Retail media has multiplied the number of platforms a brand or agency needs to report on. Amazon, Walmart Connect, Instacart Ads, and Criteo each have their own console, their own API, and often their own definition of basic metrics like "spend" or "conversion." Teams end up building the same report four times a week, once per platform, then manually stitching totals together in a spreadsheet.
The core problem: every platform models data differently
The technical challenge isn't just pulling data from four APIs; it's reconciling them into one consistent model. Attribution windows differ. Some platforms report same-day conversions, others report 7-day or 14-day windows. Campaign structures don't map one-to-one. Without a normalization layer, a "unified" dashboard is just four dashboards pasted next to each other, giving a false sense of comparability.
What a real unified reporting layer requires
- A canonical data model: campaign, spend, impressions, and conversions defined once, with each platform's data mapped into it
- Explicit attribution-window handling: so cross-platform ROAS comparisons are actually apples to apples
- Scheduled multi-source ETL: pulling from each platform's API on its own cadence and rate limits
- A single BI layer: dashboards built once against the unified model, not once per platform
- Version-controlled metric definitions: so "ROAS" means the same thing across every report a client sees
That's what we built for BTR Media: ETL pipelines for Amazon, Walmart and Instacart feeding one dashboard, with 50+ campaigns in one view and 30% less manual optimization work. The unified data model became the foundation for both the reporting layer and the automation workflows for bids and budgets.
“Campaign and performance data is collected from multiple retail media platforms and standardized into a unified data model, ensuring consistency and accuracy across all reporting layers.”
From the BTR Media case study
Start with the metric definitions, not the dashboard
It's tempting to start a unification project by designing the dashboard first. The harder, more important work is agreeing on metric definitions before any data is pulled: what counts as a conversion, what attribution window is standard, how blended ROAS is calculated across platforms with different fee structures. Get that wrong and every dashboard built on top of it inherits the same inconsistency. Our retail media data page lists the networks we connect.
Frequently asked questions
Why don't retail media platforms report the same numbers for the same campaign?
Differences usually come down to attribution windows (same-day vs. 7-day vs. 14-day) and how each platform counts a "conversion." These aren't bugs; they're different measurement methodologies, which is exactly why a normalization layer is needed to compare them fairly.
Do I need a data warehouse, or can a BI tool connect directly to each platform?
Direct BI connections work for single-platform reporting, but cross-platform comparison requires a warehouse layer in between to hold the normalized, unified model. Without it, you're just viewing separate datasets side by side.
How many retail media platforms justify building a unified pipeline?
Once you're reporting on three or more platforms regularly, the manual reconciliation time typically exceeds the cost of building a proper pipeline, making it the point where unification pays for itself fastest.