JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1101 10.6339/23-JDS1101 Statistical Data Science A Time To Event Framework For Multi-touch Attribution ShenderDinah1 Nasiri AminiAli1 BaoXinlong1 DikmenMert1 WangJingjiwang@google.com1∗ Richardson FrickeAmy† Google, Mountain View, CA, USA Corresponding author. Email: jiwang@google.com.

Author was at Google when work was done.

 202426520232215676 Supplementary Material

The supplementary material contains three files. The first is a PDF with additional technical material: In Appendix A we prove a result about E [ N o r m a l i z e d C r e d i t ( j ) ] mentioned in section 3.2.1 of the main text. In Appendix B we give a detailed discussion of the similarities and differences between Backwards Elimination (our proposed attribution method) and Shapley Values, another commonly used method. In Appendix C, we present additional simulation scenarios and their results.

The other two files are a data file, data.csv, with sample data used in our simulations, and a README file with a detailed description of the data. We do not include code to replicate the simulation results. While our method can be applied using standard Poisson regression methods, our simulation framework is tightly integrated with our production environment and our proprietary data format. This makes sharing the code impractical.

31020222242023 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

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 base budget and bidding decisions on these attributions, spending more 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 a credit assignment 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.

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