Clean Rooms and Attribution
Retail media promises a direct link between ad spend and sales, but each network reports its own results in its own way. Clean rooms and proper attribution show what your retail media is actually delivering, across networks and against sales that would have happened anyway.
We set up clean rooms such as Amazon Marketing Cloud, match your CRM data privacy-safely, and run incrementality and sales lift studies. Results come together in unified dashboards across every network, so budget decisions rest on incremental sales rather than each platform's own claims.
Proof of what retail media actually delivers.
Who Needs Clean Room Measurement
Brands and agencies spending across several retail media networks usually face the same problem: every network reports its own ROAS, its own attribution window and its own definition of a new customer. Adding those reports together does not give a true picture. Clean rooms attribution is for teams that want one honest answer to the question of what advertising actually added, before the next budget round.
It also suits international teams working under strict privacy rules in the US, UK, Europe and Australia. Customer lists cannot simply be uploaded and shared. A clean room lets you analyze your first-party data next to a retailer's purchase data without either side exposing raw records, which keeps legal and data protection teams comfortable while the marketing team still gets usable answers.
What the Work Covers in Practice
We configure the clean room environment, starting with platforms such as Amazon Marketing Cloud, and agree with your team which questions it should answer. Typical queries cover path to purchase, overlap between channels, audience building from exposure, and new-to-brand behavior. We write and maintain those queries, document the logic, and make sure the outputs are repeatable rather than a one-off analysis nobody can reproduce.
CRM matching is handled inside the retailer's rules, so loyalty members, subscribers and past buyers can be compared with exposure data. We also design incrementality tests, such as holdout groups and geographic splits, where the network supports them. Each test has a written hypothesis and a stated minimum sample before it starts, so the result can be trusted by finance as well as marketing.
Reporting Beyond Platform Dashboards
Findings are collected in one set of dashboards that places every network on the same footing. Reporting separates reported sales from incremental sales, shows new-to-brand customers alongside cost per new buyer, and flags where an in-store or offline lift view is available. Definitions and attribution windows are written next to each metric, so a number is never compared with another that was counted differently.
The results feed back into planning. If a channel looks strong on platform reporting but weak on incrementality, you see it clearly and can move spend with evidence. Pair this work with retail media strategy and planning, and read our guide to retail media measurement and clean rooms for the wider method.
What we need from you: the networks in scope, first-party data you may use, the clean room access you already have and the budget decisions the results must inform. Problems we commonly catch: attribution windows compared as if equal, samples too small to read and holdouts contaminated by other campaigns.
Next step: list the networks and the three budget decisions this should inform. We will return the questions a clean room can answer and the test design for each.
How an engagement runs
Measurement Questions
We agree the decisions the data must support, list the networks in scope, and note each platform's attribution window, definitions and reporting gaps.
Clean Room Setup
We configure the clean room, confirm data access and privacy rules with the retailer, and prepare your first-party data for matching.
Tests and Analysis
We run documented queries and incrementality tests, check results for bias or thin samples, and record the method so it can be repeated.
Dashboards and Review
Results go into shared dashboards, and we hold a regular review to turn findings into budget, audience and creative changes.
Frequently asked questions
What is a data clean room?
A secure environment where a retailer's data and your data can be analyzed together without either side seeing the other's raw customer records.
Why does incrementality matter?
Platform reports can credit ads for sales that would have happened anyway. Incrementality studies measure the sales your ads actually added.
Is CRM matching privacy-safe?
Yes. Matching happens inside the clean room under the retailer's privacy rules, so personal data isn't exposed.
What do the dashboards show?
One view across every network: ROAS and ACoS, new-to-brand sales, share of voice, incremental sales and in-store lift where available.
Do we need a large data team to use a clean room?
No. We write and maintain the queries, handle access and documentation, and present results in plain language. Your team supplies the business questions and first-party data.
Can you measure networks that do not offer a clean room?
Often yes, through alternative designs such as holdout tests, geographic comparisons or retailer-provided sales reports. The method differs by network, so we state clearly how confident we are in each result and explain why.
How is incrementality different from attributed sales?
Attributed sales count purchases that followed an ad, including ones that would have happened anyway. Incrementality compares exposed shoppers with a similar unexposed group, so it isolates the extra sales the advertising actually caused.