Retail media is the fastest-growing corner of advertising, and almost no one can measure it honestly

Retail media has a structural problem that its growth rate keeps outrunning. The network sells the advertising, and the network also reports how well it performed. That is not necessarily dishonest, and it does not require anyone to behave badly. It only requires the default measurement to be attribution, which credits the network whenever an exposed household later buys, whether or not the advertising had anything to do with it. Retail media sits closest to the purchase, so it captures the most of that already-arriving demand, and reports it as return.

Suneel Dasika's response was to build the control group into the network itself.

The approach is structurally simple and organisationally difficult. A randomised four per cent slice of the addressable first-party audience is carved out and made permanent, assigned at customer level by hashed identifier, re-randomised only once a year, and excluded upstream in the audience-build layer so that no campaign can target into it even by accident.

The consequence is that every campaign on the network inherits a valid control group automatically. No advertiser has to be persuaded to give up reach, no bespoke study has to be designed, and nobody has to justify sacrificing revenue on a particular campaign to learn something about it. Incrementality stops being a project and becomes a property of the infrastructure.

What that changed is best expressed as coverage. Lift measurement had previously reached roughly five per cent of eligible campaigns, limited to a handful of managed accounts each year by the design and approval cycle each study required. With the holdout standing permanently, coverage became close to universal, at a fully amortised cost of well under one per cent of network revenue, absorbed centrally as what Dasika describes as a measurement tax rather than billed to advertisers.

The pilot ran with five advertisers chosen for spend volume, category diversity and enough weekly conversion density to reach significance inside a four-week flight. Each received a weekly exposed-versus-withheld read on incremental sales, new-to-brand rate and lift percentage, presented alongside their existing attributed return rather than instead of it.

In all five cases, measured lift came in materially below attributed return.

"Which was the point," Dasika says.

The predictable expectation is that advertisers shown a smaller number spend less. All five expanded their investment. Two reallocated budget toward the tactics that survived the comparison, and the holdout became the default reporting layer for those accounts within two quarters. He has seen the pattern before, in an earlier productised incrementality effort that reached eighty per cent adoption among top-tier advertisers and lifted their spend by twenty per cent.

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The reason is worth sitting with, because it inverts the usual assumption about measurement transparency. An advertiser who cannot tell which part of a reported return is real has to discount the whole number. An advertiser who can separate genuine lift from captured demand can confidently invest behind the part that works. Precision about what is not working turns out to be what unlocks budget for what is.

Presenting the honest number alongside the attributed one, rather than replacing it, also reframed the conversation from defending a figure to calibrating it.

Where Dasika's account becomes genuinely unusual is in what he volunteers about the method's limits, at length and without being asked.

The control group is randomised, but it is not clean. Withheld households still encounter the brand through organic search, store visits, competitor retail media and the advertiser's

own upper-funnel advertising, so the comparison estimates lift against a control contaminated by everything happening outside the network's walls. Match rates, identity resolution and cross-device fragmentation introduce further slippage. At small spend levels, the confidence interval is wide enough to swallow the effect entirely.

His team chose to publish that uncertainty rather than smooth it: stating the bounds, suppressing underpowered reads instead of reporting them, and framing every estimate as a conservative floor on true incremental value rather than a point estimate.

"Advertisers trusted a defensible range more than they trusted a precise number nobody could defend," he says. His view is that the trust, rather than the methodology, is what actually unlocked the incremental budget.

He is equally direct about what makes this hard to operate, and the failure modes he lists are mostly organisational.

Withholding four per cent of addressable reach shows up immediately as lost impressions and reduced delivery, which caps what a sales team can promise. That resistance is structural, so the holdout has to be funded centrally as infrastructure and can never appear as a line item an account team is able to opt out of.

The exclusion also has to hold across every path an activation can take. Offsite platforms, third-party demand partners, agency-managed buys and clean-room activations each represent a route by which a withheld household can be reached anyway, and every new integration is a fresh place the holdout can break silently.

Identity is the quiet risk. A customer-level assignment is only as durable as the identity graph beneath it, and re-hashing, identifier churn and imperfect matching erode the group over time. Dasika's phrasing is the sharpest warning in his account: a control that quietly degrades produces confidently wrong lift numbers.

And at four per cent, most campaigns will never clear significance at all, which requires documented minimum thresholds for spend, flight length and conversions, plus the discipline to tell a smaller advertiser that they do not get a number yet.

That last one is the real test of whether a measurement system is honest. Every incentive points toward producing a number, because a number is what the advertiser is asking for. Declining to supply one is harder than supplying a bad one, and considerably less popular.