Multi-touch Attribution for Complex B2B Customer Journeys using Temporal Convolutional Networks

摘要: Customer journeys in Business-to-Business (B2B) transactions contain long and complex sequences of interactions between different stakeholders from the buyer and seller companies. On the seller side, there is significant interest in the multi-touch attribution (MTA) problem, which aims to identify the most influential stage transitions (in the B2B customer funnel), channels, and touchpoints. We design a novel deep learning-based framework, which solves these attribution problems by modeling the conversion of journeys as functions of stage transitions that occur in them. Each stage transition is modeled as a Temporal Convolutional Network (TCN) on the touchpoints that precede it. Further, a global conversion model Stage-TCN is built by combining these individual stage transition models in a non-linear fashion. We apply Layer-wise Relevance Propagation (LRP) based techniques to compute the relevance of all …