A Time To Event Framework For Multi-touch Attribution

Dinah Shender
Ali Nasiri Amini
Xinlong Bao
Mert Dikmen
Amy Richardson
Jing Wang
Google(2020) (to appear)


Multi-touch attribution (MTA) estimates the relative contributions of the multiple ads a user may see prior to any observed conversions. Increasingly, advertisers also want to bid based on these attributions, increasing bids on ads that drive more conversions. We describe two requirements for an MTA system to be suitable for this application: First, it must be able to handle continuously updated and incomplete data. Second, it must be sufficiently flexible to capture that an ad’s effect will change over time. We describe an MTA system, consisting of a model for user conversion behavior and an attribution algorithm, that satisfies these requirements. Our model for user conversion behavior treats conversions as occurrences in an inhomogeneous Poisson process, while our attribution algorithm is based on iteratively removing the last ad in the path.