Definition
A geo experiment measures lift by varying spend across regions and comparing outcomes against controls.
How to use it
- Use geo tests when user-level holdouts aren't feasible (e.g., offline or privacy constraints).
- Ensure regions are comparable and avoid cross-region spillover.
Common mistakes
- Using too few regions (low power) and over-interpreting noise.
- Changing major campaigns mid-test (confounds).
Why this matters
This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.
Practical checklist
- Write a 1-line definition for "Geo Experiment" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Use a calculator that references this term (e.g., Incrementality Lift Calculator) to sanity-check assumptions.
- Read the related guide (e.g., Incrementality: how to tell if ads are actually driving growth) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- Incrementality Lift Calculator: Estimate incremental conversions, incremental ROAS, and incremental profit from a holdout test.
Guides
- Incrementality: how to tell if ads are actually driving growth: Platform-reported ROAS can overstate impact. Learn what incrementality means, when it matters, and practical ways to test it.
- Incrementality lift: how to compute incremental ROAS from holdouts: Turn an exposed vs holdout test into incremental conversions, incremental ROAS, and incremental profit for decision-making.
- Paid ads measurement hub: ROAS, MER, marginal ROAS, and incrementality: A practical hub for paid ads measurement: connect ROAS to profit, use MER for top-down truth, watch marginal ROAS for scale, and validate incrementality with holdouts.
- Attribution vs incrementality: what to trust, when, and how to test: A practical guide to attribution vs incrementality: common attribution models, window pitfalls, how MER/marginal ROAS fit in, and how to run holdout/geo tests.