User-Centric Behavioural Data is the Engine of a Real-Time, AI-Native Insights Economy Amidst the ResTech Evolution We are Witnessing • Behavix Updates

1) Market research’s missing “adtech moment”

More than a decade ago, adtech standardized on APIs, liquid event streams, and machine-speed optimization. Media buyers learned to plan at breakfast and optimize by lunch. Meanwhile, still today a large slice of market research is still shipped as custom projects, executed in silos, and delivered weeks or months after the moment that mattered. I made a point already when at Arbitron, back in 2011, that so many things should, and shall be automated, and static reports should be avoided, and important information should flow in a more liquid way, not through static and infrequent “drops”. That’s not a workflow gap—it’s an existential problem for a market research industry, increasingly expected by clients to guide their decisions at operating-system speed, to no avail.

At Behavix, we’ve staked our strategy on a different path: ResTech. This means instrumenting the world with opt-in, cross-device, user-centric measurement; turning raw signals into analyst-ready feeds; and wrapping those feeds with AI that explains, predicts, and prescribes—so stakeholders can move from event to decision in seconds, not in sprints. That philosophy underpins our platform and partnerships: equipping panels and audience owners with a free SDK (and browser extension) for passive measurement, then delivering clean, enriched, productized data to clients across research, advertising, and finance.

But one conviction sits at the center of our system: panels remain indispensable—not as a relic from the 1990s, but as the only durable, auditable lens on people in a privacy-first, platform-controlled internet.

2) Why the “census-only” dream keeps breaking on reality

The industry loves the idea of replacing panels with pure server-side or publisher integrations, aka census data—wire every network, pull every log, join everything, and voilà: a perfect census of human attention. Except three hard facts keep getting in the way:

First, walled gardens set the rules of visibility. Clean rooms such as Google’s Ads Data Hub are deliberately designed to withhold user-level exports and to enforce privacy thresholds—for most queries, results won’t return unless ≥50 users qualify; click/conversion-only queries can drop as low as 10, but the principle stands. Those constraints are healthy for privacy—and structural limits for “see everything” measurement outside a platform’s walls. And for most walled gardens, there is no access to census data within those in the first place… Still they conquer a lion’s share of total consumer time spent online!

Second, the privacy architecture fragments identity on purpose. Apple’s App Tracking Transparency and Android’s Privacy Sandbox remove or reduce cross-party identifiers and push measurement into aggregated, on-device, thresholded patterns. Again: good for people, but it means cross-platform person-level truth cannot be reconstructed by “just integrating more pipes.”

Third, contracts and law resist full commingling. Even if you could plug into everyone, reconciling those datasets at the person level runs into purpose limitations, consent scope, competitive controls, and anti-re-identification rules. The result: any census-only worldview inevitably sees slices—not the whole.

Taken together, these constraints explain why the most credible currencies have moved toward hybrid—explicitly fusing panel truth with big data scale. In the US, Nielsen’s Big Data + Panel won Media Rating Council (MRC) accreditation this year and will replace panel-only ratings by late 2025; in the UK, BARB Panel Plus is expanding big-data integration to reduce zero-ratings and improve consistency. Hybrid isn’t a hedge; it’s the new standard.

3) The five non-substitutable jobs of a modern panel

A modern panel isn’t a crutch for missing logs; it’s a purpose-built instrument that does jobs big data cannot credibly do on its own:

A. Personification (the “who,” not just the “what”). Device logs and account analytics rarely resolve people. Panels resolve co-viewing, household composition, multi-user devices, and attention/engagement constructs that platforms either can’t or won’t disclose. This is why panel-anchored personification sits at the core of contemporary currency work.

B. Calibration & bias correction. Set-top box logs, ACR streams, and server analytics all have structural skews. Panels quantify those skews and re-weight the big data, reducing zero-ratings and stabilizing estimates—exactly the benefits BARB and Nielsen are pursuing at currency scale.

C. Coverage of the “unknown unknowns.” Panels see across platforms, including multi-tasking and cross-journeys (e.g., a user comparing two ride-hailing apps mid-session). A single publisher’s server can’t see that.

D. A privacy-durable truth set. As clean rooms and APIs limit granularity, independent, consented panels remain the auditable reference to validate and explain fused systems—reinforced by CIMM’s 2024 work on the future role of panels.

E. Rare-event and small-cohort visibility. Platform UIs and clean rooms enforce floors on cohort sizes. Panels recover visibility for new creatives, niche demos, local dayparts, or small geos that fall below aggregation thresholds.

4) Clean rooms and privacy APIs are progress—but they aren’t a universal window into people

To be clear: Behavix embraces clean rooms and privacy APIs. We use them. We build for them. But they’re not a panacea:

Clean rooms (e.g., ADH) are ideal for secure joins and outcome analysis, yet they’re thresholded and typically scoped to a single ecosystem. Cross-garden deduplication is still piecemeal, and exports are aggregate, not user-level. Those are features, not bugs.
Privacy Sandbox’s Attribution Reporting (on web and Android) measures conversions with limited data sharing, noise, and aggregation—powerful ideas that, by design, make person-level tracking and cross-party joins hard.
Server-side analytics restore some visibility for publishers, but they don’t dissolve purpose limits, consent scope, or identity fragmentation. In short: you get secure slices of truth—valuable—but not the whole.

Conclusion: the only way to produce a holistic view without violating the rules of the modern internet is to anchor your system in panels and fuse everything else to them.

5) What Behavix brings to the hybrid table

Our operating model is intentionally two-sided. Instead of building yet another standalone panel, we equip existing panels, apps, and publishers with our lightweight, free SDK (and browser extension) so they can become consented, cross-device measurement assets. On the demand side, we deliver clean, enriched, analyst-ready feeds for research, media, and financial use cases—from app sessions and clickstream to ad exposure surfaces, product journeys, and AI-derived consumer attributes.

Speed: hourly/daily cadences so traders and marketers see reach shifts, session winners, overlap, and retention as real-world events unfold (an outage, a release, a price change).
Depth: clickstream + in-app session detail; IAB and proprietary content taxonomies; ownership/ticker linkages; and AI enrichment that converts behavior into thousands of interpretable attributes, including brand affinities and purchase intent.
Privacy: pseudonymous IDs, explicit opt-in, retention discipline, standardized erasure flows—and native compatibility with clean-room activation where permitted.

This is ResTech as operating system: instrument once, analyze continuously, activate ethically.

6) From panels to synthetic panels—and why the timing is perfect

The real breakthrough is what happens when you combine panel truth, big-data coverage, and explainable AI:

Calibrate with people, not dashboards. If you train MMM, brand-lift, or propensity models only on thresholded platform outputs, you’ll reproduce platform bias. Calibrating with panel-anchored truth improves stability—precisely what current currency initiatives are reporting. 2. Fuse safely, then explain. Bring publisher/ACR/server sources into clean rooms; correct them with panel-based re-weights; publish deduped reach/frequency and journey motifs. 3. Generate synthetic populations. Using modern generative and simulation techniques, construct daily-refreshable synthetic panels that reflect the market’s structure but are anchored to audited panel behavior and updated with privacy-scoped big data. This is now moving from theory to practice across insight teams and vendors—paired with clear governance for bias, leakage, and drift. 4. Activate ethically. Link the learnings back to addressable media via hashed IDs or clean-room joins—not data leakage—so research can move markets without compromising privacy.

The result is a world where you can run consumer-journey modeling, brand health and lift, competitive intelligence, ad tracking, even audience ratings on synthetic universes that are continuously refreshed—and then connect those insights to the real world through privacy-preserving activation paths.

7) A practical blueprint: from projects to products, from decks to decisions

For research organizations (big and small), the operating model shifts along five lines:

From projects to products. Replace one-offs with always-on data products and reusable components (pipelines, taxonomies, model registries).
From analyst armies to AI co-pilots. Analysts don’t disappear; they move up the stack—curating prompts, governing models, setting quality bars—while decision-makers self-serve prompted workflows.
From monoliths to modular ecosystems. The future favors interchangeable modules for collection, identity, enrichment, QA, and delivery. Legacy codebases aren’t just expensive; they’re incompatible with seconds-to-signal expectations.
From black-box dashboards to auditable models. If you can’t trace inputs, re-weights, and uncertainty, you can’t defend the number—to clients, auditors, or regulators.
From “reporting” to “activation.” Clean rooms transform research into operational levers—budget shifts, creative rotation, retail media bets—because the outputs are already wired for action.

8) What this means for large research groups—and why some will win

Some argue that modularization will make the large research groups obsolete. I disagree—if they play their strengths:

Method IP is training data. Decades of validated questionnaires, lift frameworks, equity models, and brand tracks are priceless teaching sets for guardrailed AI.
Access to humans remains strategic. Panels, communities, and field ops are scarce assets—central to any hybrid system that hopes to be explainable.
Reputation still matters. In regulated categories, buyers prefer auditable methods and known stewards.

But the bar is higher: groups that don’t refactor stacks, modularize services, and commit to real time will lose to ecosystems shipping like software companies. The technical debt that once felt manageable becomes, in a seconds-to-signal world, strategy debt.

9) Where the industry is clearly heading (2025–2028)

Hybrid becomes table stakes. Expect more MRC-recognized, panel-anchored products, which use hybrid models of both panels and (some) census data
“Walled compounds,” not just walled gardens. Platforms will broaden their multi-party clean rooms—good progress, but still bounded by thresholds and policies, increasing the premium on independent panel truth to arbitrate overlap and deduplicate audiences.
Privacy APIs reshape measurement. The Attribution Reporting family (web + Android) will expand; it’s great for privacy and pushes measurement toward aggregate, noisy, delay-tolerant paradigms—again elevating panels for person-level calibration.
Synthetic panels go mainstream—with governance. As modeling practice matures, expect “digital twin”-like universes that are explainable and auditable, not black-box alchemy.

10) Behavix: putting the blueprint to work

Our role in this future is straightforward:

Equip supply with no-/low-code collection (free SDK/extension), so panels and audience owners become measurement-ready without heavy lifts.
Deliver deep, real-time feeds—sessions, share, overlap, retention, funnel lift, ad exposure surfaces—that let buyers react to reality now.
Use AI to enrich and explain—thousands of behavior-derived attributes per person, plus monthly narratives that productize “who is this consumer?” in plain language.
Operate natively with clean rooms and privacy APIs—so research can be activated ethically across media, commerce, and product.

Panels aren’t yesterday’s technology. In a world engineered to limit raw visibility, panels are the independent, consented lens that brings the picture back into focus. Pair them with modern ResTech pipelines and auditable AI, and you don’t just measure markets—you move them.

If you’re building toward that future and want a partner already operating this way, let’s talk.

Behavix.io

Co-Founder & CEO of Behavix

Hannu Verkasalo