Shapley Value Methods for Attribution Modeling in Online Advertising

TL;DR

This paper enhances Shapley value methods for online advertising attribution by introducing a more efficient calculation and an ordered approach that considers user journey stages, demonstrated on real campaign data.

Contribution

It develops a simplified, computationally efficient Shapley value formula and an ordered method that accounts for user journey order, expanding attribution analysis capabilities.

Findings

Improved computational efficiency of Shapley value calculation

Ordered Shapley method captures user journey stages

Demonstrated effectiveness on real-world data

Abstract

This paper re-examines the Shapley value methods for attribution analysis in the area of online advertising. As a credit allocation solution in cooperative game theory, Shapley value method directly quantifies the contribution of online advertising inputs to the advertising key performance indicator (KPI) across multiple channels. We simplify its calculation by developing an alternative mathematical formulation. The new formula significantly improves the computational efficiency and therefore extends the scope of applicability. Based on the simplified formula, we further develop the ordered Shapley value method. The proposed method is able to take into account the order of channels visited by users. We claim that it provides a more comprehensive insight by evaluating the attribution of channels at different stages of user conversion journeys. The proposed approaches are illustrated using a real-world online advertising campaign dataset.