Research at the Pace of Product: What UX and Consumer Insights Can Learn From Each Other

The conference rooms at TMRE 2025 buzzed with a familiar tension. In one session, a UX researcher described their team's two-week sprint cycles and the pressure to deliver insights before the next standup. Three floors up, a consumer insights leader outlined their quarterly brand tracking study and annual segmentation refresh. Both were doing excellent work. Neither could use the other's research to inform their decisions.

This disconnect isn't just an organizational chart problem. It represents a fundamental mismatch between how modern product development operates and how traditional research functions are structured. The gap has real consequences: UX teams make product decisions without brand context, consumer insights arrive too late to influence feature prioritization, and organizations end up with fragmented understanding of the same customers they're trying to serve.

What became clear across multiple TMRE presentations is that this isn't a problem either function can solve alone. The solution requires both disciplines to fundamentally rethink their operating models—not to become identical, but to create complementary rhythms that actually serve business needs. The question isn't whether UX should slow down or CI should speed up. It's how both can restructure their work to deliver insights when decisions actually get made.

The Velocity Problem: Why Traditional Research Cadences Break Product Development

The typical consumer insights calendar looks rational from a research methodology perspective. Quarterly brand tracking provides statistically robust trending. Annual segmentation studies require sufficient sample sizes and analysis time to be defensible. Ad hoc projects get scoped, recruited, fielded, and analyzed with appropriate rigor. For questions that operate on strategic timelines—brand positioning, market segmentation, long-term trend analysis—these cadences work.

But product development doesn't operate on this schedule. According to the 2024 State of Agile Research report by dscout, 78% of product teams now work in sprint cycles of three weeks or less. Feature decisions get made in planning meetings that happen every two weeks. A/B tests run for days, not months. The product roadmap that seemed fixed in January has pivoted three times by April based on user feedback, competitive moves, and technical constraints.

When research operates on quarterly or annual rhythms, it creates a predictable pattern. Product teams need answers about feature prioritization by Thursday's planning meeting. Research can deliver insights in six weeks. The team makes the decision based on whatever information they have—engineering complexity estimates, stakeholder opinions, that one customer email that's been forwarded around. Research arrives in week seven with data showing the feature wasn't the highest priority. The feature is already in development.

This velocity mismatch compounds over time. Product teams stop asking research questions because they know the answers won't arrive in time to be useful. Research teams end up studying questions that have already been decided, producing reports that document history rather than inform decisions. The organization maintains both functions but loses the value that comes from their collaboration.

The UX research function evolved specifically to solve this timing problem. By embedding researchers directly in product teams, using rapid methods like unmoderated testing and prototype evaluation, and trading some statistical power for speed, UX research can operate at sprint velocity. But this creates a different problem: UX research typically focuses on narrow, tactical questions about specific features or flows. It doesn't capture the broader brand context, market dynamics, or strategic positioning questions that consumer insights addresses.

The Integration Challenge: Why Bolting Functions Together Doesn't Work

The obvious solution is integration: get UX and CI working together. Most organizations have tried this. The typical approach involves regular meetings, shared Slack channels, and encouragement to "collaborate more." These efforts consistently fail to solve the fundamental problem.

The reason is structural, not personal. UX researchers are evaluated on their impact on product velocity—did insights inform this sprint's decisions? Consumer insights teams are measured on strategic impact—did research influence brand positioning, market strategy, or major investment decisions? These aren't compatible success metrics. One function is optimized for speed and relevance to immediate decisions. The other is optimized for rigor and strategic validity.

Project structures reflect these different objectives. A typical UX research project might look like this: question emerges in sprint planning Monday, researcher conducts five user interviews Tuesday and Wednesday, synthesis happens Thursday morning, findings presented at Thursday afternoon design review, decisions incorporated into next sprint. Total cycle time: four days. Sample size: five participants. Methodology: qualitative interviews with current users.

A typical consumer insights project follows a different pattern: business question defined in planning meeting, research design developed over two weeks including sample planning and instrument development, fielding happens over three weeks to ensure proper representation, analysis takes two weeks, stakeholder presentations occur over another two weeks. Total cycle time: nine weeks. Sample size: 300-1000 participants. Methodology: quantitative survey with statistical analysis.

These aren't just different speeds. They're fundamentally different approaches to knowledge generation, with different assumptions about what makes research valid, actionable, and defensible. Telling UX to "be more rigorous" makes their research too slow to inform product decisions. Telling CI to "be more agile" undermines the statistical validity that makes their work credible for strategic decisions.

Several TMRE presentations acknowledged this structural incompatibility. The breakthrough came from organizations that stopped trying to make the functions identical and instead focused on creating complementary operating models with intentional synchronization points.

Shared Learning Agendas: From Parallel Work to Coordinated Discovery

The most successful integration approaches start with shared learning agendas—not shared backlogs, but coordinated frameworks that identify the questions both functions need to answer and explicitly design how different research methods will address different aspects of those questions.

Airbnb's research organization, discussed in their TMRE case study, provides a working model. Rather than maintaining separate UX and CI backlogs, they organize around "learning themes"—major questions the business needs to answer over a six-month period. For example: "What drives host retention after their first year?"

This learning theme immediately suggests different types of research. UX research can conduct interviews with hosts in their first year to understand onboarding friction, test improvements to the host dashboard, and measure whether changes to the getting-started experience improve early engagement metrics. Consumer insights can field a quantitative study with hosts across their lifecycle to identify which factors predict long-term retention, segment hosts based on their motivations and behaviors, and track how retention drivers change over time.

These aren't redundant studies. They're complementary investigations designed to answer different aspects of the same business question. UX research provides tactical insights that inform immediate product improvements. Consumer insights provides strategic context that shapes long-term product direction. Both functions work faster because they're not trying to do everything—they're doing what their methods do best, with coordination ensuring the pieces fit together.

The learning agenda approach requires structural changes to how research gets planned and prioritized. At Spotify, another organization featured in TMRE discussions, this meant moving from functional backlogs to cross-functional learning roadmaps. Every quarter, research leads from UX, consumer insights, and data science meet with product and marketing leadership to define 3-5 major learning themes. Each theme gets a "learning brief" that articulates the business decision this needs to inform, what we already know, what we need to learn, and how different research methods will contribute.

This process makes the complementarity explicit. A learning theme about music discovery might include UX research on in-app discovery flows, consumer insights on how people discover music across all contexts (not just in Spotify), and data science analysis of discovery patterns in behavioral data. The learning brief clarifies how these pieces fit together and where hand-offs need to occur.

Research Building Blocks: Designing for Reuse and Recombination

Learning agendas coordinate strategy, but they don't solve the velocity problem. Product teams still need insights faster than quarterly studies can deliver. The solution requires rethinking the fundamental unit of research work.

Traditional research is project-based. Each business question triggers a new research project with custom design, custom recruitment, custom analysis. This made sense when research was expensive and infrequent. But when research needs to happen continuously, project-based approaches become bottlenecks. The time spent on research design, sample planning, and instrument development for each project prevents research from operating at product velocity.

Several organizations showcased at TMRE have moved to component-based research models, where instead of designing each study from scratch, teams maintain "research building blocks" that can be deployed and recombined quickly. This requires investment in creating reusable components, but dramatically accelerates research once those components exist.

Fidelity's research organization, which presented their approach at TMRE, maintains a library of validated question modules covering core topics: investment decision-making, financial confidence, advisor relationships, digital tool satisfaction. Each module has been tested for reliability, validated against behavioral data, and optimized for different contexts (long surveys, short intercepts, in-product questionnaires). When product teams need research, they can assemble these modules into custom instruments in hours rather than designing from scratch.

This approach works because many research questions aren't actually unique. Product teams think they need custom research because they're asking "Should we build this specific feature?" But that question typically decomposes into more universal components: Do users have the need this feature addresses? How do they currently solve this problem? What frustrations exist with current solutions? How would this fit into their workflow? These are building blocks that can be designed once and reused across many specific feature decisions.

The building block approach extends beyond questions to entire research protocols. Several TMRE presenters described maintaining "research plays"—standardized but flexible protocols for common research needs. A "feature validation play" might include: five user interviews using a specific protocol, a prototype test with ten participants following a defined structure, and a quantitative desirability study using validated scales. Product teams can trigger this play when they need validation, with research operations handling recruitment and execution while UX researchers focus on adaptation and interpretation.

This standardization doesn't eliminate customization—it relocates it. Instead of customizing everything, teams customize only what needs to be unique to their specific question. The framework, recruitment approach, and analysis methods are standardized, making execution faster and results more comparable across studies. The specific content, context, and application remain customized to the decision at hand.

Consumer insights functions can contribute building blocks that UX teams can deploy. Brand perception questions that take ten seconds to answer can be embedded in UX studies, providing ongoing brand tracking without requiring separate studies. Segmentation frameworks developed through deep consumer research can be applied as screening criteria in UX recruitment, ensuring prototype testing includes appropriate segment representation. Validated scales for measuring brand attributes or category attitudes can be embedded in product feedback surveys.

This requires CI teams to think differently about their outputs. Instead of delivering studies, they deliver research infrastructure—validated instruments, sampling frameworks, analysis templates, and normative benchmarks that other researchers can use. This infrastructure work happens at strategic cadence, but enables tactical research to move at product velocity.

Synchronization Points: Where Strategy and Tactics Need to Connect

Even with shared learning agendas and research building blocks, UX and CI operate at different speeds. This isn't a problem to solve—it's a reality to design for. The key is creating explicit synchronization points where strategic context informs tactical decisions and tactical insights surface strategic questions.

The most effective synchronization points aren't meetings, they're structured information flows. Microsoft's research organization, profiled at TMRE, maintains a "research context repository" that surfaces relevant CI findings when UX researchers are planning studies. If a UX researcher is studying onboarding, the system surfaces segmentation data about new user types, brand perception research about what drove acquisition, and competitive analysis about alternative onboarding patterns. This takes minutes, not hours, but dramatically improves how UX research gets contextualized within broader market understanding.

The information flow needs to work in both directions. UX research generates thousands of small insights—individual user feedback, usability issues, feature requests, behavioral observations. Individually, these are tactical. Aggregated, they often reveal strategic patterns. But this aggregation rarely happens because UX researchers move too fast to synthesize across projects, and CI teams don't have access to the granular UX data.

Several organizations have created "insight aggregation systems" that automatically surface patterns in UX research data. When ten different UX studies in a quarter mention pricing confusion, that's a tactical finding in each study but a strategic pattern overall. When prototype testing consistently shows misunderstanding of a key brand message, that's valuable input for brand strategy that would otherwise remain buried in individual study reports.

This aggregation needs to happen systematically, not manually. Manual aggregation is too slow and incomplete. Organizations using insight management platforms like Dovetail, Notably, or User Interviews are building automated analysis that flags when tactical patterns suggest strategic questions. These systems use natural language processing to identify recurring themes across studies, flag when findings contradict previous research, and surface when tactical insights have strategic implications.

The strategic-to-tactical flow also requires structure. CI teams produce 50-100 page reports that no one on product teams has time to read. The solution isn't to make reports shorter—strategic analysis requires depth. The solution is creating multiple entry points: executive summaries for leadership, key findings slides for product reviews, and "research briefs" designed specifically for product teams that extract the 3-5 insights most relevant to their current decisions.

Capital One's approach, shared at TMRE, includes "insight digests" produced monthly that translate recent consumer research into implications for product teams. These digests are explicitly tactical: "Recent research on digital banking preferences found that 67% of users prefer biometric authentication over passwords. Recommendation: Prioritize fingerprint/face authentication in upcoming security improvements." The digest links to full research for those who want depth, but provides actionable takeaways for those who need speed.

Operating Models That Work at Multiple Speeds

The organizations successfully bridging UX and CI have converged on similar operating models despite starting from different contexts. These models share several characteristics:

They maintain distinct research functions rather than trying to merge them. UX research remains embedded in product teams, operating at sprint velocity, focused on tactical product decisions. Consumer insights remains centralized or matrixed, operating at strategic cadence, focused on market understanding and brand questions. But the functions coordinate explicitly through shared learning agendas rather than working in parallel.

They invest in research infrastructure—building blocks, protocols, validated instruments—that enables speed without sacrificing rigor. This infrastructure work happens at strategic cadence, but enables tactical research to accelerate. The infrastructure itself becomes a synchronization point, as strategic research produces tools that tactical research uses.

They create bidirectional information flows through systems rather than meetings. Strategic insights flow into tactical planning through research repositories and decision support tools. Tactical patterns flow into strategic analysis through insight aggregation and pattern detection. This happens continuously rather than at scheduled intervals, making the connection between functions ongoing rather than episodic.

They measure both functions on business impact, not research output. UX research is evaluated on how product decisions improved because of research insights. Consumer insights is evaluated on how strategic decisions improved because of market understanding. This creates natural pressure for coordination—if tactical decisions ignore strategic context, products drift from market needs. If strategic research ignores tactical realities, it becomes irrelevant to actual decisions.

They recognize that different questions require different research approaches and explicitly match methods to decision cadence. Feature-level decisions get fast, qualitative research. Investment decisions get rigorous, quantitative research. Strategy decisions get comprehensive, mixed-method research. This matching happens intentionally rather than defaulting to whatever method the assigned researcher prefers.

Practical Implementation: Starting From Where You Are

Most organizations can't rebuild their research function overnight. The question is how to start moving toward this integrated operating model from wherever you currently are.

The most common starting point is creating one shared learning agenda as a pilot. Select a business priority that requires both tactical product insights and strategic market understanding—new product development, major feature initiatives, or expansion into new segments all work well. Define the learning theme collaboratively, identify what different research functions will contribute, and design explicit hand-offs where insights from one function inform work by the other.

This pilot serves multiple purposes beyond the actual insights generated. It creates a template for shared learning agendas that can be replicated for other priorities. It identifies operational friction points—where information doesn't flow smoothly, where timing doesn't align, where goals conflict—that need to be addressed. It demonstrates to leadership that coordination produces better outcomes than parallel work, building support for more systematic integration.

The second common entry point is creating research building blocks for the most frequent research needs. Most product teams repeatedly research similar questions: feature validation, onboarding optimization, pricing decisions, competitive comparison. Converting these from custom projects to standardized protocols with reusable components immediately accelerates research in those common areas. This doesn't require comprehensive infrastructure—start with 2-3 research plays that address the most frequent needs.

Building blocks also provide an opportunity for CI to contribute to product velocity. Consumer insights teams can identify validated measures, segmentation frameworks, or benchmark data that UX teams can incorporate into their protocols. This doesn't require CI to operate at sprint velocity—the building blocks are developed at strategic cadence but deployed tactically.

The third starting point is improving information flow in one direction. If UX insights aren't informing strategic research, create a monthly digest that synthesizes tactical findings and flags strategic implications. If strategic insights aren't accessible to product teams, create a research context tool that surfaces relevant consumer research when UX researchers plan studies. Pick the direction where information flow is most broken and fix it first, then address the other direction.

Each of these starting points creates visible value relatively quickly—in quarters, not years. They also create momentum for broader changes by demonstrating that integration is possible and valuable, not just theoretically appealing.

What This Means for How Research Functions Evolve

The convergence toward integrated operating models has implications for how both research functions need to evolve. For UX research, the challenge is expanding from tactical execution to strategic contribution. This doesn't mean slowing down—sprint velocity remains essential. It means ensuring tactical insights connect to strategic questions, incorporating broader market context into product recommendations, and systematically tracking whether product improvements are moving brand perceptions and market position.

For consumer insights, the challenge is becoming infrastructure as well as insight. This means creating research building blocks that others can use, not just conducting studies. It means shifting from comprehensive standalone projects to modular components that can be deployed at different cadences. It means measuring impact not just by study quality but by how effectively insights inform decisions across the organization.

For both functions, it means recognizing that research at product velocity requires different structures than research at strategic cadence. The organizations that will win aren't those that force all research into one speed. They're those that maintain appropriate rhythms for different questions while creating coordination mechanisms that ensure the pieces fit together.

The TMRE presentations made clear that this evolution is well underway at leading organizations. What remains is translating these models into operational reality at the hundreds of companies still struggling with mismatched cadences and fragmented insights. The templates exist. The challenge now is implementation.

The Path Forward

Research at the pace of product isn't about making everything faster. It's about matching research cadence to decision cadence across different types of questions, creating infrastructure that enables speed without sacrificing rigor, and building coordination mechanisms that ensure insights from different functions and timeframes inform each other.

The organizations that figure this out gain compound advantages. Tactical decisions become more strategic because they incorporate market context. Strategic decisions become more grounded because they reflect tactical realities. The research function becomes more valuable because insights arrive when decisions actually get made. And perhaps most importantly, the organization develops more coherent understanding of customers because different research approaches complement rather than contradict each other.

This isn't just an operational efficiency question. It's about whether research can actually fulfill its promise of making organizations more customer-centric. When research operates at the wrong cadence, even excellent insights become irrelevant. When research operates at appropriate cadences with effective coordination, it becomes the connective tissue that aligns product development with market needs.

The examples from TMRE 2025 show this is possible. The question now is how quickly the rest of the industry can adopt and adapt these models. Because in markets where competitors are already operating with integrated research functions, the strategic disadvantage of fragmented insights compounds every sprint.

‍