Geo-experiment
A geo-experiment uses geographic regions as test/control groups, running ads in some markets and pausing in others to measure incremental lift.
What does Geo-experiment actually mean?
Geo-experiments are the practical workhorse of incrementality testing. Instead of randomly excluding individual users, you split markets: ads run in some metro areas (test) and are paused in similar markets (control). After the test window, you compare aggregate revenue or conversion lift between matched test/control pairs.
The methodology requires market matching: pairing similar markets by population, demographics and historical conversion rates so any difference is attributable to advertising rather than baseline differences. Tools like Google's Geo-Lift, Meta's built-in geo-tests, and third-party platforms (Recast, Haus) automate the matching and statistical analysis.
Geo-experiments work better than user-level holdouts in 2026 because privacy changes (ATT, cookie deprecation) make user-level identification unreliable. Geo-level data is aggregate, privacy-safe, and harder to confound.
Example
A DTC brand runs a 4-week geo-experiment: Atlanta, Dallas, Phoenix get full Meta + TikTok ads. Houston, Charlotte, San Diego get zero. Conversion-rate lift between matched pairs is the incremental measurement.
When it matters
Geo-experiments are the practical way to measure incrementality without depending on user-level tracking. The standard incrementality method post-cookie-deprecation.
Related terms
Holdout test
A holdout test withholds ads from a randomly-selected control group, then compares performance vs. the test group to measure incremental lift.
Incrementality
Incrementality measures the additional revenue ads generate vs. what would have happened without them, usually measured via holdout tests or geo-experiments.
MMM
MMM is a top-down statistical method that estimates each marketing channel's contribution to revenue using time-series data. It's privacy-friendly and attribution-window-agnostic.
Attribution
Attribution determines which ad touchpoint receives credit for a conversion, using last-click, first-click, time-decay or data-driven models.
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