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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.09198 |
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| _version_ | 1866914300062334976 |
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| author | Kong, Li Wang, Bingzhe Chen, Zhou Hu, Suhan Ma, Yuchao Qi, Qi Song, Suoyuan Jin, Bicheng |
| author_facet | Kong, Li Wang, Bingzhe Chen, Zhou Hu, Suhan Ma, Yuchao Qi, Qi Song, Suoyuan Jin, Bicheng |
| contents | Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this scenario, we propose a novel marketing framework, named \textbf{S}equence-\textbf{A}ware \textbf{C}onstrained \textbf{O}ptimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce Kong, Li Wang, Bingzhe Chen, Zhou Hu, Suhan Ma, Yuchao Qi, Qi Song, Suoyuan Jin, Bicheng Machine Learning Artificial Intelligence Coupon distribution is a critical marketing strategy used by online platforms to boost revenue and enhance user engagement. Regrettably, existing coupon distribution strategies fall far short of effectively leveraging the complex sequential interactions between platforms and users. This critical oversight, despite the abundance of e-commerce log data, has precipitated a performance plateau. In this paper, we focus on the scene that the platforms make sequential coupon distribution decision multiple times for various users, with each user interacting with the platform repeatedly. Based on this scenario, we propose a novel marketing framework, named \textbf{S}equence-\textbf{A}ware \textbf{C}onstrained \textbf{O}ptimization (SACO) framework, to directly devise coupon distribution policy for long-term revenue boosting. SACO framework enables optimized online decision-making in a variety of real-world marketing scenarios. It achieves this by seamlessly integrating three key characteristics, general scenarios, sequential modeling with more comprehensive historical data, and efficient iterative updates within a unified framework. Furthermore, empirical results on real-world industrial dataset, alongside public and synthetic datasets demonstrate the superiority of our framework. |
| title | SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09198 |