How a phenomenon-driven approach can help you build adjacency growth platforms
By Charlotte Vangsgaard & Martin Nyløkke Gronemann
Business history is littered with examples of companies that missed out on major changes in their industries and paid a hefty price. Kodak, despite having invented the core technology behind digital cameras, failed to digitize at a sufficient speed. Blockbuster hesitated to move away from its retail-focused strategy and was left in the shadow of Netflix and other companies with remote-distribution models. Nokia, once the world leader in mobile phones, missed out on the fast-growing smartphone market and is now trailing Apple and Samsung. Yet history also shines with examples of companies that understood the forces that change the global scene and were able to deliver something the world had never seen before.
Over the past decade, a discipline around how to organically grow your core business has developed. Applying qualitative methods to understand customers’ needs, using creative methods for ideation, and establishing a front-end innovation process are just a few of the essential components. However, sometimes companies find themselves in a situation where growing their core business no longer suffices. Examples of this are companies facing an eroding market (e.g. a producer of franking machines) and companies with ambitious growth strategies. Forced outside the comfort zone of their core business, they must ask themselves: What business are we in, and what business should we be in?
When it comes to building adjacency growth platforms outside your core business, there is less guidance to be found. From years of consulting multinational companies across industries and organization types, we know that there are many different contexts for innovation. Therefore, the aim of this article is not to provide a turnkey solution for building adjacency growth platforms. Rather, the aim is to point to some fundamental principles about how to identify and work with adjacency growth platforms. We define a growth platform by:
An identifiable pocket of growth—we are talking about a big potential market.
An unresolved customer/consumer aspiration or need—there is a gap between what people need and what companies currently offer.
An indication of commercial viability—there is money to be made.
A well-defined growth platform can have implications for determining which adjacent industries a company should expand into, the technologies a company will need to acquire or build, and the offerings a company should choose to develop.
Part 1: The gap—Companies talk about the importance of adjacency growth but find it difficult to act
We have observed how more and more executives make it a top priority to identify growth outside their core business. Global surveys support these observations:
In 2007, some 70 percent of corporate leaders said innovation was among their top three priorities for driving growth. Three years later, 84 percent of executives agreed that innovation is extremely or very important for growth.
Some 48 percent of business executives believe their primary growth challenge is to extend into areas beyond their core business.
Half of all CEOs based in developed markets find that emerging economies are more important to their company’s future than their traditional home markets are.
Fifty-seven percent of executives express the need for a more robust pipeline of big ideas.
However, despite their eagerness to expand into new business areas, we have also observed that many executives find it difficult to take the necessary actions to pursue new growth opportunities. This “action gap” between the intentions and actions of business executives is also supported by global data:
A recent study showed a clear gap between the importance CEOs ascribed to fourteen megatrends and the extent to which they had acted on them.
Seventy-nine percent of CEOs expect a high or very high level of complexity over the next five years, but only 49 percent feel prepared to deal with it.
Fiscal constraints, lack of available resources, and talent shortage are just some of the explanations we have heard CEOs offer over the years for this action gap. But in addition to these external factors, there are some fundamental barriers in the minds of business executives that prevent them from acting on their intentions.
Part 2: Why is it so hard to make good decisions?
We have identified one of the key factors that cause skilled business executives to make bad decisions when moving beyond their core businesses: the human reality. Social scientists and behavioral economists have proven that humans aren’t the rational, calculating machine that microeconomic theories assume. We have singled out five biases, which have been confirmed in our numerous interactions with business executives, that can make it difficult to build adjacency growth platforms.
The status quo bias
The tendency to like things to stay relatively the same
We have an inclination to leave things the way they are, for at least two reasons. The first is loss aversion, the tendency to be more concerned about the risk of loss than excited about the prospect of gain. The second is the sunk-cost fallacy, the inclination to continue investing in existing initiatives, even when the original economic case no longer holds, because so much has already been spent.
The bandwagon effect
The tendency to follow the actions or beliefs of others
We believe in and do things because many others believe in and do the same things. The only thing worse than making a huge strategic mistake is being the only person in the industry to make it. When there is widespread enthusiasm for an industry trend, it can be difficult to have the courage to ignore the herd and rely on one’s own hunch, information, or analysis.
The confirmation bias
The tendency to filter information in a way that confirms one’s preconceptions
As humans we tend to seek out opinions and facts that support our own beliefs and hypotheses, a tendency that manifests in two ways. The first is selective recall, the habit of remembering only those facts and experiences that reinforce our assumptions. The second is biased evaluation, the quick acceptance of evidence that supports preconceived hypotheses, while contradictory evidence is subjected to rigorous evaluation.
The anchoring effect
The tendency to rely too heavily on one piece of information when making decisions
We tend to rely too heavily on a few pieces of information when making decisions. Research shows that when you present people with a number and then ask them to make an estimate on something completely unrelated, they often anchor their estimate on that number.
The confidence bias
The tendency to overestimate one’s own performance
We are often overconfident. In general, people and businesses overestimate their own performance. Employees tend to overestimate their contribution, and as a result, the sum of all perceived contributions is often beyond the actual numbers. In addition, people seem overly confident in their ability to make precise estimates.
These five biases make it difficult for business executives to maintain a fact-based perspective when identifying and working with adjacency growth platforms. Bias among business leaders can lead to three major challenges:
Business executives underestimate the value of investing in adjacency growth platforms because they are better at identifying the risks of investing in these new growth platforms than seeing the risks of failing to change.
Business executives struggle to ignore the herd, leading them to pursue “me too” growth platforms that will not drive competitive differentiation.
Business executives find it hard to veer away their preconceived ideas, a few seductive data points, or the need to be overly precise.
How can companies overcome these biases when building adjacency growth platforms?
Part 3: Closing the gap
Five principles for a phenomenon-driven approach toward exploring adjacency growth platforms
Having conducted more than two hundred innovation engagements and having spent years advising multinational companies on innovation, we have defined five fundamental principles for business executives on how to address these biases when building adjacency growth platforms.
The five principles of a phenomenon-driven approach
Build dedicated ownership to look for adjacency growth.
Define a phenomenon to be the focus of analysis.
Follow an abductive problem-solving process.
Harvest data from a wide variety of complementary methods.
Make it concrete.
1. Build dedicated ownership to look for adjacency growth
A couple of years ago, we systematically studied the innovation practices of a range of multinational companies. The study had several interesting takeaways, two of which are worth mentioning here. First, companies that had a clear division of roles and responsibilities vis-à-vis innovation generally performed better than ones where several entities had more or less the same innovation mandate. The first category of companies assumed more ownership of the tasks at hand and were less entangled in internal power politics. Second, we found that companies that had delegated responsibility for working with adjacency growth platforms to a specific entity generally were better at developing breakthrough offerings. Unlike other organizational actors, they were asked, encouraged, and incentivized to challenge the status quo, asking not what the organization is capable of today but what it could be capable of becoming tomorrow.
2. Define a phenomenon to be the focus of analysis
Over the years, we have observed three pitfalls in the approaches that executives take when scouting for adjacency growth opportunities.
Placing too much emphasis on megatrends - A megatrend-driven approach can help you identify macroeconomic forces of development that most (if not all) corporations must adapt to. But an overemphasis on megatrends will make it very difficult to account for contextual specifics and next to impossible to extract non-generic growth platforms.
Placing too much emphasis on value pools and target groups - A value pool-driven approach can help you identify big pockets of growth. But an overemphasis on the size of those pools will distort your focus from understanding the needs and aspirations of people and leave you without much guidance as to what new offerings you should deliver.
Jumping too quickly into solution mode - A product-driven approach can help you remain concrete as you look for new areas of growth. But by jumping into solution mode too early in the process, you dramatically increase your risk of either developing offerings that are close to the existing ones—what we call “me too” offerings—or developing revolutionary offerings that don’t address a market demand.
To avoid the above-mentioned pitfalls, we suggest following a phenomenon-driven approach. Phenomenology focuses on everyday life—the intentional relationships between people and the meanings of the things they’re interested in and experiencing. How is the role of television in the household changing? What role does tea play in Chinese culture? Why are most diabetes patients not taking their medicine? Why don’t young people want to pay for media? In a nutshell, phenomenology is what makes us understand that we don’t just see a glass bottle with Coca-Cola written across it; what we see is a Coke bottle that evokes emotions and associations, a bottle that leaves us with thoughts of joy, befuddlement, nostalgia, pleasure, or disgust.
Working with a phenomenon-driven approach requires three fundamental building blocks:
A sophisticated understanding of what it means to be a human being
A special set of tools to understand human behavior
A reasoning process that allows you to discover new insights
The main challenge of a phenomenon-driven approach is that it demands more analysis before specific concepts and solutions can be developed and value pools can be sized. However, this is offset by the richer, more differentiating, and potentially more unique growth platforms that can be generated.
3. Follow an abductive problem-solving process
Hypothesis-driven problem solving has permeated the world of corporate business strategy. The benefits are obvious: it offers a fast, simple, and structured framework. But it has one major flaw that makes it inadequate in building adjacency growth platforms and other situations where there is a high degree of uncertainty: it cannot be used to develop original ideas. Given our confirmation bias, hypothesis-driven problem solving is confined to exploring conceivable hypotheses, and can only help you understand which of your preconceived ideas are most likely to solve the problem.
When identifying adjacency growth platforms, we suggest a problem-solving approach for original and nonlinear thinking, and abductive reasoning offers just that. Abductive reasoning is the “ability to face constructively the tension of opposing ideas and, instead of choosing one at the expense of the other, generate a creative resolution of the tension in the form of a new idea that contains elements of the opposing ideas but is superior to each.” Abduction is the process of actively seeking out data that might contradict preconceived ideas and existing assumptions, and then trying to make sense of it. The form of inference, therefore, is this: The surprising fact, C, is observed; but if A were true, C would be a matter of course. Hence, there is reason to suspect that A is true.
A successful abductive problem-solving process is characterized by data overload, continuous pattern recognition, and open and critical discussions carried out by a team of people with diverse problem-solving skills. Surprisingly, the process is more intense than it is lengthy. For example, when working with clients we typically spend no more than two to three months identifying the most attractive adjacency growth platforms.
4. Harvest data using a wide variety of complementary methods
Following the principles of abductive problem solving, it is necessary to collect data from a variety of sources to ensure that blind spots are uncovered. You need a perspective informed by a rich, nuanced understanding of people, which can only be gathered through deep qualitative research, broad validity of quantitative data, and the business pragmatism of rigorous financial analysis. While the most progressive companies apply all three of these elements, a surprisingly high number of companies still find it difficult to embrace qualitative methods with the same conviction and rigor that they apply when making NPV calculations or sizing a customer segment.
It is not sufficient to merely collect data from different sources. One should think carefully about when to collect that data. While there is no generic recipe, we suggest you to follow five steps when choosing which adjacency growth platforms to explore:
Define the phenomenon that should guide your analysis (e.g. for a client in the wine and spirits industry, it could be around “conviviality”; for a client in the appliance industry it could be around “the home”).
Conduct interviews and open-ended surveys within your organization to define the company-specific DNA that you can build your adjacency growth platforms on.
Identify the long-term drivers of change and define potential hot spots (e.g. socioeconomic data).
Look for emerging customer/consumer behavior around the phenomenon in each of the potential hot spots using qualitative data (e.g. interview experts in the industry, in adjacent industries, in academia, etc.).
Look for emerging signs of commercial viability to validate the business opportunities in each of the potential growth platforms (e.g. market-share development).
5. Make it concrete
The last principle addresses a fundamental challenge facing business executives working with adjacency growth platforms: now that I have defined which adjacency growth platforms to pursue (typically between five and eight), how do I create impact in the line organization?
After having identified a selection of adjacency growth platforms to explore, we suggest you to operate with a five-step process to deliver breakthrough offerings and sales impact:
Conduct ethnographic deep dives in selected markets to identify the unmet needs and aspirations that will drive new offerings within the growth platform.
Use the customer/consumer insights to guide ideation, and then select the best ideas.
Prototype the best ideas.
Test the prototypes in the market through business experiments.
Having assessed market impact, the new offerings should be handed over to the line, sold to an outside partner, or discarded if results do not meet expectations.
In addition to providing a structured framework, our experience is that operating with this gated approach also lends agility to the process—poor ideas can be killed early on, which leaves more room for the good ones to flourish.
Setting aside time and resources to explore adjacency growth platforms requires corporations to take a five- to ten-year view. Furthermore, making the move away from the comfort of certainty toward a more iterative and unpredictable problem-solving process does not resonate with all companies. However, based on more than two hundred innovation engagements, it is our firm conviction that the analytical hardship associated with a phenomenon-driven approach will be offset by more attractive adjacency growth platforms, which will provide a source of competitive differentiation that can be harvested for years to come. So it might be time for you to ask yourself: What business are we in?
 Kodak filed for bankruptcy in January 2012.
 Among these are well-known, innovative companies like Apple, Google, and Facebook.
 McKinsey & Co., “How companies approach innovation: A McKinsey Global Survey,” 2007.
 McKinsey & Co., “Innovation and commercialization, 2010: McKinsey Global Survey results,” 2010.
 In the McKinsey 2010 Global Survey, 48 percent of executives stated that their primary growth challenge was to grow through opportunities beyond the boundaries of their existing core business. Only 41 percent stated that their primary challenge was to grow in their existing core business. McKinsey & Co., “Innovation and commercialization, 2010: McKinsey Global Survey results,” 2010.
 In PwC’s “15th Annual Global CEO Survey 2012.”
 Fifty-seven percent of executives from around the world and in different industries agreed with the following: “We execute well on the few good ideas we have but need a more robust pipeline of big ideas.” McKinsey & Co., “Innovation and commercialization, 2010: McKinsey Global Survey results,” 2010.
 CEOs were asked to assess the importance of fourteen global trends and describe how they had taken action to address them. Across all fourteen trends, there was a gap between the impact executives assigned to these trends and the extent to which they had taken active steps to seize the opportunities. McKinsey & Co., “How companies approach innovation: A McKinsey Global Survey,” 2007.
 IBM, “2010 IBM Global CEO Study,” 2010.
 Kahneman, D. and Tversky, A., “Choices, Values, and Frames,” American Psychologist 39:4 (1984), 341–350.
 This isn’t to say that the status quo is always wrong but more that it is important for business executives to distinguish between a status quo option that is genuinely the right course and one that feels deceptively safe due to innate bias.
 Asch, S. E., “Opinions and Social Pressure,” Scientific American 193 (1955): 31–35.
 Warren Buffet addressed this when he said, “Failing conventionally is the route to go. As a group lemmings may have a rotten image but no individual lemming has ever received bad press.”
 The banking industry, like many others, has demonstrated the bandwagon effect by lending out too much money to the same kind of borrowers at the same time.
 Nickerson, S. and Raymond, S., “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises,” Review of General Psychology (Educational Publishing Foundation) 2:2 (1998): 175–220.
 Tversky, A. and Kahneman, D., “Judgment under Uncertainty: Heuristics and Biases,” Science 185 (1974): 1124–1130.
 For example, many retail fund managers advertise their funds on the basis of past performance. However, repeated studies have failed to show any statistical correlation between good past performance and good future performance. By citing good past performance, they increase the perceived likelihood of future good performance in the consumer’s mind.
 Alpert, M. and Raiffa, H., “A progress report on the training of probability assessors,” in Judgment under Uncertainty: Heuristics and Biases, ed. D. Kahneman, P. Slovic, and A. Tversky (Cambridge: Cambridge University Press, 1982), 294–305.
 Surveys have shown that up to 90 percent of people believe they that drive better than the average driver, and, similarly, most companies perceive their brands to be of above-average value. Svenson, O. “Are we all less risky and more skillful than our fellow drivers?” Acta Psychologica 47:2 (1981): 143–148.
 In Ahead of the Curve, Joseph H. Ellis argues that the problem with current forecasting models lies not in the data but in the lack of any clear framework for putting the data in context and reading it correctly. Ellis, J. H., Ahead of the Curve: A Commonsense Guide to Forecasting Business and Market Cycles (Cambridge: Harvard Business Review Press, 2005).
 The risk of spending substantial resources and entering a potentially large market with the wrong offering is real, and many companies have burned their fingers (and money) chasing what eventually turned out to be a phantom market. Having caught on to the 950 million potential consumers in India, Kellogg’s sought to expand its U.S. ready-to-eat-cereal market position in the early 1990s. With only a few local rivals, its ambition of a 2 percent market share seemed reasonable. However, Kellogg’s overlooked one crucial feature about the Indian market: Indians prefer a warm breakfast. By offering only products that go with cold milk, Kellogg’s found itself struggling in India. Sixteen years after entering the Indian market, their market share was less than 1 percent, generating only $70 million in annual revenues, compared with the $65 million it invested in the first year alone. (For the full story, go to: http://blogs.hbr.org/cs/2012/05/are_you_targeting_a_phantom_in.html.)
 Segway is one of the many examples of revolutionary offerings that haven’t seen the market adoption initially estimated. The expectation was to sell 10,000 per week, but Segway sold only 92 per week in its first five years (http://hbr.org/2011/04/why-most-product-launches-fail).
 Roger Martin’s definition of integrative thinking is similar to adductive reasoning. Martin, R., The Opposable Mind (Cambridge: Harvard Business Review, 2007).
 Peirce, C. S. , Harvard “Lectures on Pragmatism,” 1903.
 Examples of the type of data-collection methods we deploy when creating adjacency growth platforms are expert interviews (e.g. having a conversation with industry experts, members of academia, or experts from related industries), socioeconomic analysis (e.g. demographic changes, income distribution, consumption patterns), contextual research (e.g. databases, articles, reports, blogs, social-network scouting), bibliometric research (e.g. uncovering industry innovation patterns and discourse analysis), ethnographic deep dives, and synthesizing client-specific insights (e.g. conducting executive interviews, flash surveys, synthesizing strategic reports and market data).
 Focusing too much on qualitative data can make you lose sight of market sizes, and focusing too much on demographic changes will not provide a deep understanding of the needs and aspirations of your target group. Similarly, if you rely too much on external data you risk becoming too generic in your analysis, and if you rely too much on internal data it can be difficult to break free from established thinking.
 If adjacency growth platforms are integrated too soon in the line organization, there is a risk of new solutions that only marginally improve upon previous ones, and if it happens too late (or not at all) it will result in poor capitalization of the growth platforms.