By Jay Given, VP, Retail & Brand Strategy, Affinity Solutions
Welcome back to our Outcome Collective series on Retail Media. In โฏpart one, we established the importance of retail media measurement and addressed common industry challenges like a lack of universal definitions and accounting for incremental sales lift.ย
Part two built on that foundation by examining how product types, sales channels, and marketing strategies influence the effectiveness of ad campaign measurement.ย
In this final installment, we focus on:ย
- Test-and-Control Methodologies: We’ll explore this core approach to incremental measurement and its role in proving the real-world effectiveness of advertising campaigns.ย
- The Right Control Group: We’ll examine the advantages and disadvantages of using a synthetic control group (built from data) versus an observed control group (a real-world comparison group).ย
- Standardizing Measurement: We’ll discuss how crucial it is to define and measure ad exposure consistently across all media platforms, ensuring your measurement model is both agile and effective.ย
Additionally, we highlight the evolving role of clean rooms and AI in overcoming signal loss as well as enhancing the accuracy and speed of lift studies. By continuously refining these methodologies, retailers and brands can collaborate towards mutual outcomes, growing their revenue together.ย ย
The Importance of Test-and-Control Methodologies for Media Measurementย
Test-and-control methodologies are the backbone of media lift measurement. They allow advertisers and publishers to isolate the true impact of campaigns by comparing exposed groups to control groups that either were unexposed to media campaigns or whose behavior was modeled to account for media exposure. Effectively implementing these methodologies to align with advertisersโ goals requires thoughtful planning and strategic execution.ย ย
Retail Media is unique because, unlike many traditional advertising ecosystems, the advertiser (or supplier) and the publisher (typically the retailer) are directly aligned in their goals. Both parties benefit from driving incremental revenue, with advertisers aiming to sell more and retailers profiting from both the sale and the media investment. This shared incentive creates a collaborative environment where measuring campaign impact becomes a mutual priority.ย
The Role of Synthetic vs. Observed Control Groupsย
To support this shared focus on outcomes, advertisers and retailers must rely on rigorous frameworks, particularly test-and-control methodologies. A core decision point in test-and-control design is whether to use observed or synthetic control groups.ย ย
Synthetic Control Groups: Pros and Consย
Synthetic groups are generated using algorithmic data to simulate what would have happened without exposure to the campaign. Synthetic controls, built from modeled shopper cohorts, offer scalability and faster testing, making them useful for large-scale campaigns. However, they rely on accurate modeling assumptions and robust data inputs and may introduce biases if not carefully calibrated.ย
While powerful, synthetic controls are best used as part of a hybrid approach that balances precision with operational agility.ย
Observed Control Groups: Pros and Consย
Observed groups use real-world data from consumers who were not exposed to the ad, offering a high level of precision by grounding analysis in actual shopper behavior.ย However, this approach requires greater data access and resources. Observed controls are ideal for advertisers who prioritize accuracy and are willing to invest in granular analyses.ย ย
This method can be difficult to scale across all channels and campaigns as it is resource-intensive and can take longer to analyze.ย ย
Standardization is Criticalย
Equally critical is the standardization of exposure definitions across media types. Without consistent definitions of what it means to be โexposed,โ it becomes nearly impossible to compare outcomes across channels or aggregate results into a unified performance view. By aligning on common definitions, retailers and advertisers can more confidently evaluate true incrementality and avoid inflating results.ย
For a more detailed discussion on standardized definitions, check out part one of this series.ย ย
The Importance of Clean Roomsย
As the industry navigates signal loss caused by privacy shifts and data restrictions, clean rooms have become a secure way to match datasets without compromising compliance. Retail Media Networks should consider sharing their exposure log files and/or their purchase data via cleanrooms in 2026. Clean rooms allow advertisers and retailers to collaborate with greater transparency while still protecting sensitive information. When paired with AI-driven modeling, they can enhance the accuracy, speed, and scalability of lift studies, turning what was once a static process into a dynamic, always-on capability.ย
To learn more about how Affinity Solutions is helping clients measure campaign effectiveness with precision to drive optimal outcomes, check out this interview with Kalyan Lanka, our Chief Product Officer.ย ย ย
Conclusionย
Ultimately, the future of measurement lies in building flexible, repeatable methodologies that evolve with changing market conditions and regulatory realities. The future of measurement will also be defined by the brands themselves. By collaborating on test-and-control strategies, clean rooms, and applying AI transparently and effectively, retailers and brands can create a measurement model that drives accountability, fosters collaboration, and accelerates shared revenue growth.ย
