Multi-Touch Attribution
An analytics model that distributes conversion credit across multiple touchpoints in a customer journey, rather than giving all credit to the first or last interaction.
Common models
Multi-touch attribution comes in several variants: linear (equal credit to all touchpoints), time-decay (more credit to recent touchpoints), position-based (40% first, 40% last, 20% middle), and data-driven (ML-determined weights). Each has trade-offs, but all share a fundamental requirement: visibility into the complete journey.
The data completeness problem
Multi-touch attribution is only as accurate as the touchpoint data feeding it. When cookie-based analytics miss 87% of visitor interactions due to consent rejection, ad blockers, and browser restrictions, the model distributes credit across a fragment of the real journey.
This systematically undervalues top-of-funnel channels (organic, social, display) because first touches are most likely to be lost when cookies are not yet active. Revenue attribution on complete data eliminates this bias.