Data-driven attribution assigns credit to touchpoints using models based on observed data rather than a fixed rule applied equally to every journey.
How it works
The goal is to estimate the relative contribution of interactions within available journeys. The output is still an attribution model and does not automatically equal causal measurement or an incrementality test.
Practical example
Two channels that frequently appear together in journeys may receive different shares of credit based on how their presence changes observed conversion probability.
Why it matters
It can provide a less arbitrary view than simple models such as last click, especially in customer journeys with many touchpoints.
What to watch
A sophisticated model does not eliminate tracking, consent, cross-device and unobserved-channel limitations. Input quality always constrains attribution quality.