Segment Discovery: Enhancing E-commerce Targeting
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913628463038464 |
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| author | Li, Qiqi Singh, Roopali Polpanumas, Charin Fiez, Tanner Kumar, Namita Chakrabarti, Shreya |
| author_facet | Li, Qiqi Singh, Roopali Polpanumas, Charin Fiez, Tanner Kumar, Namita Chakrabarti, Shreya |
| contents | Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based on the propensity of desirable behavior. However, such policies can be suboptimal as they do not target the set of customers who would benefit the most from the intervention and they may also not take account of any constraints. In this paper, we propose a policy framework based on uplift modeling and constrained optimization that identifies customers to target for a use-case specific intervention so as to maximize the value to the business, while taking account of any given constraints. We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Segment Discovery: Enhancing E-commerce Targeting Li, Qiqi Singh, Roopali Polpanumas, Charin Fiez, Tanner Kumar, Namita Chakrabarti, Shreya Machine Learning Information Retrieval Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based on the propensity of desirable behavior. However, such policies can be suboptimal as they do not target the set of customers who would benefit the most from the intervention and they may also not take account of any constraints. In this paper, we propose a policy framework based on uplift modeling and constrained optimization that identifies customers to target for a use-case specific intervention so as to maximize the value to the business, while taking account of any given constraints. We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation. |
| title | Segment Discovery: Enhancing E-commerce Targeting |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2409.13847 |