A Non-parametric Approach to the Multi-channel Attribution Problem

Multi-channel marketing attribution modeling is a two-stage process. First, the value of exposure from di erent marketing channel needs to be estimated. Next, the total surplus achieved needs to be assigned to individual marketing channels by using the exposure e ects from the rst stage. There has been limited work in exploring possible choices and e ects of determining the value of exposure to di erent marketing channels in the rst stage. This paper proposes novel nonparametric and semi-parametric approaches to estimate the value function and compares it with other natural choices. We build a simulation engine that captures important behavioral phenomenon known to a ect a customer's purchase decision; and compare the performance of ve attribution approaches in their ability to closely approximate the known ground truth . Our proposed method works well when marketing channels have high levels of synergy. We apply the proposed approaches on two real-world datasets and present the results.