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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.05653 |
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| _version_ | 1866909133250232320 |
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| author | Tang, Sean Musunuru, Sriya Zong, Baoshi Thornton, Brooks |
| author_facet | Tang, Sean Musunuru, Sriya Zong, Baoshi Thornton, Brooks |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying Marketing Performance at Channel-Partner Level by Using Marketing Mix Modeling (MMM) and Shapley Value Regression Tang, Sean Musunuru, Sriya Zong, Baoshi Thornton, Brooks Machine Learning 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. |
| title | Quantifying Marketing Performance at Channel-Partner Level by Using Marketing Mix Modeling (MMM) and Shapley Value Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.05653 |