A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution

Jun Tao (), Qian Chen (), James W. Snyder (), Arava Sai Kumar (), Amirhossein Meisami () and Lingzhou Xue ()
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Jun Tao: Digital Experience, Adobe Inc., San Jose, California 95110
Qian Chen: Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania 16802
James W. Snyder: Digital Experience, Adobe Inc., San Jose, California 95110
Arava Sai Kumar: Digital Experience, Adobe Inc., San Jose, California 95110
Amirhossein Meisami: Digital Experience, Adobe Inc., San Jose, California 95110
Lingzhou Xue: Eberly College of Science, The Pennsylvania State University, University Park, Pennsylvania 16802

Management Science, 2025, vol. 71, issue 9, 7312-7332

Abstract: Marketers rely on various online advertising channels to reach customers and are increasingly interested in multi-touch attribution , which evaluates the contribution of each touchpoint to a conversion. However, as the numbers of marketing channels and touchpoints increase, the attribution challenge becomes more intricate because of the complex interplay among different touchpoints within and across channels. Utilizing customer path-to-purchase data, this article addresses this challenge by developing a novel graphical point process framework to investigate the relational structure among various touchpoints. Based on this framework, we propose graphical attribution methods that allocate attribution scores to individual touchpoints or corresponding channels for each customer’s path to purchase. These scores are calculated using a probabilistic definition of removal effects. We evaluate the proposed methods and compare their performance with commonly used attribution models through extensive simulation studies and a real-world attribution application.

Keywords: Granger causality; graphical model; high-dimensional statistics; multi-touch attribution; point process (search for similar items in EconPapers)
Date: 2025
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