Attribution Evaluation with User Matched Paths

Qixuan Feng
Google Inc.(2021)


Many digital advertisers continue to rely on attribution models to estimate the effectiveness of their marketing spend, allocate budget, and guide bidding decisions for real time auctions. The work described in this paper builds on previous efforts to better understand the capabilities and limitations of attribution models using simulated path data with experiment-based ground truth. While previous efforts were based on a generic specification of user path characteristics (e.g., ad channels considered, observed events included, and the transition rates between observed events), here we generalize the process to include a pre-analysis optimization step that matches the characteristics of the simulated path data with a set of reference path data from a particular advertiser. An attribution model analysis conducted with path-matched data is more relevant and applicable to an advertiser than generic path data. We demonstrate this path-fitting process using data from The simulated matched paths are used to demonstrate a few key capabilities and limitations for several position-based attribution models.