founders from 2005 to 2012 and their 1,747 startups in the biotechnology and medical device sectors during previous economic downturns. Their research was conducted before COVID-19 came knocking this year. An earlier version of their paper was published in 2019.

The first finding is that an increase in the unemployment rate is associated with a substantial rise in the share of entrepreneurs who are most sensitive to worsening labor market conditions (mainly PhDs/postdocs and individuals previously employed at small firms). Specifically, the results indicate that a one percentage point increase in the unemployment rate is associated with a 36 percent rise in the odds that founders are PhD/postdocs relative to our benchmark of professor founders. Similarly, the odds that founders were previously employed at small firms increases by 29 percent.

"Examining venture performance in the first few years after creation, we find that startups founded by individuals most sensitive to labor market conditions display lower financial and innovative performance than startups founded by entrepreneurs who are less sensitive. Our results suggest that these different responses to worsening labor market conditions explain at least 11 percent of the established negative relationship between unemployment and startup performance outcomes." - Maria P. Roche.

An extensive number of additional analyses we conducted confirm that findings are most likely driven by changes in individuals’ opportunity costs of starting new ventures as a result of worsening labor market conditions and not by changes in skills, or technological and financing opportunities.

Those individuals most sensitive to business cycles are enticed to lower the quality threshold, the bar, for turning existing ideas into new ventures during downturns, resulting in negative startup performance outcomes.

Similarly COVID-19 may induce individuals who are most sensitive to labor market conditions to start new ventures in order to avoid being unemployed, or to escape another type of unfavorable job situation. As a consequence, the quality threshold that the idea underlying the startup needs to pass may be lower than during normal times. Considering findings from other periods of economic distress, lowering the idea-quality bar may then imply long-term negative outcomes in terms of a venture’s innovative and financial performance on average.

Funding opportunities may dry up as venture capitalists increasingly struggle to raise new funds or exit from their existing investments. Investors may target less risky projects or projects related to COVID-19, primarily in the life sciences sector, as the latter might have higher expected returns. As a result, entrepreneurs outside of the life sciences or pursuing more risky projects might find it difficult to obtain adequate funding.

To the extent that individuals may be induced to start new ventures out of “necessity” (in a very broad sense of the term), they may have to lower their quality threshold for turning ideas into new ventures. This mechanism, coupled with the diminished availability of financing, may have a negative impact on entrepreneurs’ ability to obtain cash, exit, and innovate.

Some advices:

According to Maria Roch e, she would advise those with outside options or operating in areas unrelated to COVID-19 to consider waiting, if possible, until the economy rebounds. A temporary period of unemployment or reduced compensation/advancement prospects may be the better alternative for individuals relative to initiating a venture with poor prospects.

After all, starting a company takes a lot of time, resources, and energy, and is a long-haul game. Conditions at the time of founding matter, so entrepreneurs may want to wait for better times and develop their ideas further to extract more value from their efforts.

Maria Roch e: "Relative to past recessions, when studying the current pandemic we need to consider a host of factors that are simultaneously at play. Just to name a few, there is pervasive uncertainty regarding the virus itself (when can we expect a vaccine?), and the pandemic has hit countries and regions in very different ways. In the meantime, we are faced with trade disputes, consumers are changing habits, and companies are adjusting their business models. Untangling the role of any of these factors presents a tough puzzle for any researcher to crack.

My goal for the next few years is to dig deeper and better understand how we can design and manage our most imminent environments—our neighborhoods, may they be of physical, social, or intellectual nature—to best support knowledge creation, diffusion, and performance outcomes."

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Tech giants, trade war and COVID-19

alessio.defilippis@gmail.com (Alessio De Filippis) — Fri, 18 Sep 2020 14:14:11 GMT

The world has grown rapidly more complex and needs more collaborative frameworks

The trade war

In 2019 the U.S. government threatened to impose 100% tariffs on French goods, including champagne and cheese, in retaliation for France’s planned 3% tax on revenues generated by large digital companies in its territory. France agreed to postpone these taxes until the end of 2020 to avoid a trade war, while hoping that the Organization for Economic Co-operation and Development ( OECD ) would come up with a multilateral solution by then.

They haven’t. Instead, the world has grown rapidly more complex. An international trade war now seems inevitable, as governments worldwide undertake massive Covid-19 rescue efforts while facing a large shortfall in tax collections. If finding common ground was difficult before, it likely feels impossible now. The only way it can be solved is through historic collaboration.The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

Meanwhile, the World Bank estimates a 5.2% contraction in global GDP in 2020, which will be the deepest recession the world has seen in decades. Covid-19 will further negatively impact national incomes in the following years because of lower investment, an erosion of human capital through lost work and schooling, and fragmentation of global trade and supply linkages. Governments will have to make extraordinary efforts to mitigate the downturn and offer fiscal and monetary policy support to their citizens and corporations.

Tech giants

For a long time now, digital giants such as Google, Apple, Facebook, and Amazon (GAFA) have found innovative ways to reduce tax payments to foreign governments, a possible factor in foreign governments’ lowered tax collections. Unlike the local physical companies, digital giants don’t need physical infrastructure, factories, or warehouses to do business. Their lack of a “permanent establishment” makes it difficult for any government to ring fence or identify their economic activity in any region — forget calculation of profits and collection of taxes. In addition, the main cost of a digital company is developing intellectual property, not labor, raw material, or energy. It can easily shift its costs — and thus profits — from a high-tax country to a low-tax country by relocating its intellectual property and imposing large royalty payments. As a result, digital giants are currently one of the largest users of innovative transfer pricing and income shifting schemes.

Covid

This shift in economic activity and tax base from local companies to digital giants has been going for more than a decade; Covid accelerated it. The irony is that during Covid-19, while countries are hurting, digital giants have further captured the market and revenue shares from local companies. They are doing really well in this crisis, accelerating the global tax transformation. For example, Amazon took over a large part of profits that would have otherwise accrued to local retailers, who would then pay the taxes to the local governments. Covid has also had a devastating impact on local newspapers and their advertising revenues , which is now shifted to giants like Facebook and Google. Governments thus face one-two punch: fund local recovery and welfare, while not getting enough taxes from digital giants who have usurped their tax base. They have no choice but to look towards digital giants to meet at least a part of their budgetary deficits.

The discussion

imilarly, discussions about how to more fairly tax digital giants have also been going on for more than a decade ; Covid created a new sense of urgency. Roughly 137 countries have been working diligently with European Union (EU) and OECD to find a collective solution. The solution revolves around a revenue-based tax on digital giants, that is, a fixed percentage of gross revenues remitted by local companies to digital giants. This system has two advantages. First, it eliminates the need to calculate profits, which can be reduced by transfer pricing schemes. Second, the burden of deducting tax and remitting to the local tax authority is placed on the local person or company, eliminating the need for a foreign government to run after a company that is beyond its administrative control. The U.S. participated in these negotiations and discussions until June, when they withdrew .

Without U.S. cooperation and with Covid raging around the world, many countries have implemented their own unilateral solutions , worsening the risk of tax disputes and trade tensions. For example, Austria recently implemented a 5% tax on digital advertising revenues. India and the United Kingdom have enacted a 2% tax with retrospective effect. Spain is halfway through legislating a 3% tax. Canada is considering a 3% tax. And Italy has enacted a 3% tax with retrospective effect.

Since the burden of new revenue-based taxes falls disproportionately on digital giants that are U.S. corporations, the U.S. government threatened to retaliate against those countries with its own trade restrictions and tariffs. It has launched a series of trade investigations — whether these actions amount to an unfair trade practice. The affected countries include: Austria, Brazil, Czech Republic, India, Indonesia, Italy, Spain, and Turkey, as well as the European Union. If the French incident is any guide, then the U.S. inquiry promises to be an aggressive trade war. Note that when the U.S. imposed tariffs on France, it also affected U.S. consumers and restaurants who buy French cheese and wines. So, any tariffs imposed by the U.S. not only affects companies, they also affect U.S. consumers and companies that obtain goods and services from foreign countries. Those countries in response could impose their own tariffs and measures affecting U.S. exporters.

Such a trade war is harmful to global economic stability and progress at any time; it is especially harmful when the world needs recovery from Covid-driven historical downturn. The world economy is already facing a U.S.-China cold war and Covid-driven fragmentation of global trade and supply linkages. Moreover, a confrontational approach would impede technological progress around the world. Foreign governments can raise unfounded fears , withhold the expansion of digital services, and enact protective legislations . That approach would not only harm economic recovery but could make digital giants miss out on potential revenues and market growth.

It is clearly a time for calmer heads to prevail. The U.S. must resume participation and play a meaningful role in OECD’s multilateral discussion, instead of just threatening to derail the process while sitting on the sidelines. Foreign countries must also be more patient and postpone their implementation at least until the end of this year, by which time, OECD has promised a more general solution. And digital giants must become more willing to contribute to the world’s economic well being, which is so essential for their own growth and profits. After all, the shareholders of digital corporations have received tremendous returns on their investments during Covid-19 times . Mark Zuckerberg recently expressed willingness to support tax reforms in Europe, and concedes that Facebook “may have to pay more.” A more collaborative approach would be a win-win solution for all.

If our content helps you to contend with coronavirus and other challenges, please consider subscribing to HBR . A subscription purchase is the best way to support the creation of these resources.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Company's real impact and IWAI index

alessio.defilippis@gmail.com (Alessio De Filippis) — Thu, 10 Sep 2020 13:26:43 GMT

Two promising research from Harvard Busienss School and Morning Consult about the future of business with a common fil rouge

Two interesting contributions to the discussion in business environment about the future of business arrive almost in the same moment:

From Morning Consult, a research about the consumers' perception of what we, in Libenitum, define "Political Branding";
The publication of the first part of Harvard Business School study about the new Impact-Weighted Accounts Initiative (IWAI) .

Both the researches point out that profit is not a measure of company impact in the same way GPD is not enough to measure the wealth and sustainability of a nation's socio-economic performance, in short and long term too. The investors, employees and customers are starting to be aware of that and at the same time they are increasingly aware of the difference between brand narrative and brand behaviour.

In 2021 Harvard Business School will publish the final report based on Impact-Weighted Accounts Initiative (IWAI). The first overall index for measuring enterprise real impact.

Ronald Cohen: “The era of impact transparency has begun, and it is moving the goal posts for businesses and investors. Technology and Big Data have combined with longstanding efforts by many individuals and organizations to make the measurement and valuation of corporate impact a reality. With the arrival of impact transparency, impact and profit set the new rules of the game.”

According to the data just published, about the cost of the environmental impact of 1,800 companies (the rest of the research will be published in 2021) seems that the "true profitability" of companies is very different nature than it seems. It becomes apparent that many companies are creating environmental costs that exceed their total profit (EBITDA). Of the 1,694 companies which had positive EBITDA in 2018, 252 firms (15%) would see their profit more than wiped out by the environmental damage they caused, while 543 firms (32%) would see their EBITDA reduced by 25% or more.

Performaces vary a lot among industries and economic segments, in the challenged oil and gas industry, where 75% of companies would see more than a 25% reduction in EBITDA, a few best performers have overtaken their competitors.

On the other side companies generate positive impact, for example Ronald Cohen and George Serafeim highlight that Intel’s employment impact as an example. In 2018, it created approximately $3.6 billion of positive impact in the U.S. through the wages it paid and the jobs it provided in areas of high unemployment. Intel can increase this impact by improving its level of diversity and offering more equal opportunity for racial minorities and women to advance within the company.

Why IWAI is very promising

Ronald Cohen and George Serafeim: "Impact transparency will have far-reaching consequences. First, instead of taxing all of us to remedy negative impacts such as pollution, pay below the minimum wage, and products that cause obesity and ill health, governments will be able to tax companies directly for the harm they create. They will also be able to provide direct incentives — in the form of reduced taxes, subsidies or preferential procurement — for companies to deliver positive impact through their products, operations and employment practices."

Furthermore the observation of 13,000 companies performed in the analysis shows that a significant correlation between negative environmental impacts and lower stock market valuations in many industries, including chemicals, apparel and construction materials. Such a correlation does not yet appear in other industries such as utilities, hospitality, or industrial conglomerates. But it can be expected to appear once impact transparency enables investors to reliably account for impacts in their valuation analysis.

Therefore investors will price the environmental and social impacts of companies into their investment analysis. Firms with greater negative impact generate less investor interest, which reduces their stock market valuation and raises their cost of capital. Impact transparency will, therefore, motivate management to improve corporate impact, in order to increase stock market value and, sometimes, their own compensation too.

There is also a customer side: transparency will allow customers and employees to align their purchasing and career choices with their values. “Impact-washing” is currently widespread because relevant impact data is sparse. For instance, all automobile manufacturers claim that their products benefit society more than the products of their competitors. But when we measure all manufacturers’ product impact, according to safety, affordability, customer satisfaction, fuel efficiency, and emissions, we find that only a few companies, such as Tesla, Renault, Hyundai and Nissan, can justifiably make these claims.

How far are we from adding impact to the profit paradigm that has driven capitalism since its origin?

Transparency and accountability go hand in hand. To date, the absence of effective impact measurement has obscured the accountability of companies for the harm they cause. Rewriting accounting rules to include impact will alter investors’ assessment of corporate performance, leading them away from negative-impact companies to positive-impact ones, and catalyzing a change in corporate behavior.

Last year a research by HBS identified 56 leading organizations around the world that practice some impact-weighted accounting. This list is growing every week. Danone, the French food leader, has just published earnings per share that are weighted for its environmental impact. Detailed methodologies, data sets, and guides now exist for the preparation of impact-weighted accounts that reflect the operational, employment and product impact a company has on people and the environment. The Covid-19 crisis will exacerbate already flagrant inequality, intensify the need for a fair and sustainable recovery, and accelerate the shift to impact-driven economies.

The introduction of impact-weighted accounts is sped by a global network of innovators, companies, investors, NGOs and other stakeholders. These actors have come together through the GSG (Global Steering Group for Impact Investment) and the IMP (Impact Management Project) which have initiated the IWAI with Harvard Business School. Numerous other organizations in the field are contributing directly and indirectly to hasten the shift to the new paradigm.

The sooner governments mandate the publication of IWAs — and align companies and investors with the great effort needed to tackle climate change, inequality, and Covid-19 — the better off our society will be.

At a microecnomic level is a responsability of every entrepreneur, investors, and customer to start to modify everyday behaviour (e.g. what to buy, how to invest) according to the impact we want to have and the world we want to share.

Cross-industry innovation as a competitive advantage

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:57:38 GMT

Someone else has already solved your problem

Vullings and Heleven :

… Cross-industry innovation is a clever way to jump-start your innovation efforts by drawing analogies and transferring approaches between contexts, beyond the borders of your own industry, sector, area or domain ...

Cross-industry innovation is not a new phenomenon, but we are experiencing an increasing attention mainly due to the combination of maturing industries and disruptive change that makes the traditional approach to innovation within the boundaries of an industry less effective.

Probably the best way to describe and lead enthusiasm in cross-industry innovation is by using inspiring case history:

BMW iDrive has been transferred from video gaming industry
Philips “ Pay per lux ”, Light as a service as the new business model for LED products
In 2006 Norwich Union launched first Pay as you drive insurance service
Circue du Soleil can be considered the mix of theatre and circus world, matching experiences and assets from both industries

Most businesses are deeply rooted in the conventional wisdom of their own industry. Managers and strategists do what had proved to be the best way of doing things in the past. They praise their expert knowledge of their industry as a strong foundation of their success. This approach effectively prevents the transfer of ideas from other industries and reduces the opportunity of generating value in a VUCA world .

Sometimes the answer is to look to best practices, sometimes is going beyond and find new paths , ideas and sources of inspiration. As Ramon Vullings writes: go from best practices to next practices!

Don’t be lazy, let’s start looking to the outside world: different sectors, art, nature, other companies …

M. Heleven, R. Vullings Not Invented Here: Cross-industry Innovation
Marc Heleven: Cross industry innovation toolkit: 50 inspiring companies and industries you can learn from
Ramon Vullings: Cross-industry innovation - How to innovate by learning from other sectors

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Silicon Valley is not a one best way

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:57:36 GMT

When we think about innovation ecosystem and business we all think to Silicon Valley. It’s a mistake.

I’ve recently read an article of Greg Satell about the limitations of Silicon Valley model . It’s been an epiphany. He’s right, Silicon Valley it’s not suitable with all innovation paths. We need other scenarios and frameworks.

I’m firmly convinced that in general there is not a one best way, I usually doubt when I speak with someone that share with me an apologetic view of “the solution”. Economic development, social development, innovation, like every other human activity is a cultural manifestation and therefore is at least country specific.

There is not a one best way of cooking, there is not a single way of doing business, there is not a single framework for the innovation driven economy.

As Greg Satell argues in the article, “ Silicon Valley DNA ” is the typical mantra for doing business in innovative business sectors even for policy maker that talk about Silicon Valley like the model to be replicated in other countries or economic systems. It’s like talking about Marshal Plan when politics talk about the economic development of underdeveloped countries, more a claim than a deep comprehension of the meaning of what Marshal Plan was. Silicon Valley has become something similar to Marshal Plan, a sort of chimera. Silicon Valley ecosystem was born more or less 50 years ago, we can consider the milestone of the generation of this model, the first seed, when in 1968, “ … an investor named Arthur Rock backed executives from Fairchild Semiconductor to start a new company, which would become known as Intel. … this transaction in large part became the model for the Silicon Valley way of doing business.”

Silicon Valley in a nutshell

Silicon Valley has become an extraordinary engine of entrepreneurship, it’s a charming and inspiring vision, but for a specific country in a specific time, and, mainly, for a specific industrial sector, ICT one. It’s not replicable because is a combination of specific features.

I want to focus on the most specific feature of this model, and of the related myth, the venture-funded entrepreneurship. People with great minds and high risk attitude (usually engineers), become entrepreneurs, usually they founded a tech company, make money, then they invest money in new projects as small or big venture capitalists. It’s a virtuous circle, a self-perpetuating model that has worked very well. It’s a marvelous model, but not suitable with every circumstances, perhaps not suitable with the next innovation era we are approaching. The era of genomics, longer Death Valley phases (more than the standard 5 years), not mature technologies to be commercialized, about no simple products (like an app).

Just to be clear, Silicon Valley model is a great ecosystem, but, what about hard technologies? What about not mature technology areas like genomic or green technologies (for example the implementation of a circular economy practice) that need huge investments for a long period of time? What about product that need huge investment for the development of a prototype? What happened when you don’t have a prototype of an app that can be built with the support of two friends but you need a lot of money for creating a new type of engine for airplanes?

Silicon Valley model work well when you need money for the go to market of a digital product, something of a mature sector like consumer software, you have an easy prototype to show, you can scale up gradually but probably the death valley phase will be short. In other words, Venture capitalists in the Silicon Valley model, accept the market risk, the financial risk, not the technology and the product risk.

Think out of Silicon Valley

When we have to deal with a technology risk, we need a different model, a model for sectors like green technologies for example, with great microeconomic and macroeconomic impacts, but that need money for trying to transfer a theory, a paper work to be turned in a complex prototype, or different prototypes, to be tested probably with the help of a great company (able to produce the prototype or engineering the production capabilities for deploying and delivering the product). You need an ecosystem of patient capital, with high risk propensity, and industrial support of endup companies. This is not Silicon Valley model.

Geg Statell suggests to take example by grants model of universities, foundations and other kind of organizations that are not focused on rapid profits. He’s right, but probably that’s not the only solutions, it’s not enough. In Europe there is a great support of public grants for example, we have a long experience, but it doesn’t work in a very efficient way (the measure of the inefficiency is simply the economic performances of the innovations supported, that are not very high compared to USA innovation driven sectors), probably for the chronic inefficiency of the public sector, probably for the lack of accountability culture and processesin public and private areas, but I’m quite sure that is not enough.

In my opinion, a true hard tech ecosystem, and more in general, a true innovation ecosystem outside Silicon Valley, needs something that can be boosted by endup companies and open innovation principles.

A more or less traditional approach are what are experimenting by the corporate venture funds. A big company with technical and financial resources cansupport a new idea (a startup usually) founding the scale up or more recently, founding and helping with tangible and intangible resources the R&D phase, the validation of the industrialization feasibility and the market analysis.

But a more game-changer alternative is the so called open innovation approach. If you are an endup, you have capabilities in terms of industrial, commercial and know how in general. You are good in selling the product you are able to do now. But you need to innovate to remain competitive, and you have all you need to take a concept of proof or a scientific idea and transform that idea in a business success. If you are a research lab, a wannabe entrepreneur, you lack all the stuff endup has, but you are able to create concept of proof and you are more performing in “listening the market without constraints”. Starting from this premises, what is needed is something that, like Silicon Valley, can become the self- perpetuating ecosystem of innovation suitable with hard technology innovations and probably supported by the dialogue between endups and startups.

There is a lot of hype around open innovation, the right way of approaching is not yet clear, there is not a clear answer, but a lot of promising experiment like Philips Campus in Eindhoven it’s probably one of the possible way to experiment the future landscape of innovation in business.

It’s an unpredictable and exiting future, we have only to try and learn from our experiences and mistakes and see where all these opportunities will bring us.The body content of your post goes here. To edit this text, click on it and delete this default text and start typing your own or paste your own from a different source.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Problem Solving

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:57:35 GMT

Searching for unexpected answers trough cross-pollination

If you are developing a new airplane, try to ask insights to a musician. Exiting from the usual path can be worthwhile.

During my professional career, I have supported endups and startups, in different countries, influenced by different cultures and approaches, competitive environments and organization cultures. Sometimes more visionary enterprises sometimes less. Sometimes very professional and focused on planning and executions, sometimes very agile (and anarchic). I’ve collaborated with high-tech and traditional businesses.

Usually I’ve been involved in projects because there is a problem to be solved, or a new opportunity to be caught, and now I’m firmly convinced that solutions are not always a mere issue of engaging a subject-matter expert.

During a speech for TEDXNASA, Stephen Shapiro highlighted that “If you’re working on an aerospace challenge, and you have 100 aerospace engineers working on it, the 101st aerospace engineer is not going to make that much of a difference. But you add a biologist, or a nanotechnologist, or a musician, and maybe now you have something fundamentally different.”

When you are looking for the solutions to your business or technological issues, the usual approach consists in “Call an expert”. This is right but not always the best approach.

If you have an issue, probably is because common paths and frameworks are not enough. This doesn’t mean that usual approaches are wrong, they are the results of years of practical and academic work, the contribution of experienced consultants and so on. They are precious, but often there is potential in looking outside the usual areas.

This approach can lead to more creative solutions, to challenging ideas or questions, can be the opportunity to create game changer ideas and solutions.

It’s important to pinpoint that is not only a fancy way to feel more “smart” or “trendy”, it’s practical. You need a solution, you need new ideas, you need something that can be considered valuable for your business, asking for alternative perspectives can give something valuable.

For example, if you have an issue about your digital strategy or your digital architecture, first think to do is seeing what other organization have done in terms of digital evolution or transformation, what technology solution they have chosen etc. If you need answer, you start calling consultants or expert of digital transformation. That’s the usual path, but sometimes there is a potential in other opportunity windows, that you are not exploring. Try to ask to a biologist or an artist, sometimes a total different mindset can lead to new point of views new solutions and so on.

Try to ask about their experience in day job, what they think about your problem, try to boost an idea generation process. Sometimes can be useless, sometimes a sharing of different mindset can create unexpected and extraordinary new set of insights and opportunities. The time to be invested can be very profitable.

Engaging unlikely sources of ideas, their different perspectives can give you surprising answers, but also unexpected new questions, because they simply don’t know what are you talking about and they challenge you even while trying to understand. That’s a powerful training that challenge your most rooted certainties.

Usually, human instincts suggest to underestimate ideas generated by not expert, the resistance is logic but counter-productive and can leads to a loss of opportunities.

The more the challenge is unexpected and complex, the more the alternative contribution can be worthwhile.

Moving into unexplored territories, with no preconceptions, is a longtime investment, with significant effort, but can leads to amazing surprises.

To Learn More:

Simple Ways to Spot Unknown Unknown s , Dorie Clark, Harvard Business Review

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Innovation in end-ups and start-ups, a unique ecosystem

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:57:33 GMT

There are still many differences between end-ups and start-ups:

( John Maeda )

The challenge for both is to seek greatness by learning from each other.

According to the Wall Street Journal , large consumer companies are investing lots of million in venture fund to help finance start-ups. The main motive is that growth is increasingly hard to come by, so they are looking to entrepreneurs to help them find it.

For example, over the last four years, the entire U.S. grocery store’s entire food and beverage category grew just 2.3% a year. The largest 25 food and beverage companies contributed only 0.1% of that annual growth rate. The growth came from 20,000 small companies outside of the top 100, which together saw revenue grow by $17 billion dollars.

So, as already said, start-ups and established companies would both improve their success rates if they collaborated instead of competed: start-ups excel at giving birth to successful proof of concepts; larger companies are much better at successfully scaling proof of concepts.

Some big companies are restructuring their organization to manage the trade-off between startup and established companies approach and to manage new business (e.g. Google restructuration to become Alphabet).

A new theory, based on a research at Bain & Company and developed by Chris Zook , affirms that, even if conventional wisdom says that start-ups are better at launching and growing a new business than large companies, established companies that follow three main principles (pursue businesses that protect and defend the core, establish a repeatable formula and look for a founder’s mentality) are ideal places to found a new business. In fact, they are much more likely than a start-up to produce profitable growth.

The research shows that large companies that leverage the strengths of their strong core business have on average about a 1-in-8 chance of creating a viable, large scale new business: you can benefit from a strong existing business and the scale advantages of an established company. We may be seeing the rise of a new type of multi-business company: those that use their advantages of scale to start or acquire smaller businesses, grow them in size and perhaps even spin them off again.

Success path seem to lay in the dialogue between the two worlds, bridging the distance between the two ecosystem will be a key success factor in next years.

Bain &Company: The strategic principles of repeatability
Harvard Business Review: Big companies should collaborate with startups
John Maeda: Startups are great, but we can learn a lot from “end-ups,” too
Harvard Business Review: When Large Companies Are Better at Entrepreneurship than StartupsThere are still many differences between end-ups and start-ups

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium.

Founder and Partner of Libentium, developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Technology Scouting and Open Innovation

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:57:31 GMT

How to see over the horizon

Technology Scouting is a main driver of Open Innovation processes.

New technology scouting is a never-ending process that needs a precise methodology to guarantee a constant commitment and therefore an overall effectiveness.

Technology scouting is a common practice among end-ups and start-ups. It’s an iterative process that collects information about new technologies and opportunity windows. Every organization find new technologies, collect information about them, validate them, find alternatives, select the most suitable or attractive solution. Like in an innovation funnel framework, it starts with the identification of company’s needs. The second step is the deploy of a scouting phase for collecting a set of alternatives. The third one is the assessment of the alternatives and, after the identification of the best scenario, it’s possible to start the final phase, the adoption of the new technology.

The first step refers to the company’s needs identification that is less easy than it can seem. It’s needed a clear vision of company’s present and future internal and external context, it’s needed a precise list of needs and subsequent details that furthermore will lead to the setting of selection criteria for the scouting step.

During the searching step, some profiles responsible for the scouting are needed that can be involved in a wide scouting of every possible scenario. The scout profile is a real skilled resource with the suitable experiences and capabilities.

The selection step is based on the criteria identified during the staring phase. The more interdisciplinary is the team responsible for the selection step, the more effective and disrupting is the potential output of the section step. Nevertheless, it’s needed to manage the tradeoff between the complexity of managing a multidisciplinary team and its potential performance.

This is a very risky choice.

Sometimes not only scientific or technologic profiles are needed but they have to collaborate with a set of people with different backgrounds and languages like sociologists or psychologists, marketer and so on..

The selection step is the most iterative moment of the overall process. It’s important that the team has a clear focus and furthermore is aware that the selection phase has not to end with the selection of a solution among the alternatives they have. The output can also be that none of the solutions proposed are suitable for company’s needed. If so, a new searching phase can start. It’s even possible that no solution is available on the market and the team must be aware of that possible output.

Solution and first approach

When the team has identified a solution it’s needed a first approach with the organization that has the intellectual property of the technology in order to assess the opportunity of a negotiation (Do they want to collaborate on the further development needed? Do they want to sell the technology? Can they share information about the technology? Etc.). During this step, It’s needed to collect all the information available on the technology to assess the feasibility study and understand if the hypothesis made on its suitability can be confirmed.

If everything has gone in the right way, the scouting ended and the company can start the adoption of the technology and therefore the development of a new product, process etc.

Searching new technologies and starting a collaboration with scouts, other companies or universities that own the technologies that you need requires a deep external orientation, a cultural commitment towards collaboration and openness that not every organization has.

It’s is not easy, because the overall process needs organizational, cultural and financial capabilities, but an open innovation approach is a powerful enabler of company’s competitiveness and innovation attitude in the modern unpredictable and complex competitive ecosystem.

Cover picture by Jean-Jacques Alcalay

Push you out of your comfort zone

alessio.defilippis@gmail.com (Alessio De Filippis) — Mon, 07 Sep 2020 06:19:19 GMT

Networking is proximity and diversity

All of us (people and organizations) are more confident in creating and curating relations based on proximity (e.g. within our business field or academic background).

Usually, we try to avoid to be exposed to new perspective, it’s perceived as a risk that has to be avoided. In other words, everyone is attracted by the comfort zone.

This behavior is reasonable but there is a hidden risk in terms of missing career and business opportunities.

But if your industry will have a downturn, having connections with other opportunity windows can help your career path or your company. Furthermore, some radical innovation in business and in the society are the outputs of contamination and cross-industry path of inspiration.

Sometimes you need to look at life from a different perspective.

Research suggests the companies with diverse boards experience better financial performance.

Harvard sociologist Robert Putnam highlights the importance of balancing both side of networking, bonding and bridging, proximity and diversity.

It’s not easy to carry out a networking strategy and mainly a networking strategy based on a balance between bonding and bridging, the mindset needed (for people and companies) is featured by curiosity and risk propensity.

It’s a cultural challenge. It’s a huge effort, it’s a long-term investment, but it’s worthwhile.

by Alessio De Filippis , Founder and Cheif Executive Officer @ Libentium .

Founder and Partner of Libentium , developing projects mainly focused on Marketing and Sales innovations for different type of organizations (Multinationals, SMEs, - Start-ups).

Cross-industry experience: Media, TLC, Oil & Gas, Leisure & Travel, Biotech, ICT.

Yuri Yegorov, Econo-physics: A Perspective of Matching Two Sciences

Dorie Clark from Harvard Business Review

Steven Johnson, Where Good Ideas Come From: The Natural History of Innovation

Partnership Libentium - Emintad

alessio.defilippis@gmail.com (Alessio De Filippis) — Sat, 05 Sep 2020 06:20:11 GMT

Strategic Finance for SMEs and startups

We are delighted and proud to announce a new partnership with Emintad llc ( https://www.emintad.com/ ) for developing new services for startups and SMEs in the field of strategic financial services. We will deliver together new services aiming to support the access to capital market in New York, Dubai and Hong Kong fot enterprises that want to implement funding projects and access to capital market, both OTC ( https://www.otcmarkets.com/ ) and other financial markets in Europe, North America, Middle East and Asia.