Sample Chapter: Stop Wasting Research

This is a sample chapter from Jake Burghardt‘s book Stop Wasting Research: Maximize the Product Impact of Your Organization’s Customer Insights. 2025, Rosenfeld Media.

Chapter 1

What is Research Waste?

If you listened to what people say, you’d think customer insights were central to how things get done. When I talk to folks about how insights are influencing product development and delivery at their organizations, they tend to paint a rosy picture. After all, they can point to decisions that were made with customers in mind. It’s a commonly held belief that all sorts of staff are supposed to work from in-depth data about customers, so people want to say they’re living up to that ideal. Specialized research staff often feel like more research is needed, but generally speaking, their off-the-cuff retrospectives tilt toward optimism.

As I’ve jumped into different organizations to advance how research gets integrated into product planning, I’ve certainly seen amazing successes that are worth celebrating. Researchers in all sorts of disciplines are racking up impacts on what’s being offered to their customers. Many of those researchers’ careers are advancing nicely. And new research job postings keep coming online (though their pace does ebb and flow). Overall, some things are clearly going right.

But when you are taking a closer look at the “rosy picture,” it usually doesn’t take long to find some major gaps in the status quo. When you talk to leaders about how they use insights to inform their ideas—not just validate them—it often feels like you have to really search to find the “R” in their “R&D.” Traceability between research insights and resulting actions can be very low. Off the record, many full-time researchers have told me about their struggles getting product planning responses for their most important insights. They count their wins, but they see real losses.

Research as Momentary and Optional Input

Across the industry, expectations of what customer research pro-vides—and what will routinely be done with insights—are often set very low. The status quo of how research gets applied to product planning leaves too much value on the table, both for the customer and the business.

Best-in-class study methods and practices may result in truly useful learning, but what’s actually happening with those insights? All sorts of researchers may have impactful share-out presentations that inspire their partners, but “eureka” moments aren’t enough. Insights have to continue moving forward into plans for next steps—or as inputs into appropriate backlogs, to be prioritized at a later date. Some commitments may be made on the spot. But what about all of the insights that didn’t move the needle during the timeframe of a study?

When I’ve talked to clients and partners about digging into past research in their organizations, they’ve often discouraged me. They’ve told me that past learning is “no longer relevant” and“doesn’t represent where we’re going.” Invariably, I’ve ended up finding reams of crucial unapplied insights that could have been driving the exact wins that leadership was looking for. These insights simply got lost in the shuffle, but they could be dusted off and brought back.

Just because decision-makers were exposed to research doesn’t mean that it will inform their work. I’ve sat in many meetings where a leader used a blank board to start a discussion about new possibilities. They may have been directly involved in research studies—but research-based problems to solve were nowhere to be seen. Their resulting lists of ideas often had limited connection to real, research-identified customer needs. Without researchers there to inject what they had learned, existing customer insights didn’t shape the conversation. And even when researchers happen to be invited to planning sessions, it can be difficult for them to prepare digestible inputs in real time. Recent learning can hold an outsized place in what’s easy to recall.

In today’s status quo, customer insights aren’t treated like the business assets that they are. Insights are too often seen as a momentary, optional input—when they could be ongoing drivers of more effective planning. It’s worth asking yourself: What would a competitor give to know what your organization is effectively ignoring? What would they give to know all of those unsolved customer problems?

Preventable lapses in insight usage can send products spiraling in the wrong direction. So many failed launches result from decision-makers who didn’t have a sense of what would be meaningful or valuable in the real world. This disconnect can mean life or death for new entries into the marketplace and can drastically influence the continued success of existing offerings.

A New Concept to Shift Perspectives

The status quo of research usage needs to evolve. You can motivate changes by creating some dissatisfaction with the current state. To this end, the concept of “research waste” can create a headspace for the problem of underutilized insights. To quote the artist Brian Eno: “Giving something a name can be just the same as inventing it.”

Note: Defining “Research Waste”
Research waste is valuable customer insight that was unseen, ignored, or unintentionally left out of planning. Research waste is absent from backlogs, roadmaps, goals, designs, specifications, or other “next step” deliverables from relevant “owning” teams. When research is wasted, there’s less advocacy for what really matters to the people an organization is striving to serve. Solutions that have not been implemented to address critical customer insights can represent opportunity costs for core business measures.

Research is wasted when:

• Insights fail to cross silos and never get communicated to the “right” potential owning teams (product, marketing, engineering, growth, sales, service, customer support, and other functions).
• Unaddressed insights do not have an established location where they can be predictably found and reconsidered later.
• Accumulated research evidence for important insights is scattered, failing to make the best case for planning.
• Potential owning teams are unwilling to review insights that clearly fall within their charter and scope.
• Even when owning teams have considered an insight, they do not respond in trackable ways—which can include declining to pursue an insight in their plans.
• Owning teams do not remain aware of “their” open insights over time.

As research becomes waste, it becomes the “unknown known” (see Figure 1.1). It’s all those unacknowledged and overflowing virtual folders filled with game-changing insights. It stems from a lack of accountability for identified customer needs. Wasted research is often nowhere to be seen at the time when decision-makers take on their most important planning decisions.

Research waste does not include effort that might be wasted as part of conducting studies. Skilled researchers often want to connect the concept of research waste to studies that asked the wrong questions, used the wrong methods, did not follow appropriate governance, left data unanalyzed, jumped to the wrong conclusions, didn’t result in useful insights, or other issues. There are many ways to conduct low-ROI, low-validity, and simply bad research. While these outcomes are clearly worthy of improvement, they fall outside of this book’s scope. As mentioned in the opening FAQs, the emphasis here is not on conducting new research. Instead, this volume is full of ideas on the challenge of moving forward from the discovery of a valuable customer insight into its use in product planning.

Figure 1.1

The pathways of immediate research use, research waste, and research reactivation. 

Note: When Leaders Say “No” to Critical Insights
Insights don’t have to disappear when teams decline to act. It’s common for potential owners to acknowledge an insight but say that it doesn’t fit into their current goals or roadmap. Also, some teams may not be accustomed to basing their plans on customer research (despite their claims of working from this kind of data).

After an important insight has been declined, it can stick around as long as it continues to be applicable. Tracking the reasons why teams turn insights down can become part of improving your organization’s learning processes. Insights that were not pursued on one occasion can become invaluable when the focus of planning shifts. Alternatively, if an insight warrants more immediate action, your researchers and their advocates may consider escalating to additional levels of leadership.

Acknowledging research waste is not about “promising the world.” Acting on every single insight—or even some fixed ratio of insights—isn’t the right goal. This isn’t about proposing a researcher takeover of product development and delivery, ignoring realistic limitations and constraints. It’s not a “wouldn’t it be great” fantasy. Instead, it’s the recognition that too much of the top tier of customer research is out of sight, out of mind, and yet waiting to be put to use.

Definitions to Clarify What’s Being Wasted

To better understand research waste in your own organization, it’s useful to establish some base definitions for key terms. To make progress, you’ll need to nail down some ubiquitous but ambiguous language. The first of these terms is customer research. This definition sets a boundary that some will find expansive—bigger than how they currently think about the term—while others may feel the sting of what it excludes (at least until certain researchers explore raising the bar on their practices).

Note: Defining “Customer Research”
Customer research is an inclusive term that covers a variety of customer-focused investigative practices in organizations (UX, market, CX, research science, data science, strategists, customer success or sales analysts, business intelligence, people who do research, and more). To be considered research, it must have a rigorous planning phase that bridges learning goals, research questions, and research methods (including data collection, analysis, and insight activation protocols). It must follow internal standards and conventions and generate some sort of documented study plan and reporting output.

You Might Be Asking

Isn’t some “old” research out-of-date?

Just because an insight is “old” doesn’t automatically invalidate it. There’s a common belief in tech organizations that anything that’s not at the front of their information “ticker tapes” will have little to say about the future. Contrary to this cultural bias for newness, a surprising majority of unused customer insights can still be relevant—either in their current form or through some triangulation with more recent evidence.

For one thing, products don’t usually evolve in ways that negate foundational customer understanding. Many market changes are relatively slow, and even big punctuations don’t negate all prior learning. Looking at specific customer experiences, even insights about smaller needs for improvements can remain relevant for long periods of time. Zooming out to overarching customer needs, many learnings are hyper durable. For example, for an educational product, insights about the core needs of students are slow to change, even as a landscape of educational products evolves to meet those needs.

That’s not to say that there aren’t many cases where it’s crystal clear that specific research is no longer relevant to current challenges. Some research expires. But let’s not throw out the “you could make people love your product if you tackled this existing insight” babies with the “yes, it’s true that this particular insight is out-of-date” bath water. Discounting gold-standard research because it’s not “hot off the presses” is a common path to wasted research.

Within the boundary of customer research, customer insights are specific assets that can be applied or wasted. Customer insights are the headlines that can spark advancement or pass like ignored items in a feed. In general, the term insight is used in so many different ways that it’s well on its way to becoming meaningless. By adding some precision, you’ll clarify your efforts to reactivate existing learning.

Note: Defining “Customer Insights”
Customer insights are research-based statements about customers (people, users, prospects, lapsed customers, etc.) that can shape product development and delivery. Useful customer insights are more than automatically identified themes. They are carefully written as “problems to solve” or “fundamentals” about people and their needs (more on these insight types in Chapter 5, “Launch Knowledge-Consolidating Tools”). They are backed by supporting evidence and crafted with particular internal audiences in mind. Customer insights do not have to represent new learning, and they can include small-scale, emerging observations or learning from multiple studies. While some insights are “evergreen,” others can be “solved” through a series of experiments or even a single release.

Depending on the market you work in, your customer insights could sound something like the following examples:

• Health care: Patients released from major surgery struggled to follow through on assigned next steps, with the majority missing at least one assigned step.
• B2B Retail: Prospective customers had difficulty finding sufficient information about how our return process worked, increasing their hesitancy about making larger purchases.
• Artificial Intelligence: Customers found that the prompt examples that they copied from the web did not always deliver promised results, resulting in them frustratedly rewriting prompts to get closer to their desired outcomes.
• Logistics: Small business shippers felt misled by our 24-hour shipping promise, surprised to find that the promise did not include the lead time needed to process their new shipping request.
• Enterprise: Information workers were accidentally deleting comments while trying to mark them as “in progress,” resulting in misleading and embarrassing notifications being sent to their colleagues who provided feedback.

With definitions for “customer research” and “customer insights” dialed in, it’s worth also getting precise about the desired results of research efforts. “Research impact” is what every insight-generator is aiming for—but ask ten folks what it means, and you may get ten different answers.

Note: Defining “Research Impact”
Research impact is the informed action that builds meaningful plans, launched outcomes, organizational changes, and individual careers. To have an impact, research can provide new conceptual understanding that frames future decisions, serve as justification for decisions that were already underway, or provide the instrumental reason to create and prioritize a plan. Research impact may occur long after a study is complete. The sum of impacts is valuable to understand at the level of the individual contributor, team, research discipline, and broader research community (described in Chapter 11, “Link Research Rationale to Plans”).

To make these definitions more concrete for you and your partners, consider running a small analysis. Revisit a set of research reports that struck you as high quality and insightful. Choose reports from very different types of customer research. In this review, identify the insights in each report and investigate which were actually acted upon. If there’s a lot there, you can focus on the top insights. It’s worth capturing any type of impact that you can track down (it doesn’t have to map to the report’s particular recommendations).

In this analysis, you may uncover insight wins that tell stories of minds shifted, features envisioned, designs enhanced, problems prevented from launching, and many other tangible results (see Figure 1.2). These impacts are essential to shared success, improving your customers’ everyday experiences and the topline numbers that your organization measures itself by.

Figure 1.2

You can find research waste by visualizing the impacts of insights in an “old” report. 

Alongside those winning insights that landed clear impacts, a cruel reality awaits. When you’re looking across a sampling of reports, there’s often an unsettling volume of insights that did not seem to be addressed in any way. And it’s not necessarily the secondary, less important learning. It can include critical insights that have fallen off the radar of teams who might do something about them—if they even heard about them in the first place.

Now imagine scaling up your small analysis. As your organization continues quarter after quarter, different lines of investigation are accumulating their own volumes of untapped evidence and insights. So much of the potential value of research cost centers (or insight generators embedded in profit centers) lies undiscovered and unconnected.

Imagine a Way Forward

What analyzing insight utilization might look like

A senior user experience researcher at a government agency is interested in how much of the learning generated in her division actually gets applied. To try to get a sense of the type of insights that aren’t being used, the UX researcher decides to:

• Find the two major customer research reports her manager told her to read when she joined the team, as well as two more recent studies that used very different methodologies.
• Select what seemed like the top insights from each report, relying on executive summaries when they’re available.
• Meet with the research authors to learn what they counted as “wins” from these studies.
• Investigate resulting changes to the citizen experience and meet with relevant implementation teams to gather their input.
• Assign a level of impact of each top insight: green for minds shifted from conceptual learning, yellow for minor improvements to planned launches, orange for major enhancements to planned launches, red for distinct new feature launches driven by research, and gray for no known impact.
• Present her analysis to leadership, pivoting to a future-focused brainstorm of how to better connect top insights to planning rituals.

Lost Value from Research Waste

Research waste is a problem at organizational scale, and addressing problems at that scale requires some interest from your leadership. To that end, you can connect the idea of research waste to what your leaders fundamentally care about. For example, some negative outcomes of research waste that might get the attention of any leader are:

• New offerings that fail to find a viable market fit, despite significant investment
• Products that have found a customer base but are abandoned in favor of a competitor who better understands customer needs
• Actionable opportunities to spur needed growth that never even made it to the drawing board
• Extensive engineering effort spent on features that teams touted as successful—based on small lifts in internal metrics—but were actually unneeded “product debt” that customers perceived as unwanted complexity
• Friction that remains in your product’s current interaction design, leading to lower engagement and increased time spent in frustration with your brand
• Overlooked organizational changes that could address root causes for recurring issues in the customer experience
• High-velocity experimental tweaks that attempt to discover customer needs through expensive implementation but fail to hit the needed scale of improvement
• Unintended harm to individuals and communities that’s left recurring in the wild, one social post away from becoming a major fire drill

Do any of these sound familiar? You can look for instances of these types of outcomes in your own organization and engage with different groups of researchers to learn about the insights that they wish would have been used. Consider how teams’ choices not to prioritize the use of certain insights could become cautionary tales and case studies to build from (described more in Chapter 2, “Understand Your Research Waste”).

The results of wasted research can represent the opposite of what anyone working on a product or service wants to see. These are more than just failures to use some internally generated information. These are end-result problems that have negative impacts on success, in the top-line ways that your organization measures it. In total, across your org chart, all of these individual disconnections represent an expansive problem without a single point of failure.

Living up to an ideal of research-informed product planning can require effort from many different roles. Research waste is clearly not just about researchers. There are no quick fixes. It will take some sort of ongoing initiative (see Chapter 3, “Start a Movement to Reduce Research Waste”) with executive sponsors who can catalyze changes to how customer research gets prepared and used.

You Might Be Asking

What if one type of research is already highly impactful?

Just because a research discipline or team is especially effective and respected, it doesn’t mean that a lot of their most crucial learning isn’t being forgotten or ignored. In fact, researchers who are already landing compelling impact in your organization will often have much more untapped value to give.

Qualitative researchers may point to technologists’ biases toward using quantitative research, but quant-focused researchers may also be complaining about the difficulty of influencing the roadmaps of various internal teams. A quant insight may not inspire new product directions in the same way that a deep qual insight can.

Taking a step back, reducing waste is not an individual race between different researchers. There’s a lot more impact to be found by banding insight-generators’ work together and advocating as a collective across silos. Although it’s not obvious until someone’s given it a try, more integrative insights can become more compelling targets for product planning. If one research discipline (such as data science) has the ear of leadership, it can bring others in to provide decision-makers with better-rounded inputs. This outreach doesn’t have to be some form of good-natured altruism. Instead, it can be part of achieving what researchers are already after: improved outcomes and career advancement.

Won’t tools with AI solve research waste?

Knowledge management (KM) tools won’t solve research waste on their own. While AI options can boost productivity in component tasks, there’s no “push-button” technology that’s going to make the problem of research waste go away. Research repositories (described in Chapter 5) can be essential enablers of impact—but only if they’re deeply adopted into a range of different types of work. Research influence on product planning is a highly social practice that’s enabled by technology, not a strictly technological process.

When appropriate, adding AI-based features to research tooling can valuably support operations like transcription, translation, outlier detection, theming, mapping patterns, standardizing information formats, and more. AI in defined use cases can be a complement for researchers’ smarts, sometimes saving extensive manual efforts.

As you consider applying AI options, these tenets can inform your decisions:

• Security, privacy, ethics, and governance first
  Start by asking “should we” before moving on to “are we allowed?” Seek out diverse perspectives and inputs.
• Enlist technology to support tasks, not to outsource understanding
  Define specific sub-outcomes to offload on the way to larger outcomes, rather than running an AI system to see a definitive “answer.” AI automation can locate and transform content, but only people can understand it. Time spent in research evidence can lead to more valuable and differentiated insights and product plans.
• The more domain knowledge required, the more human involvement
  Being able to create, maintain, and connect actionable research insights requires a rich understanding of your customers, your products, and your organization. Great insights can sit at the intersection of factors that are hard to offload to general automation: related prior learning, documented frameworks, niche industry standards and terms, the features of your product or service itself, and the specific “so what” for possible internal owners. As the variety of domain knowledge needed to accomplish a task increases, researchers should be more invested and “in the loop” of the work.
• The higher importance and risk, the more human involvement
  Ambiguous, “fuzzy” AI automation should not run “hands free” in research processes when the results are critical for the customer or business. As potential criticality rises, researchers should be more invested and “in the loop” of the work.
• The more human interpretation, the less value in automated reinterpretation
  Don’t overwrite researchers’ prior efforts. Instead, explore ways to discover, navigate, amend, and represent previously authored content. For example, AI shouldn’t summarize an insight title that researchers have refined and built consensus on—but AI-based features can build further connections out from that starting point.

A Form of Wealth to Resurface

Research waste represents lost potential to fuel value, and much of that value is waiting to be reactivated. From micro advancements to macro directions, lost impacts from unused insights can happen at every scale. In total, these persistent oversights can add up to substantial opportunity costs that could still become wins. These potential wins could drive plans for better serving people, supporting decision-makers as they advance your organization toward its mission and core goals.

To move forward, your organization needs to find ways to transform more research waste into research wealth—which is the opposite of research waste. You need to capitalize on all of that underutilized learning, using it to inform product decision-making and winning launches. You need to prepare your research assets for use over the long haul of product planning, build motivation for actually using customer insights, and make sure they get integrated into the places where they could inform important decisions. It’s the focus of this book, and it’s a long-term endeavor.

In your own situation, the relative importance of reducing research waste may seem low, given competing priorities. Maybe customer research is a newer focus in your organization, and you currently only have a few people documenting insights. From this vantage point, you may see your organization’s lack of insight coverage to be a more pressing issue. It doesn’t have to be an either/or situation. Keep in mind that decreasing waste is not a single, monolithic task. If you focus on smaller steps to repeatedly activate more research wealth, then there’s no time like the present.

For example, you can ask to see the plans for new studies, looking for opportunities to improve insight follow through. You can take a second look at important research reports to see if you can find some unused insights that could be brought back to owning teams. You’ll learn things from these early conversations with researchers and decision-makers that can enable you to craft bigger ideas for integrating research. And through early, low-investment actions, you can start collecting wins that get people excited about change.

In these early moves, you can grow the acknowledgment that research waste is a problem in your organization—and that going after the problem will unlock advances for the things that your leadership is aiming for. Eventually, your organization’s collected research learning can become an internal product for planning, and you can continually experiment to grow the influence of your distributed research community. As you gradually transform research into a more prominent collective stakeholder, customer insights can be recognized more consistently as valued business assets.

Imagine a Way Forward

What Starting to Recycle Research Might Look Like

A research operations professional at a household name financial firm has established solid operations for recruiting participants, and she now wants to show the value that “old” research reports can deliver. To create an example case study, the reops pro decides to:

• Ask researchers around her organization about wasted insights from old studies that could become quick wins.
• Meet with a technical product manager to get their sense of which existing insights could be low effort and high reward, factoring in the context of existing roadmaps.
• Do a deep dive on one promising existing insight, only to discover that not much has changed in the feature area or customer behavior since the research was originally conducted.
• Pitch a research-driven experiment to the group that owns the feature area.
• Help analyze the resulting experimental data, uncovering a massive win based on the years-old research—the kind of win that gets everyone talking.
• Connect the leaders to researchers with more insights that could be driving big metric lifts.

Reduce Research Waste

Here’s a summary of ideas for growing a culture that gets more value from research:

• Share the idea of “research waste” with your colleagues to give a name to this category of improvement.
• Revisit an acclaimed research report to investigate which customer insights were acted on—and invite others to do the same.
• Start an inventory of stories about lost value from research waste in your organization (including unsuccessful launches that did not adequately use known customer insights).
• Kick off conversations with different types of researchers and decision-makers about what could be done to increase research-informed planning.
• Experiment with revitalizing overlooked insights that could still add value.

You’ll know that your early efforts to define and spotlight research waste in your organization are paying off when:

• There’s an emerging understanding that many types of research can have enduring value that’s not being capitalized on.
• Some planning processes are being scrutinized with an eye toward improving how they incorporate customer research.

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