The Next Era of Insights: From Reporting to Co-Creating

The insights profession stands at an inflection point. At TMRE 2025, a pattern emerged across sessions on insights operating models, anthropological research methods, and analytics integration: the traditional post-mortem function is giving way to something fundamentally different. The question is no longer whether insights teams should move upstream, but how they execute that transformation without losing the rigor that makes their work credible.

The shift represents more than organizational restructuring. It reflects a recognition that in markets where competitive advantage compounds weekly rather than quarterly, retrospective analysis arrives too late to matter. When Unilever's Chief Insights Officer described their team's evolution from "report generators to strategy architects," she quantified the impact: decisions informed by prospective insights delivered 3.2x higher ROI than those validated retrospectively. The stakes of this transformation have become measurable.

The Limitations of Hindsight

Traditional insights functions were built for a different competitive environment. When product cycles measured in years and campaigns ran for quarters, retrospective analysis made sense. Teams could conduct post-launch research, synthesize findings over weeks, and feed insights into the next planning cycle. The methodology was sound. The timing was catastrophic.

The economics of delayed insights compound in ways most organizations fail to quantify. Consider the typical enterprise product launch: conception in January, research findings in March, product refinement through summer, launch in September. By the time post-launch insights arrive in November, the market has already voted. Competitors have responded. Customer needs have evolved. The insights are accurate but irrelevant.

Research from the Conference Board's 2024 insights effectiveness study reveals the magnitude of this timing problem. Among Fortune 500 companies, insights that arrived during strategy formation influenced decisions 4.7x more effectively than those delivered post-execution. The median time-to-influence for retrospective insights was 11 months. For prospective insights embedded in strategy formation, it was 6 days. The organizational impact extends beyond influence metrics. When insights arrive after decisions are made, they serve primarily to validate existing choices or assign blame for failures. Neither function builds the strategic credibility that transforms insights from a service function into a competitive advantage.

The Co-Creation Model

The alternative model discussed throughout TMRE positions insights as strategy co-creators rather than validators. The distinction matters. Validators react to plans developed elsewhere. Co-creators shape those plans from inception. The operational implications are profound.

Mars Petcare's head of consumer insights described their transformation with unusual candor. Three years ago, their team received product briefs after concepts were developed. Today, they sit in initial ideation sessions, contributing customer understanding as products are conceived. The change required more than calendar invitations. It demanded new capabilities: the ability to contribute insights at the speed of conversation rather than the pace of research projects, methods that generate directional intelligence in hours rather than conclusive findings in weeks, and frameworks that connect customer truth to business strategy without ten-slide decks.

The results justify the disruption. Product concepts developed with insights present from inception achieved 67% higher market acceptance scores than those where insights validated later-stage concepts. More tellingly, the early-involvement products required 40% fewer iterations to reach launch readiness. Co-creation doesn't just improve outcomes. It accelerates them.

The anthropological perspective offered by EPIC's session on immersive research methods provides theoretical grounding for why co-creation works. When insights professionals observe strategy formation in real-time, they recognize the unstated assumptions, implicit biases, and knowledge gaps that shape decisions. These moments of formation are when customer truth matters most—when mental models are still fluid enough to incorporate evidence that challenges comfortable assumptions.

Layered Data Architecture

The co-creation model fails without appropriate data infrastructure. Traditional insights functions could operate with episodic research because their role was retrospective. Co-creation requires continuous intelligence across qualitative depth and quantitative scale. The architecture that enables this transformation emerged repeatedly in TMRE sessions on analytics integration and real-time insights.

PepsiCo's analytics lead outlined a three-layer model that several Fortune 500 companies are converging on. The foundation layer consists of always-on behavioral data: transaction patterns, usage analytics, digital engagement metrics, and customer support interactions. This layer provides continuous signals about what customers do, at population scale, with minimal latency. It answers the "what is happening" question that orients strategic discussions.

The middle layer adds attitudinal context through scaled qualitative research. Where traditional approaches treated qualitative research as small-sample exploration, modern methods enable continuous conversation at scale. The breakthrough isn't technological—conversational AI that can conduct naturalistic interviews existed for years. The breakthrough is methodological: research designs that maintain qualitative depth while achieving quantitative scale. This layer answers "why it's happening" at sufficient scale to inform strategy with confidence.

The top layer consists of deep ethnographic investigation. These immersive studies explore contexts, uncover unstated needs, and reveal the cultural dynamics that quantitative methods miss. Where the foundation layer provides breadth and the middle layer adds explanation, the top layer delivers the insight that creates differentiation. This layer answers "what does it mean" in ways that transform strategy rather than simply inform it.

The power emerges from integration. Behavioral data identifies anomalies worth investigating. Scaled qualitative research explains patterns at population level. Ethnographic depth reveals the strategic implications. Separately, each layer provides partial intelligence. Together, they enable the prospective understanding that co-creation requires.

Upstream Engagement Model

Moving insights upstream requires more than better data. It requires operational transformation. The sessions on building insights functions repeatedly emphasized that co-creation fails when insights teams retain the operating model designed for retrospective reporting. The required changes span how insights teams structure their work, develop their capabilities, and measure their impact.

The structural transformation involves shifting from project-based to embedded models. Procter & Gamble's organizational restructuring, discussed in their operating model session, illustrates the pattern. Rather than a centralized insights function that serves the organization through formal research requests, they distributed insights professionals into product and marketing teams while maintaining a center of excellence for methodology and capability development. The embedded professionals participate in daily strategic discussions, contributing customer intelligence as decisions are being shaped rather than after they're made.

This structure requires different capabilities than traditional insights roles demanded. The embedded professional must synthesize findings in conversation rather than through formal presentations, contribute directional intelligence based on partial data while being clear about uncertainty, translate between customer language and business strategy, and recognize which questions require deep investigation versus rapid exploration. These capabilities differ from those that made someone an excellent research project manager or survey methodologist.

The capability development challenge explains why many upstream transformations stall. Organizations restructure reporting lines without developing the new skills the model requires. Nestle's learning and development approach, described in their capability-building session, addresses this gap explicitly. Their insights professionals rotate through strategy, product, and marketing roles to understand how those functions think and work. The goal isn't to make insights professionals into strategists or marketers. It's to make them fluent enough in those disciplines to contribute effectively in formation-stage discussions.

Real-Time Impact Measurement

The transformation from retrospective reporting to prospective co-creation demands new impact metrics. Traditional measures—research project completion, stakeholder satisfaction scores, insight delivery timelines—evaluate execution of the old model. They fail to capture whether insights actually influenced decisions or improved outcomes. The measurement challenge emerged repeatedly at TMRE, with several organizations sharing approaches that move beyond activity metrics to genuine impact assessment.

The foundational shift involves measuring decision influence rather than delivery completion. Johnson & Johnson's insights team tracks a metric they call "strategic direction changes": instances where insights led to meaningful alterations in product, positioning, or go-to-market strategy before execution. They distinguish between insights that validated existing plans (useful but not transformative) and those that changed strategic direction (the co-creation outcome they seek). Over two years of tracking, strategic direction changes correlated with market performance 3.4x more strongly than total insights delivered.

The measurement extends to business outcome attribution. While perfect causal attribution remains elusive, modern approaches estimate insights' contribution to outcomes with reasonable confidence. One pharmaceutical company described their methodology: for each major product launch, they document the strategic decisions informed by insights, estimate the revenue impact of those decisions, and calculate insights' contribution to the outcome. The analysis revealed that insights influenced decisions worth $2.3 billion in incremental revenue over three years—a metric that transformed budget discussions.

The temporal dimension of measurement matters equally. Traditional insights functions measured cycle time: days from request to delivery. Co-creation models measure time-to-influence: days from insight generation to strategic incorporation. Adobe's insights organization reduced their median time-to-influence from 47 days to 6 days through upstream embedding and continuous research methods. The acceleration compounds: decisions informed by timely insights succeed more often, creating organizational appetite for insights that perpetuates the virtuous cycle.

The Anthropology Advantage

The anthropological perspective discussed in multiple TMRE sessions provides theoretical grounding for why co-creation succeeds where retrospective reporting fails. Traditional market research, for all its methodological rigor, often treats customers as static data sources. Ask about preferences. Document behaviors. Analyze patterns. The approach generates valid findings about what exists but struggles to reveal what's emerging or what's possible.

Anthropological methods start from different assumptions. Customers aren't just data sources. They're participants in cultural systems that shape behavior in ways surveys cannot capture. The brand-switching decision that appears irrational in survey data makes perfect sense when understood within the social context where it occurred. The product feature that tests poorly in concept evaluation succeeds in market because it solves a problem customers couldn't articulate until they experienced the solution.

Samsung's consumer insights team, describing their ethnographic approach to product innovation, illustrated this advantage with specificity. Traditional research on smartphone preferences consistently emphasized battery life, camera quality, and processing speed—rational attributes customers can easily articulate. Ethnographic observation revealed that status signaling through device choice drove purchase decisions more powerfully than any functional attribute. This finding didn't emerge from asking about preferences. It emerged from observing how people used, displayed, and discussed their devices in social contexts. The insight transformed their positioning strategy in ways that pre-launch concept testing never would have revealed.

The anthropological approach proves particularly valuable when markets are evolving rapidly. Cultural change precedes behavioral change, which precedes attitudinal change. By the time customers can articulate new needs in surveys, early movers have already addressed them. Ethnographic methods detect weak signals—subtle shifts in language, emerging practices, changing social norms—that signal what's next before it becomes obvious. This temporal advantage is precisely what upstream co-creation requires.

The Analytics Integration Challenge

The layered data architecture sounds elegant in conference presentations. Implementation proves messy. The analytics integration sessions at TMRE acknowledged this reality with refreshing honesty. Building infrastructure that seamlessly connects behavioral analytics, scaled qualitative research, and ethnographic depth requires overcoming technical, organizational, and cultural obstacles that have derailed many transformation efforts.

The technical challenge starts with data incompatibility. Behavioral analytics systems output quantitative metrics designed for dashboards and statistical analysis. Ethnographic research generates narrative accounts and observational field notes. Scaled qualitative research produces conversational data at volumes that overwhelm manual analysis but resist traditional quantitative methods. These data types weren't designed to integrate. Making them work together requires more than data warehousing. It requires analytical approaches that maintain the distinct value of each data type while enabling cross-layer synthesis.

Target's approach to this challenge, described in their analytics integration session, involves what they call "insight triangulation protocols." Rather than attempting to merge different data types into unified metrics, they developed frameworks for validating findings across layers. When behavioral data suggests a pattern, they use scaled qualitative research to explain it and ethnographic investigation to contextualize it. The layers remain distinct but mutually reinforcing. The protocol ensures that strategic recommendations rest on convergent evidence rather than single-source findings.

The organizational challenge involves breaking down functional silos that were deliberately constructed. Analytics teams, market research teams, and consumer insights teams traditionally operated independently, with distinct methodologies, reporting lines, and stakeholder relationships. Integration requires shared objectives, collaborative workflows, and combined performance metrics—changes that threaten established territories and career paths. Coca-Cola's organizational transformation, discussed in their operating model session, took three years and required executive intervention when functional leaders resisted integration that diluted their autonomy.

The cultural challenge may be most difficult. Different research traditions carry different epistemological assumptions about what constitutes valid knowledge. Quantitative analysts privilege statistical significance and large samples. Qualitative researchers emphasize contextual depth and meaning. Ethnographers focus on cultural patterns and emergent phenomena. Each tradition views the others with some skepticism. Building integrated insights functions requires not just tolerance of different approaches but genuine appreciation for how each contributes uniquely to strategic understanding.

The Foresight Capability

The ultimate objective of upstream co-creation isn't just earlier involvement in strategic processes. It's developing genuine foresight capability—the ability to recognize emerging opportunities and threats before they appear in historical data. Multiple TMRE sessions explored what foresight actually means operationally and how organizations develop it systematically rather than hoping for occasional intuitive leaps.

Foresight differs from forecasting. Forecasting extrapolates trends evident in historical data. Foresight identifies signals of discontinuity—the weak indicators that current patterns are breaking down and new ones emerging. Forecasting tells you that smartphone adoption will continue at current rates. Foresight recognizes that AR wearables may make smartphones obsolete before adoption curves complete. The distinction matters because strategic advantage comes from recognizing inflection points early, not from extrapolating existing trends slightly more accurately than competitors.

Developing foresight capability requires systematic attention to weak signals that most organizations filter out as noise. Intel's strategic insights team described their "edge observation" methodology: deliberately studying customer segments, use cases, and geographies where unusual behaviors appear first. Gaming enthusiasts who modify hardware. Students in emerging markets who use technology in unexpected ways. Power users who break products through intensive use. These edge populations often preview behaviors that mainstream markets adopt later. By studying edges systematically rather than dismissing them as outliers, Intel identifies emerging patterns 12-18 months before they appear in mainstream data.

The challenge involves distinguishing meaningful weak signals from random noise. Most unusual behaviors remain unusual. Most fringe practices never go mainstream. Most outliers are just outliers. Foresight requires judgment about which signals matter—a capability that resists algorithmic automation. The organizations developing genuine foresight capability do so by combining systematic observation of edges with deep understanding of the cultural and technological forces that drive diffusion. They study weak signals in context, asking not just "what's happening" but "what conditions would cause this behavior to spread."

The New Insights Charter

The transformation from retrospective reporting to prospective co-creation amounts to rewriting the insights function's charter. The traditional charter emphasized rigor: conduct methodologically sound research, deliver accurate findings, maintain objectivity. These remain necessary but insufficient. The new charter adds strategic contribution: shape decisions during formation, provide foresight about emerging opportunities, measure impact on business outcomes.

The charter rewrite has organizational implications. Traditional insights functions could operate as service organizations, responding to research requests from business units. Co-creation requires partnership status—insights professionals who participate in strategic planning as equals, contribute perspective rather than just data, and share accountability for outcomes. This elevation sounds appealing until organizations confront what it actually requires: insights professionals with strategic capability, business units willing to share decision-making authority, and executive commitment to organizational change that disrupts comfortable patterns.

Several TMRE sessions explored how organizations navigate this transformation without sacrificing the research quality that makes insights credible. The answer involves maintaining dual commitments: to strategic relevance and methodological rigor, to speed and validity, to influence and objectivity. These pairs create productive tension rather than impossible contradiction. Strategic relevance demands that research addresses questions that matter for decisions being made now. Methodological rigor ensures that the answers are trustworthy. Neither commitment permits compromising the other.

The Kellogg Company's insights transformation, described across multiple sessions, illustrates how dual commitments work in practice. Their upstream engagement means insights professionals participate in product ideation from day one. Their methodological rigor means they pause strategic discussions to gather evidence when decisions rest on unvalidated assumptions. The combination creates strategic relevance without sacrificing research quality. It also creates cultural conflict when business leaders want quick validation of preferred options rather than genuine investigation of uncertain questions. Managing that conflict is part of the new charter.

Measuring the Transformation

How do organizations know whether their transformation from retrospective reporting to prospective co-creation is actually working? The measurement challenge occupied multiple TMRE conversations. Activity metrics—research projects completed, insights delivered, stakeholder meetings held—measure execution of the old model. Outcome metrics—decisions influenced, revenue impacted, strategic direction changes—capture the new model's success but prove difficult to attribute and isolate from confounding factors.

The most sophisticated approaches combine multiple measurement dimensions. Influence metrics track how often insights changed strategic direction versus validated existing plans. Timing metrics measure when insights entered decision processes—during formation or after commitment. Quality metrics assess whether insights met stakeholder needs for strategic clarity and actionability. Outcome metrics estimate business impact of insights-informed decisions. No single metric suffices. Together, they provide reasonable confidence about whether transformation is succeeding.

General Mills shared their measurement framework with unusual transparency. They track four primary metrics: strategic decision influence (percentage of major decisions where insights changed direction), time-to-influence (days from insight generation to strategic incorporation), business outcome attribution (estimated revenue impact of insights-informed decisions), and capability development (percentage of insights professionals rated as strategic contributors by business leaders). Over three years, they've seen strategic decision influence increase from 23% to 67%, time-to-influence decrease from 41 days to 8 days, and estimated annual revenue impact grow from $340 million to $1.8 billion.

These numbers warrant appropriate skepticism. Attribution is imperfect. Confounding factors are inevitable. Some portion of measured impact would have occurred without insights. But the directional pattern matters more than precise quantification. The transformation from retrospective reporting to prospective co-creation is working when insights demonstrably influence strategic decisions earlier in formation processes and those insights-informed decisions generate measurably better outcomes. Perfect attribution is impossible. Directional confidence is sufficient for organizational decision-making about whether to continue investing in transformation.

The Path Forward

The transformation from retrospective reporting to prospective co-creation represents more than operational change. It reflects recognition that in markets where advantage compounds rapidly, historical analysis arrives too late to create advantage. The organizations succeeding at this transformation share several characteristics: executive commitment to elevating insights' strategic role, investment in layered data architecture that enables continuous intelligence, development of capabilities that support upstream engagement, and measurement systems that track strategic impact rather than just research completion.

The transformation isn't costless. It requires organizational disruption, capability development, technical infrastructure, and cultural change. Some insights professionals will thrive in the new model. Others will struggle with demands for strategic contribution and real-time synthesis that differ from traditional research strengths. Some business leaders will embrace insights as co-creators. Others will resist sharing decision-making authority with what they view as support functions.

But the economics increasingly favor transformation. As competitive cycles accelerate, the cost of delayed insights exceeds the cost of developing foresight capability. As markets become more dynamic, the value of understanding emergent patterns outweighs the comfort of analyzing historical trends. As customer expectations evolve rapidly, the need for continuous intelligence surpasses the sufficiency of quarterly research.

TMRE 2025 made clear that this transformation is no longer emerging—it's happening. The question for insights functions isn't whether to move upstream but how to execute that move without sacrificing the research quality that makes insights credible. The organizations succeeding at this balance are building competitive advantages that compound as markets accelerate. They're transforming insights from a retrospective validation function into a prospective strategy advantage. That transformation represents the next era of insights.

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