For years big tech was one of the major employers of, and training grounds for, industry ethnographers focused on solving complex, longer-term strategic problems. But the wave of layoffs that hit white-collar workers beginning in 2022 prompted many in the research community to question their historical value to companies and their offerings to them in the future. Many participants in this discourse arrived at the same conclusion: Researchers need to do more strategic research—but what does that mean? And does the core of the issue lie with research, tech, strategy, or some combination of the three? After surveying shifts in the macro context that have upended the status of researchers within businesses, specifically large technology companies, we apply Roger Martin’s strategy playbook to develop a strategy for ourselves. We argue that to meet the moment, researchers should grow the strategic muscle of their companies by shifting their focus from how users interact with products to how their businesses interact with a wider world. By repositioning our offering to companies in this way, we argue that researchers can apply ethnography’s unique capabilities to the most pressing strategic questions facing businesses today.
Introduction
Over the last 20 years, many industry ethnographers have grown up and thrived in big tech companies. For the purposes of this paper, when we say “big tech” we mean Fortune 500 technology companies that have historically well employed and supported applied ethnographers—such as Intel, Microsoft, Meta, Amazon, and Alphabet1. Understanding how people use technology in context has proven essential for answering what product teams should build for people, as well as the why behind those offerings. And looking at broader contexts (e.g., among competing products, in different temporal or spatial situations, or in historical and future-facing contexts) has also helped applied ethnographers position their work as a key input for strategic product decisions. Good thing, too, since influencing such decisions is not only a core personal aspiration for many of us, it’s historically also been a core dimension structuring our performance evaluations and career trajectories. The more senior one’s level at many of these companies, the greater the expectation to drive broad strategy and inform “big bets.”
Over the past few years, however, something has changed, not only for applied ethnographers but across the broader research community. The halcyon days of large research teams dedicated to greenfield, generative research exploring foundational human phenomena have given way to a bleaker landscape littered with “open to work” announcements, the bruised remains of whole pathfinding teams, and reduced stakeholder appetites and financial budgets for exploratory research. From EPIC and LinkedIn to brief coffee chats at the office canteen, many within our community are asking questions like: Why and what do we do now? How can I protect my job and advance in my career? How can I afford to take risks in my research that are exciting and potentially high reward, without exposing myself to too much uncertainty? Many prominent voices from the user experience (UX) research community have aligned around some version of the same answer: Researchers of all stripes, including applied ethnographers, need to do better at connecting their insights and recommendations to business outcomes.
While such calls to action are valuable, we have noticed a gap in the discourse. Much of the conversation doesn’t adequately account for the changed business landscape of big tech (like higher interest rates, the maturation of many product spaces, and flat growth curves), or internal shifts (like the rise of data science as a powerful tool for product decision making), and consequent implications for where and how ethnographic research can and should play a role. Moreover, the effective meaning and practice of strategy in big tech has changed in response to such shifts. And within the tech research community, while we see many calls to do more “strategic” research, there seems to be little consensus on what we mean when we talk about strategy. If we as applied ethnographers want to drive even more strategic decisions in this landscape, we may need to update our own ways of thinking about strategy and our role in it.
What might help ethnographers who practice (or are aspiring to practice) strategic research is a clearer strategy for our own work, to make our approach more possible, more impactful, and more valued. We will outline one possible strategy, informed by a literature review and interviews with research practitioners, people leaders, and cross-functional stakeholders from data science and product management at some of the biggest tech companies today. This paper will thus:
- Map the current macroeconomic/sociocultural/political forces shaping big tech companies’ corporate strategies today,
- Situate the enactment of strategy in big tech companies, and
- Propose some recommendations for ethnographers looking to drive strategic decisions in this landscape, using a powerful yet under-appreciated (at least, within tech) strategic framework.
Section 1: What Is Going on in the Market Landscape?
We see at least four major shifts over the last roughly ten years that are shaping the role of research within technology companies. The first is that many of the big tech companies have evolved from startups to incumbents, and the overall tech product landscape has become more mature. More recently, we have also been experiencing relatively high interest rates and persistently tense macroeconomic market conditions. As a result, companies are experiencing increased pressure from shareholders to demonstrate strong bottom line results. At the same time, data science has matured into a powerful insights function that has become quite efficient at answering many important product questions. This section explores how such shifts have impacted how these companies’ approach strategy.
From Startups to Incumbents
Twenty years ago, many of today’s big tech companies were effectively startups that lacked financial repercussions for taking risks. Their evolution into mature companies has triggered many downstream implications for strategy and research. As startups, the business’s strategy involved coming up with a new product, market or business model idea. Once identified, the company would build a minimally viable version, then track whether enough people would use it to justify continued investment. In this nascent business landscape, the approach was a bit like throwing spaghetti at a wall to see what stuck, followed by rapid scaling if it did (Kushner 2011). The underlying logic of product strategy was one of innovation more so than monetization.
Through our interviews, we heard that this cultural emphasis on innovation persisted even as tech companies were becoming well established through the 2010s, a period that coincided with many of them building large research organizations. This combination created a relatively lush environment for many ethnographic researchers: A persistent cultural focus on innovation, supported by growing opportunities for research teams to do foundational, ethnographic work to inform those innovation bets. But as these companies have matured and become beholden to shareholders, there is even more at stake. Tiny changes to the number of rows of pixels on the most efficiently monetized surfaces can directly and nearly immediately result in billion dollar revenue impacts (McGee 2016). As a result, for many core product offerings from mature tech companies today, the underlying logic of business strategy has become more about optimization than innovation.
At the same time, the overall tech product landscape has become more mature and competitive. The first 20 years of the 21st Century were a period of massive growth in the number of product offerings launched (Palandrani 2020). The number of venture capital firms tripled in the years following the 2008 financial crisis. In 2006, only 3,500 startups were funded with $31 billion USD, but by 2021 those numbers ballooned to over 30,000 companies funded with $669 billion USD, an increase over 2,000% (Teare 2022). While the first iPhone was released in 2007, by 2016 there were more smartphones in use than people on the planet (Richter 2023). Watching this and other lines shoot up at such a rate, the financial incentive structures rewarded big risk taking, and in this context, one of the key strategic goals was being first to market with new big ideas. Investors and technologists alike saw reasons to believe that their bets were likely to pay off.
However, as the landscape has become more mature and crowded, there have been fewer true white spaces. Even recent breakthrough technologies such as generative artificial intelligence (GenAI) and mixed reality headsets are sometimes greeted with skepticism. As a result, the stance and potential opportunity for strategic research is now less about building brand new technology to capture open opportunities, and more about defending against competitors.
High Interest Rates and Reduced Cash for Investments
Another major recent shift has been the increased cost of money (Sunderji 2022). Interest rates were at historic lows in the 2010s before creeping up a bit right before 2020, when they then dropped dramatically as COVID threatened to upend the economy. Historically low interest rates were a crucial enabler for major product investments that big tech companies were making. They made it much easier to invest in major new product groups, hiring massive cross-functional teams to support these investments. As interest rates have shot up since 2022, it has become more costly to spin up new offerings, which has in turn impacted the calculus of which product bets are worth making (Walker and Dowd 2024).
Financial Pressures of Shareholders
A separate (but related) shift has been the increased pressure that Wall Street is putting on big tech companies to demonstrate financial returns (Picchi 2022). For years, investors were willing to invest in the compelling visions of tech companies, betting on profits to be realized down the road. In this context, a key product strategy question was “What should we build that users really need and will create strong user growth for us, regardless of whether it actually makes us money?”
The overall tenor of the investors, however, has changed markedly. As one Meta shareholder wrote, “Such a franchise can print money and shareholders want that money returned to them, not frittered away on head count and blue-sky bets” (Ip 2023). The new imperative for many technology companies is less about finding new product opportunities to grow user bases, and more about what will make the company money today (Forbes 2022). Businesses have responded, in turn, by looking for safer growth opportunities, for example, eking more out of core products rather than building up untested ones.
The Rise of Data Science
A recent report from Stripe Partners (2023) emphasized the rise in tech companies of high-volume, scalable A/B experiments and the powerful data science analyses that can translate them into directional insights on how to move key user metrics. Running thousands of these experiments at once is an efficient way to derive actionable learnings that can directly impact numbers that the business cares about. This observation is an incredibly important one for making sense of our discipline’s current state and potential future; it was also echoed strongly among many of the product managers and data science leaders who we interviewed. Data science answers many of the what questions with authority and speed that researchers cannot match. Of course, our interviewees point out that there are limitations to what data science can and cannot answer through even the most advanced experiment designs. For example, only having access to the data of existing users (not potential ones), and not always having the right technical infrastructure to capture and analyze impactful data. But especially for mature products in mature technology companies, which prioritize efficiency and optimization, it is data science—not research—that increasingly powers design changes and corresponding financial returns.
Such macro shifts in the overall business landscape have had substantial impacts on the way big tech companies operate, how they evaluate strategic bets, and the broader cultural ethos that drives day-to-day decision making. They have also changed the role and opportunity of strategic ethnographic research. Back in the “golden days,” deep ethnographic research on foundational human experiences (think “belonging” or “wayfinding”) was actually a relatively efficient way to identify new opportunities to build innovative and exciting new technologies. However, with less white space in the market overall, increased focus on risk mitigation and optimization rather than breakthrough innovation, increased financial pressures, and adjacent insights functions that are highly efficient at answering core product questions, strategic ethnographic research may need a new strategy.
Section 2: How Has Our Discipline Responded?
In the current labor market, many research practitioners have been asking hard questions about how we got here and where we might be going. Much of the discourse can be categorized into arguments that call out cross-functional stakeholders or leaders for not recognizing our discipline’s inherent value (see, for example, Spool 2023), or arguments that look inward to diagnose what “went wrong” for us researchers (Antin 2023).
Among many of the voices in the latter camp, we see a common call to action: To prove our value to business, we need to focus more on “strategic” work. What strikes us, however, is that many of the arguments pushing for research to be more strategic lack a clear perspective on what we mean when we talk about strategy. This is an issue that also came through strongly in our interviews with researchers and cross-functional stakeholders: We found substantial variation in what people mean when they say strategic, and we believe that this may be making it harder for us to actually drive more strategic impact.
Some of the common slippages we see in the discourse are a conflation with “future-facing,” which we believe is problematic because a lot of important strategic decisions are very focused on the here and now. Another is a conflation with “complex” or “broad in scope,” which we believe is problematic because there are plenty of research projects that tackle complex issues but are actually quite tactical. Another is a conflation with “impactful,” which we believe is problematic because impact often just means something happens because of your work—a new feature launched or sunsetted, or a product change moved key metrics. Another very common one we hear is “actionable,” which we believe is problematic because there is plenty of valuable strategic input that is actually not actionable, and a lot of tactical input that is highly actionable. Such other adjectives floating around in the discourse may or may not have anything to do with whether the outputs of said research inform actually strategic decisions for the business.
This semantic confusion is problematic for a variety of reasons. For one, “strategy” shows up all over researcher performance guidelines and evaluations. So not having a shared understanding and vocabulary for what strategy means has real consequences for individual career trajectories. But more important is that without a common understanding of what strategy means and how we should be more strategic within our organizations, we likely will collectively struggle to drive the kind of impact that we aspire to.
Confusion about what we mean when we talk about strategy is not unique to the technology world. A simple Google search will produce dozens of different definitions and interpretations of strategy. What strikes us, however, is that many in the tech world have relatively little familiarity with the long history of thinking, theorizing, and framework building related to strategy. Before we outline our working definition of strategy and its implications for ethnographic researchers, let us briefly situate it in a bit of historical context.
Section 3: What Is Strategy?
So what is strategy—and what is it not? Any aspirations for the future of strategic research depend on a clear take on what strategy means, how to develop it, and how to do it. As a formal discipline, strategy first emerged in the 1970s, when Bruce Henderson (1979) at the Boston Consulting Group championed efficiency as a goal for businesses, enabling them to outcompete rivals through pricing models and other techniques. By winning at efficiency (the thinking went), these businesses would grow in scale, compounding their advantages relative to their competitors, and accelerating a positive feedback loop.
But Michael Porter’s (1980) theory of competitive advantage soon expanded the lens of what strategy entails. He began to seriously think about different approaches to obtaining competitive advantage beyond prices and efficiency plays, including differentiating from their competitors through unique value propositions and brand positions. By the 1990s and into the 2000s, much of the conversation about strategy in business circles had substantially expanded to include discussions about unmet customer needs and customer satisfaction. Such topics are now core content in the broader business world, from business school training programs to the Harvard Business Review.
Within the landscape of strategic frameworks, we believe a particularly useful one for applied ethnographers is one popularized by the strategist Roger Martin2. In his and collaborator Alan Lafley’s book Playing to Win, strategy is described as an integrated set of decisions collectively executed to create sustainable competitive advantage over rivals (Lafley and Martin 2013). In human terms, a strategy is a bundle of bets about a business, a customer, and the landscape in which they interact. These are bets at the juncture of capital allocation, product features, customer segmentation, go-to-market channels, timing, brand positioning, and organizational structure.
Their framework outlines an integrated cascade of five types of decisions that collectively yield a comprehensive strategy:
- Set a Winning Aspiration, a clarifying purpose that all following decisions will be measured against.
- Make a decision about Where to Play, an intentional commitment to your core offerings, in which selected market categories, which geographies, and for which audiences.
- The third step, How to Win, is about how to execute in this space in order to realize your Winning Aspiration.
- Now with a plan to win, the fourth step asks What Capabilities Must You Have?, what skills and tools must be in place to realize a winning strategy.
- The last step asks What Management Systems Are Required? In other words, organizational structure is needed in order to manifest your plan?
All five pieces have to harmonize. The strategy is tuned into a comprehensive focus by looking up and back down the cascade again, iterating and fine tuning as decisions evolve. Crucially, Martin distinguishes this way of thinking about strategy from a “plan” or a goal (e.g., moving X metric Y percentage points). From our interviews with tech workers, we heard that a lot of product leaders conflate strategy with plans. According to Martin:
“Crucially, [a] plan does not specify how all these initiatives will link together to accomplish a given outcome. The essence of strategy is to compel the thing you do not control to do what you need it to do. If you think about a company, what does it control? It controls how many people it hires, how many capital dollars it puts in place, where it sells its products—all of those things are in its control. What is completely outside its control? Customers. What strategy is, is your way of compelling those people you do not control, the customers, to feel that buying your product or service is the best thing for them to do.” (Martin, Storgaard, and Lau 2023, p. 3)
Now, the framework proposed in Playing to Win has its limitations. For example, we believe there is an opportunity to expand this framework to more explicitly consider how to build strategies that win given the complexities of modern business environments. New factors worth considering include network effects, social acceptability of new technologies, collective and group needs—not just individual user or customer needs. However, we believe this framework is one of the most helpful for applied ethnographers in technology to consider, for three key reasons:
- It frames strategy as a practice of decisions, rather than a set of plans or goal metrics, which tend to dominate product strategy discussions in many tech contexts.
- It conceptualizes these decisions as integrated, rather than siloed and without implications for other decisions that the business needs to make.
- It conceptualizes strategy as inherently contextual. Customers and their needs are considered within the broader landscape of the market, other products, and other companies with other positions.
Importantly, Lafley and Martin emphasize that this framework can be exercised by anyone in any context—from a CEO crafting a generational strategy to a salesperson on the shop floor—who thinks about which customers to target, where to position themselves, and which pitch to lead with. We can use it as well, and will apply this strategy framework to ethnographic researchers in tech in Section 5, treating ourselves as a “company in need of a strong strategy.”
We believe this classic strategy framework can be particularly helpful for driving impact in tech companies precisely because many product teams within these companies don’t yet have the muscle and intuition for it. After decades of strategizing through low-cost bets and scaling up whatever sticks into high-revenue returns, they are pivoting on the spot. It’s not just researchers who don’t have a strong strategic muscle, it’s also plenty of leaders and cross-functional stakeholders. This, we believe, represents an opportunity for applied ethnographers to help tech organizations actually think strategically and do strategy differently.
Section 4: The Challenges to Success
Our interviews with dozens of research and cross-functional tech workers surfaced two key categories of challenges for ethnographic researchers aspiring to influence strategic decisions. The first centers on the organization: How it is structured and how decisions get made. The second is about the researcher’s orientation toward users and user data, rather than an orientation toward the business and commercial data. We acknowledge these challenges because they represent real constraints on the ability of researchers to drive strategic impact. But with some awareness, these factors can be worked around.
Organizational Barriers
For many of the people we interviewed, a core challenge to strategic research is cemented in org charts and organizational cultures where research functions operate in service to product design, which in turn, works in service to product management. This type of structure often results in a culture where research is looked upon to validate existing hypotheses that product managers have and have been working on with their design counterparts. A related observation we heard from respondents was that for many engineering-led product cultures (which are common in tech), problem solving is highly hypothesis driven. Getting engineering buy-in on projects can often require having a very clear hypothesis about what the problem even is in the first place, and offering testable solutions. This kind of organizational structure and culture can result in less appetite for research that may reject existing hypotheses, let alone answer more existential questions about what a product’s winning aspiration or positioning should be.
Interestingly, we heard in interviews that some of the most “strategic” research is actually being done outside of product teams, in adjacent insights teams like product strategy or product marketing. These teams often have direct lines of communication with senior leadership, rather than having to go first through design and product management. Some tech companies have even built foresight practices into core business orgs like finance (e.g., at Amazon) to ensure that they have direct line of sight to senior business leaders. While many tech companies champion the flatness of their org charts and their bottoms-up approach to decision-making, this does not always reflect reality. Truly strategic decisions tend to be made very high up on the ladder before they are passed down to different teams to execute. In the best cases, it still takes an incredible amount of patience and sometimes a little luck for research insights to work their way into strategic decision-making.
An Orientation toward Humans before Business
A second core challenge we found in our interviews is that some researchers spend the majority of their energy focusing on user data, and much less time on the commercial data that senior leadership evaluates to inform strategic decisions. Such data may describe which products are generating money and which have not yet demonstrated ROI, potential costs and benefits of a restructuring, where competitors are investing and how much, and so on.
In some cases, we heard that researchers are even intentionally shielded from this kind of commercial data. This impedes researchers at all phases of project work. At the start, it limits researchers from coming to a fresh understanding of the business’s needs and opportunities through an ethnographic read of business data. Later on, it prevents researchers from making recommendations intelligible to their colleagues who currently make strategic business decisions.
Lastly, many ethnographic researchers view themselves as advocates for users first, and in service to the business second. This philosophy is noble. However, across our interview participants, we heard a strong recommendation to shift this mindset—particularly among in-house researchers—and view ourselves to be in service to the business as well.
Section 5: A Strategy for Strategic Ethnographic Research
Below we outline a potential strategy for ethnographic researchers who aspire to do great strategic research, using the Playing to Win framework to structure our observations. Now, it should be noted that we use the strategic framework loosely, and these observations should be read as “tips and tricks” and considerations rather than strict “dos and don’ts.”
One overarching takeaway that helps set the scene is a conceptual one. Strategy can be usefully thought of as a verb, not just an adjective (as in “strategic”) or a noun (as in “this product’s strategy is XYZ.”) We believe this conceptual shift can be helpful because it reframes strategy as a practice of decision-making for researchers and the organizations they’re a part of, less so about the type of research projects we take on.
1. What Should Our Winning Aspiration Be?
The highest-level decision in the Playing to Win framework is: “What should we set as our vision for ourselves?” So for us, the question is: “What should ethnography’s winning aspiration be within big tech?” Based on our interviews, we have discovered that many tech leaders across the functional spectrum do not have particularly well-established habits when it comes to thinking strategically and practicing strategy. As a result, we suggest that a winning aspiration for ethnographic researchers in tech could be to grow our teams’ strategy muscles.
As applied ethnographers, we have been trained to think holistically and in an integrated way, paying attention to broader contexts. This positions us very well to grow the strategy muscle within our product teams. This aspiration also applies to researchers regardless of career level. Early career researchers can direct attention around strategic concerns by raising them to their cross-functional stakeholders (and put them on the agenda), as more senior researchers can facilitate conversations with leadership from the right angles. People managers, too, have a role; they can lead by example in creating a team culture of immersion in commercial data, and kicking down doors that can inspire researchers, their stakeholders, and the broader organization.
2. Where Should We Play?
The next bucket of decisions in the Playing to Win framework is: “Where should we play??” Several compelling answers to this decision surfaced in our interviews with researchers and cross-functional stakeholders that can be divided into two categories depending on what kind of product team you support:
- Where to play if you are in a position to choose your team. For example, if you are on the agency or freelancer side, are in a flexible role in-house where you can work with multiple product teams, or looking for your next position.
- Where to play if you sit in a mature product organization. For example, “cash cows” that may be legacy products and/or highly monetized, so that product tradeoffs have immediate and direct financial implications for the company.
As we outline below, where you sit influences what strategy practically means in your context. Interestingly, we observed that many researchers don’t tend to reflect much on what kind of product organization they are supporting, and therefore, what strategy means in their context and the role ethnography can plan in that context. A common thread, however, regardless of where you sit, is to focus on “territories” where making a mistake is costly to the business.
Where to Play if You Are in a Position to Choose Your Team
If you are in a position to choose what kind of team you support, we heard that some of the most impactful “territories” to play in are hardware, breakthrough technologies, and policy.
Focus on hardware. Some respondents emphasized that hardware technologies are generally more contextual than pure software. This means that there are more questions about where to play and how to win that are dependent on the broader physical and social context of use. Similarly, because hardware often has a longer product lifecycle and requires financial expenditures for manufacturing, it is comparatively costly to pivot if the business has made a wrong bet. These factors make hardware a prime territory to play in for applied ethnographers looking to drive strategic impact.
Focus on breakthrough technologies. Other respondents pointed to the opportunity to play in spaces that are focused on very nascent breakthrough technologies. Roles supporting nascent products, like GenAI, are rich territories for applied ethnographers for many reasons. For example, in these product spaces there tends to be less behavioral data available, which means there may be more need to understand why people are doing what they’re doing with the technology.
Similarly, our interviews indicated that these product teams tend to have less intuition and pre-baked hypotheses about what will win in the market, increasing the appetite for ethnographic research to explore what users need. Finally, senior leadership often has less of a sense for what these technologies can concretely do for the business, increasing the need for a truly strategic perspective that connects dots among the market landscape, user needs, and the core business.
Go into policy orgs. Other respondents explained that they found more success driving strategic decision making by moving out of product research organizations entirely and into more policy-oriented teams. The reason being that in many big tech companies, strategic questions related to the risks and rewards of corporate positioning, brand perception, as well as unique value propositions tend to be driven more by policy and communications organizations than product groups. This creates more appetite for researchers trained to understand how the broader context impacts people’s perceptions and behaviors.
Where To Play If You Are in a Mature Product Organization
If you are in a mature, “cash cow” product organization, we recommend focusing on risks to the business.
Focus core offerings on “risks,” not just innovation opportunities. One successful approach to deliver more strategic work has been to reposition the value of research’s core offering by highlighting risks to the business, not just opportunities for product growth. This is a space where our insights have unique power. Applied ethnographers have a window into people’s lives that is broader than the moment when a user has their thumb on the screen or their nose in their laptop. They can see risks such as broader cultural trends or burgeoning alternatives coming that others may not.
3. How Will We Win?
The next bucket of decisions in the Playing to Win framework is: “How should we win as we play in those spaces?” Two core recommendations surfaced through the interviews that can help us better see how to win: Integrating many types of data and applying an ethnographic lens toward our own organization.
Integrate Many Types of Data to Connect the Dots
Two key factors emerged from our interviews that emphasize the importance of triangulating data to influence decision makers:
- Senior leaders making decisions on strategy are inundated with insights from many different disciplines at the same time, on the same topics. From data science and product management to marketing and research, many different functions are charged with influencing leadership decisions.
- To have confidence in strategic decisions like where to play and how to win, senior leaders need to sense how large an opportunity is and the potential tradeoffs of investing in one area over another.
Because of these two factors, the most senior ethnographic researchers we interviewed highlighted the importance of synthesizing data and insights across functions. To the extent possible, this analysis should include financial and commercial data to ensure that the message leadership hears about key strategic decisions is unified and clearly commercially relevant.
Turn Your Ethnographic Sensibilities inward, toward the Organization
Many of the most senior ethnographic researchers we interviewed highlighted the value of intensely observing the internal dynamics, power structures, financial performance, and communication channels. Observing organizational rituals like quarterly earnings calls can help researchers position their insights and recommendations in ways that are grounded in the open strategic concerns of the business.
4. What capabilities must be in place?
The next bucket of decisions in the Playing to Win framework is: “What capabilities must be in place to deliver on our winning aspiration, the decisions on where to play, and the decisions on how to win in those spaces?” Our interviews indicate that key capabilities required to drive strategic decision making include commercial acumen, the ability to interpret user engagement metrics, and, of course,core applied ethnographic research skills.
Commercial Acumen
To influence strategic decisions in big tech organizations, many applied ethnographers have an opportunity to get better at understanding and interpreting commercial metrics. This involves understanding financial metrics like annual recurring revenue (and revenue growth) and key costs (e.g., for engineering headcount, content moderators, etc.), as well as market metrics like venture capital investments in a given product space.
Understanding of User Engagement Metrics
For many product organizations, user engagement data is one of the most critical inputs for strategic decision making. Being fluent and up-to-date with basic engagement metrics like monthly active users (MAU), daily active users (DAU), and more product-specific engagement metrics is critical for being seen as credible partners to product leaders making decisions on where to play and how to win. And while some in our community are already expert at interpreting and weaving in log data, as a collective we seem to have an opportunity to get even better at interpreting log data (Anderson et al 2009).
Ethnographic Sensibility
Many researchers, and especially applied ethnographers, have built their insights through excellence at interpreting different types of data, from the provocative space between what we see people do and what we hear people say. Our value has never come from the execution of a chosen research method but rather from the way that we see our data and apply it to problems.
More than any of our cross-functional stakeholders, we tend to bring an ethnographic sensibility that turns ambiguities from one perspective into a question that can be filled in from a different perspective. We need to expand our use of this capability as we fold new types of data and points of view into our synthesis of what matters to people and what matters to businesses. We can use this capability to see things that nobody else could or would.
Section 6: The Promise of Strategic Ethnographic Research
As business strategies evolve and the circumstances of research within companies mutate as a result, ethnographers are uniquely positioned to both ride and propel these waves. Since its emergence as a professional discipline, research has articulated its own specific value proposition to companies through reference to its proximity to users. When companies wanted to strategize more empathetically by thinking like their customers, we were there. We used our research skills to understand people’s pains and preferences and then amplified their voices throughout the product development process. We have drawn from ethnography’s openness to push a point of view—the user’s point of view—that, at best, challenged conventional wisdom in just the right way.
But the business of technology has shifted, data science has progressed, and the meaning of strategy has changed within these companies. It is time for ethnography to expand beyond a value proposition that centers empathetic understanding of users toward one that draws from more of our strengths, one that combines our research skills with problem-solving sensibilities that ethnography encourages. Specifically, we see an opportunity for researchers to expand their focus from understanding how users interact with their products to understanding how businesses interact with users and the broader market.
Our interviews have convinced us that researchers within the EPIC community are actually in an excellent position to drive this change. This is because ethnography combines both a research method with an interpretive sensibility. Through it, we tell stories that are unexpected but true. These stories open up new questions, point to new opportunities, and identify new risks. We think inside and outside of different contexts, from the lives of our interlocutors to the initiatives of our companies, seeing each from a sideways angle. In doing so, we remain open to surprises that break open a single point of view into something that nobody else would have recognized. We also present a perspective that can keep leadership awake even as the inertia of their maturity seems to hum their imagination to sleep. Ethnography’s unique technique of making the familiar strange and the strange familiar is the one that meets this new moment.
About the Authors
Morgan Ramsey Elliotis a partner at ReD Associates, and is based out of New York City. He also spent three years working as a senior researcher and research manager at Meta.
Charlie Lottermanis a consultant at ReD Associates.
Cameron Wuis a consultant at ReD Associates.
Notes
We are grateful for the members of our community who shared their experiences with us and helped us sharpen our thinking as we prepared this text. We are also grateful to our EPIC curators for their generous time and thoughtful feedback.
1. Other companies like Apple, Tesla, Nvidia, and other large-cap tech companies are certainly big but have not historically been home to large cohorts of applied ethnographers. At the same time, we believe that the argument in this paper—that researchers need to apply a strategic approach themselves to building the strategy muscle of their companies—is one that has broad relevance to any researcher working in any company, from a big tech firm or a start up to an automobile company or any other type of business where research happens.
2. Disclosure: ReD is collaborating with Roger Martin on building new perspectives on the future of business strategy.
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