Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network
Ruihuan Du, Yu Zhong, Harikesh Nair, Bo Cui and Ruyang Shou
This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. The approach has two steps, comprising response modeling and credit allocation. For step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data while capturing the impact of advertising intensity, timing, competition, and user-heterogeneity, which are known to be relevant to ad-response. For step two, we compute Shapley Values, which have the advantage of having axiomatic foundations and satisfying fairness considerations. The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes. The system is deployed at JD.com, and scales to handle the high dimensionality of the problem on the platform.