Summary of Segment Discovery: Enhancing E-commerce Targeting, by Qiqi Li et al.
Segment Discovery: Enhancing E-commerce Targeting
by Qiqi Li, Roopali Singh, Charin Polpanumas, Tanner Fiez, Namita Kumar, Shreya Chakrabarti
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 A machine learning-based policy framework is proposed to optimize customer engagement in e-commerce services. The framework uses uplift modeling and constrained optimization to identify the most valuable customers to target with interventions, such as incentives or games, while considering constraints like budget limitations. Compared to traditional targeting methods, this approach demonstrates improved results in two large-scale studies and a production implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary E-commerce companies want to keep their customers happy and coming back for more. To do this, they often use special offers or games to get people interested in their products. This helps them attract new customers and keep the ones they already have. But, sometimes these tactics don’t work as well as they could because they’re not targeting the right people. In this paper, scientists came up with a new way to figure out which customers would benefit most from these interventions, while also taking into account any limitations or rules that need to be followed. They tested their idea and found it worked better than other methods. |
Keywords
* Artificial intelligence * Machine learning * Optimization
