Attribution
Multi-Touch Attribution
An attribution model that distributes conversion credit across multiple touchpoints in the customer journey rather than giving all credit to a single interaction. Common models include linear (equal credit to all touches), time-decay (more credit to recent touches), position-based (40% to first and last touch, 20% split across middle), and data-driven (algorithmically weighted). MTA is better than last-click but still relies on trackable digital touchpoints, meaning it misses offline influence, word-of-mouth, and impressions that don't result in clicks.
Why it matters
It is the model most teams believe they are using and the one least likely to be measuring what they think. MTA sees only the touchpoints it can observe, and post-ATT that is a shrinking and non-random subset.
In practice
Read the coverage before the credit. If a third of conversions have a single observed touch, the model is not distributing credit across a journey, it is guessing.
Related terms