Summary of Quantifying Marketing Performance at Channel-partner Level by Using Marketing Mix Modeling (mmm) and Shapley Value Regression, By Sean Tang et al.
Quantifying Marketing Performance at Channel-Partner Level by Using Marketing Mix Modeling (MMM) and Shapley Value Regression
by Sean Tang, Sriya Musunuru, Baoshi Zong, Brooks Thornton
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how Shapley Value Regression can be used to analyze marketing performance at the level of individual channel partners, complementing traditional Marketing Mix Modeling (MMM). By applying this approach to real-world data from the financial services industry, the authors demonstrate its practicality in evaluating the contributions of each partner. While structured testing is often considered the most accurate method, it can be costly and complex. Shapley Value Regression offers a more feasible alternative for understanding how each marketing partner influences the channel. The paper also proposes a simple method to derive adjusted coefficients and compares this approach with others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Marketing performance is all about understanding what makes your marketing efforts successful. This study shows that by using a technique called Shapley Value Regression, you can figure out which partners in your marketing channel are doing the best job. The authors used real-world data from the financial services industry to test this approach and found it was surprisingly effective. This method is more practical than some other approaches because it’s less expensive and complex. |
Keywords
* Artificial intelligence * Regression
