First-party data clean room implementation and identity resolution: Amazon Marketing Cloud, Google Ads Data Hub, and privacy-safe audience matching.
Access granted, value unrealized
Plenty of teams have clean room access and almost nothing to show for it. The reasons are consistent: first-party data was never prepared for matching, so hash formats and email normalization silently destroy match rates. Aggregation thresholds return empty results and the query looks broken rather than blocked. And the SQL dialect is unfamiliar enough that analysis stops after the first template. The access is not the hard part.
What we implement
- First-party data preparation and hashing to each clean room's exact specification
- Identity resolution across email, phone, order ID, and device, with a match-rate baseline you can track
- Amazon Marketing Cloud instance setup, audience uploads, and custom query development
- Google Ads Data Hub queries built to pass aggregation and privacy checks the first time
- Automated export of clean room outputs into your warehouse so results join your other reporting
- Documented query library your analysts can adapt without starting over
How we work
Audit first-party data quality and estimate realistic match rates before promising outcomes
Normalize and hash identifiers, then validate match rates against a known holdout
Build the two or three questions with the clearest commercial value first
Automate the recurring queries and pipe results into the warehouse
Typical stack
Frequently asked questions
Cross-touchpoint questions the platform UI will not answer: true new-to-brand rates, overlap and incremental reach between campaign types, path-to-purchase sequences, and how ad exposure relates to repeat purchase in your own customer data. It is the only privacy-safe way to join your customer records to platform-side exposure data.
Yes, and it is worth checking before you invest. Clean rooms enforce aggregation thresholds, so small audiences return nothing at all. As a rough guide, meaningful analysis needs tens of thousands of matched records. We size this in the audit and will tell you if you are below the useful floor.
No. You upload hashed identifiers, the join happens inside the clean room, and only aggregated results come out. Neither party can read the other's raw rows. That is the entire design premise, and it is why aggregation thresholds exist.
A CDP unifies identity inside your own estate for activation and personalization. A clean room is a controlled environment for joining your data to someone else's for measurement. They are complementary, and a well-modeled CDP or warehouse makes clean room match rates substantially better.
Go deeper
Check your clean room readiness
Tell us what first-party data you hold and which clean rooms you have access to, and we will assess match potential before any build.
Start a project