Lift Study

Definition

A lift study measures incremental impact by comparing exposed vs control groups (or regions) under a test design, often run by ad platforms.

Example

A platform reports +12% incremental conversions at 90% confidence in a lift study.

How to use it

• Define primary metric (incremental conversions, revenue) before running.
• Use lift alongside MER and marginal ROAS for budget decisions.
• Check confidence intervals before making large budget shifts.
• Validate experiment design (randomization, holdout %) before launch.
• Use the same attribution window in pre and post periods for a clean read.
• Document test settings so future studies are comparable.

Common mistakes

• Reading lift as causal without proper randomization.
• Ignoring small sample sizes that make results unstable.
• Mixing multiple objectives (clicks and purchases) in the same test.
• Assuming lift is durable without re-testing after major changes.
• Reallocating spend before the test is complete.

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 "Lift Study" 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., Attribution vs incrementality: what to trust, when, and how to test) for context and common pitfalls.

Where to use this on MetricKit