Glossary
Incrementality
Whether a marketing activity actually caused conversions that would not have happened without it. The question attribution reports cannot answer.
Also called: incremental lift, causal measurement
Attribution divides credit among the touchpoints a converter happened to encounter. Incrementality asks a harder question: would this person have bought anyway?
The distinction is not academic, because the two routinely disagree in a specific and expensive direction. Branded search is the classic case. It attributes beautifully — people search your name, click the ad, buy — and a large share of those people were going to reach you regardless. The report says the campaign works. Turning it off is how you find out whether it did.
Retargeting has the same shape. You are advertising to people who already visited, already added to cart, already decided. They convert at flattering rates and some meaningful portion needed no advertising at all.
How you actually measure it. You need a group that did not see the ads. Geographic holdouts, where some regions get the campaign and comparable ones do not. Audience holdouts, where a random slice is excluded. Or a clean on-off test with enough time either side to separate the effect from normal variation. Some platforms offer built-in lift studies, which are convenient and are marked by the party selling the advertising — worth knowing when you read the result.
Why nobody does it. It requires deliberately not advertising to people, which feels like burning money, and it produces a number smaller than the one in the platform dashboard. The awkwardness is the point: the gap between attributed and incremental is the actual finding.
Do not confuse with
Close enough to get mixed up, different enough that the mix-up costs something.
- Holdout test Deliberately withholding advertising from a comparable group so the difference in outcomes shows what the advertising actually caused. The method behind an incrementality claim.
- Last-click attribution Giving a conversion entirely to the final click before it. Still the most common model, and the one that most reliably misprices everything upstream.
- Modeled conversions Conversions an ad platform estimates statistically rather than observes directly, used to fill gaps where consent, tracking prevention or privacy limits block measurement.
- Statistical significance A statement about how likely a difference this large would be if there were no real effect. It is not a measure of how large, how important, or how certain the effect is.