Data-driven attribution (DDA)

Rule-based models give credit by position: all to the last click, all to the first, evenly across every touch, or weighted toward the touches closest to the sale. Data-driven attribution learns weights from the paths the platform can observe, comparing journeys that converted with journeys that did not.

The word observe carries the caveat. A platform DDA model only sees the touches that platform recorded. Google DDA sees Google touches, and it cannot credit a Meta ad it never saw. So DDA inside one platform is still that platform grading its own contribution, with better arithmetic than last click.

DDA is also not incrementality. It estimates how credit should be shared among touches that happened, not whether the sale would have happened without any of them. For that question you need a holdout or a geo test.

The practical use is comparative. Run several models on the same first-party data and look at where they disagree. Where every model agrees on a channel, confidence is earned. Where the credit swings widely depending on the model, the allocation is a matter of opinion, and that is exactly where a test pays for itself.