Rethinking the Economics of Panels in the Behavioral Data Era

If you spend enough time talking to panel operators these days, a familiar theme eventually appears in the conversation. Recruitment costs are rising. Respondent fatigue is real. Fraud is getting more sophisticated. Meanwhile, the price clients are willing to pay for a traditional survey complete is not exactly going up. In our discussions with all the leading panel companies and survey apps in the world, these topics are up on the agenda on each call, and something we discussed with various industry people also in e.g. Succeet 2026 in Germany, and SampleCon 2026 close to Atlanta.

In other words, the math is getting harder.

None of this is new. Panel companies have been navigating these pressures for years. But something has shifted recently that makes the situation feel more structural than cyclical. The traditional economic model of panels — recruiting people, sending them surveys, paying incentives for each completed questionnaire — was designed for a time when the primary output of research was a response.

Today the primary output is increasingly data and intelligence. And that difference matters more than it might first appear.

When panels were built in the early 2000s, the market research industry operated in a fairly clear structure. Panels recruited respondents, agencies designed studies, and brands purchased insights. The economic engine that connected all of this was simple: the value of a survey completion. It worked remarkably well for a long time. Large panels were built, sophisticated targeting systems emerged, and the industry scaled globally.

But if we look at the digital economy today, it is obvious that something important has changed. The world has become a data economy. First in adtech, now increasingly in market research, too Companies create value not simply by collecting responses but by understanding patterns of behavior, predicting outcomes, and building systems that learn continuously from real-world signals. In that environment, the old model where panelists are compensated only for answering questions begins to look incomplete. After all, the value created by a panel participant often goes far beyond the individual survey they happen to complete on a given afternoon. When a person participates in a panel, they contribute something much more significant than a set of answers to a questionnaire. They contribute a connection to real human behavior, real purchasing decisions, real media consumption, real digital activity.

And as market research increasingly incorporates behavioral data alongside surveys, the scope of that contribution expands dramatically.

At that point, a slightly uncomfortable question begins to emerge. If panelists are contributing data that fuels insights across multiple products, analytics platforms, and industries, is compensating them solely for survey completions still the most logical model I suspect the answer is increasingly no. This is where a concept that we sometimes call the “direct data dividend” becomes interesting, like we have piloted and fine-tuned with many of our partners already.

The basic idea is simple. Instead of treating panelists purely as respondents who are paid for isolated tasks, panels can begin to treat participants as long-term contributors to a data ecosystem. Their behavioral signals, their opinions, and their participation collectively generate value that can support many different research and analytics applications. In that world, compensation begins to resemble something closer to a share of the value created by participation rather than a simple transaction for each completed questionnaire. To be clear, this is not about suddenly turning panelists into Wall Street traders of their own data. It is about recognizing that the economic relationship between panels and participants has evolved.

People are increasingly aware that their data has value. They see it in discussions about privacy regulation, platform economics, and digital rights. If the research industry can build transparent systems where participants understand what they contribute and how that contribution creates value, the relationship between panels and participants can become stronger rather than weaker.

From a purely operational perspective, such a shift could also address some of the most persistent challenges facing panel companies. One of the biggest frustrations in the industry has always been the presence of so-called “professional respondents.” These are individuals who participate in surveys primarily to maximize incentives rather than to contribute thoughtful responses. Over time this behavior erodes data quality and increases the complexity of fraud detection.

A model where panelists participate in a broader ecosystem of behavioral and attitudinal data — with transparent incentives tied to long-term engagement — changes that dynamic. Instead of optimizing for quick survey completions, participants have incentives aligned with sustained, high-quality participation. Panels begin to resemble communities rather than task marketplaces. At the same time, behavioral data opens new economic possibilities for panel operators themselves. The traditional panel model is built around episodic revenue streams. A client launches a study, recruits respondents, and pays for the completed data collection. Once the project ends, the revenue ends as well. But when panels incorporate behavioral signals into their infrastructure, the economic model becomes more continuous.

Behavioral datasets can support measurement products, audience analytics, product testing environments, advertising verification systems, and alternative data feeds. Each of these use cases can operate independently of traditional survey projects while still relying on the same underlying participant base.

This shift transforms the lifetime value of panelists.

Instead of generating value only when they answer a questionnaire, participants contribute to a broader intelligence ecosystem that operates continuously. That makes it much easier for panel operators to justify the investment required to recruit and maintain high-quality participant communities.

Another interesting consequence of this evolution is the role panels may play in verifying reality within the broader digital economy. If you speak to executives in advertising technology today, you will quickly hear about the industry’s “truth crisis.” The digital advertising ecosystem is enormous — global advertising spend is approaching a trillion dollars — yet much of it depends on signals that are difficult to verify. Bot traffic, fraudulent impressions, and opaque measurement practices have created persistent uncertainty about what actually reaches real people.

Panels, when built around verified human participants, offer something unique in this environment. They provide a direct connection between digital signals and real individuals. That connection could allow panels to function as an independent verification layer for parts of the digital economy that currently lack trustworthy measurement. Did an ad campaign actually reach real humans? How did those individuals respond? What behaviors changed as a result?

These questions extend far beyond traditional market research.

They touch the core infrastructure of how digital markets function.

If panels evolve into systems that combine behavioral observation with verified human participation, they may find themselves positioned at the intersection of research, advertising measurement, and data governance. Of course, for this vision to scale, the industry will need something it has historically struggled with: standardization. Behavioral data without common taxonomies and shared frameworks quickly becomes fragmented. Each platform describes behavior differently, making it difficult for buyers to combine datasets or compare insights across providers. We on our front, at Behavix, are naturally fixing and solving for this, making our data across iOS, Android, Windows, Mac, CTV, across web, in-app, e-commerce, media streaming, advertising, app usage/downloads etc., comparable and easily usable, through consistent taxonomies, which can be customized when need be.

To unlock the full economic potential of behavioral panels, the research industry, however, will also likely need to collaborate more actively on shared standards. Common definitions for behavioral attributes, interoperable data schemas, and privacy-safe mechanisms for data exchange could enable panels to participate in broader data marketplaces without sacrificing governance or participant trust. No single company should control these standards. But the industry as a whole has a strong incentive to build them. Without them, behavioral data risks remaining a collection of isolated silos. With them, panels could contribute to a much larger ecosystem of intelligence services that support media, commerce, finance, and technology companies around the world.

For panel operators, that prospect represents something more than a technical upgrade. It represents a shift in how the industry understands its own role. Panels have historically been seen as suppliers of respondents. In the emerging data economy, they may become operators of behavioral intelligence infrastructure. The difference is subtle but profound. Instead of selling access to people, panels facilitate access to structured insights derived from real human behavior.

Surveys remain an essential part of that system. They provide the attitudinal context that behavioral data alone cannot capture. But they become one layer in a broader architecture rather than the sole engine of value creation. This does not diminish the importance of research participants. On the contrary, it elevates their role. In fact, every week we turn around insights and projects, where surveys are cleverly combined and fused with behavioral data.

In any case, participants are no longer just completing surveys. They are contributing to a system that helps organizations understand how markets evolve, how technologies are adopted, and how consumers interact with the digital world. In that sense, the idea of panelists as “data shareholders” is not merely a metaphor. It reflects a deeper shift in how value is created and shared in the research ecosystem.

For the industry, this transformation will require thoughtful design, transparent governance, and ongoing dialogue with participants about how their data is used. But if done correctly, it offers a path toward a more sustainable economic model for panels. And sustainability is something the industry needs.

For years, market research has struggled with declining margins, commoditized survey pricing, and a perception that innovation was happening faster in adjacent fields like advertising technology and analytics. Behavioral panels offer an opportunity to change that narrative. By combining verified human participation, behavioral observation, and modern data infrastructure, the research industry can reposition itself as a core provider of trusted intelligence for the digital economy. From my perspective, that possibility is both exciting and overdue. Panels have always been about structured access to human reality. What is changing now is the scale and richness of the signals we can observe, and the number of ways those signals can create value.

If the industry embraces this shift thoughtfully, the future of panels may look less like a declining legacy business and more like a foundational layer of the global data economy. And in that future, the relationship between participants and the platforms they join may look less transactional and more collaborative. Not respondents and survey invitations. But contributors and ecosystems.

Or, if you like the more optimistic version: citizens of the data economy who finally get to share in the value they help create.