Multi-touch attribution (MTA) is the family of attribution models that apportion conversion credit across multiple marketing touchpoints in a customer's journey. Touchpoints might include a first paid-social impression, a later organic visit, an email click, a branded search, and a sales demo. Credit is assigned either through fixed rules (linear, time-decay, position-based / U-shaped, W-shaped) or through machine-learning models trained on observed conversion paths, with the goal of measuring channel contribution more honestly than single-touch models allow. It is the most-used class of attribution model among in-platform analytics tools and the one most affected by the post-2021 privacy shift.
The rule-based MTA fa...