Albertsons Media Collective Launches In-Store Incrementality Measurement With Matched-Market Framework
Albertsons is the first major retailer to offer causal incrementality measurement for in-store digital media. A Mondelez beta campaign delivered a $2.41 iROAS and 14% sales lift. The network plans to expand to 800 additional stores in 2026.
Albertsons Media Collective launched an incrementality measurement solution for its in-store digital media network on January 6, making it the first major retail media network to offer causal measurement of in-store advertising impact. The announcement, timed to CES 2026, addresses what has been retail media's most glaring measurement gap: proving that in-store screens actually drive incremental sales.
How It Works
The solution uses a matched-market framework that compares sales in test stores running in-store campaigns against a rigorously selected set of control stores with no media exposure. According to Albertsons' announcement, the matching algorithm leverages nearly 60 variables at the store level — including demographics, sales patterns, store format, and geography — to reduce bias and noise in the comparison.
By estimating the counterfactual (what would have happened without advertising), the model delivers a statistically validated measure of true lift. As VP of media and measurement Liz Roche put it: "For too long, in-store media has been measured with tools that confuse correlation for impact."
This is a meaningful distinction from how most retail media networks report performance. Standard ROAS figures from retail media platforms typically include purchases that would have happened anyway — a shopper who was already going to buy Sargento cheese sees a screen ad for Sargento cheese and buys it. That purchase gets attributed to the ad. Matched-market incrementality testing strips out that baseline to isolate the actual causal effect.
The Mondelez Proof Point
The first public results come from a beta campaign promoting Sargento Cheese Bakes across 116 Albertsons banner stores. The results were strong:
These numbers matter because they are incremental, not total. A $2.41 iROAS is a fundamentally different claim than a $5 or $10 ROAS that includes baseline sales — and it is a much more defensible number to bring to a budget conversation.
Scaling the Network
Albertsons plans to expand its in-store screen network by approximately 800 additional stores in 2026, with scaled coverage across 10 divisions. The network has already attracted over 50 advertising partners, and the measurement framework is designed to work across both existing and new screen placements, including deli and pharmacy locations.
The technology infrastructure is provided by Stratacache, which supplies the digital signage platform and the ad tech layer needed to operationalize measurement at scale.
Why This Matters for Retail Media
In-store retail media is one of the fastest-growing segments in commerce media, but it has lagged far behind digital formats in measurement sophistication. The IAB released its Framework for Maturing In-Store Media Measurement in December 2025, acknowledging that the in-store environment — no clicks, no cookies, no pixel fires — requires fundamentally different measurement approaches.
Albertsons' solution aligns with the incrementality guidelines that IAB and IAB Europe published in 2025, which outlined matched-market experiments as one of four recommended methodologies for commerce media. But Albertsons is the first to operationalize this at scale for in-store specifically.
The competitive implications are clear. Kroger, Walmart, and other major grocery retailers with growing in-store media networks will face pressure to offer comparable measurement. Advertisers who have been spending on in-store screens based on impression counts and correlation-based ROAS now have a benchmark for what causal measurement looks like — and they will start asking other networks to match it.
Sources & References
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- [4]Albertsons Media Collective debuts in-store incrementality measurement— Retail Customer Experience
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