Quantifying Marketing Performance at Channel-Partner Level by Using Marketing Mix Modeling (MMM) and Shapley Value Regression

• URL: http://arxiv.org/abs/2401.05653v3
• Date: Mon, 11 Mar 2024 13:55:01 GMT
• Title: Quantifying Marketing Performance at Channel-Partner Level by Using Marketing Mix Modeling (MMM) and Shapley Value Regression
• Authors: Sean Tang, Sriya Musunuru, Baoshi Zong, Brooks Thornton
• Abstract summary: This paper explores the application of Shapley Value Regression in dissecting marketing performance at channel-partner level. We demonstrate the practicality of Shapley Value Regression in evaluating individual partner contributions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the application of Shapley Value Regression in dissecting marketing performance at channel-partner level, complementing channel-level Marketing Mix Modeling (MMM). Utilizing real-world data from the financial services industry, we demonstrate the practicality of Shapley Value Regression in evaluating individual partner contributions. Although structured in-field testing along with cooperative game theory is most accurate, it can often be highly complex and expensive to conduct. Shapley Value Regression is thus a more feasible approach to disentangle the influence of each marketing partner within a marketing channel. We also propose a simple method to derive adjusted coefficients of Shapley Value Regression and compare it with alternative approaches.

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