Data-driven attribution

Data-driven attribution is a model that distributes conversion value between touchpoints based on actual patterns in the data rather than a fixed rule. The algorithm compares converting and non-converting paths to estimate each touchpoint's real contribution. The model requires a certain volume of data to produce reliable results.

With data-driven attribution, an advertiser can see that a generic keyword early in the journey contributes more to sales than last-click would otherwise suggest.