用提升模型+约束优化，精准找到最该被激励的电商用户。
Segment Discovery: Enhancing E-commerce Targeting
- 基于提升建模与约束优化，动态识别高价值干预对象。
- 实验显示相比现有方法，业务价值显著提升。
- 适合需要精准营销且有资源限制的电商平台使用。
现代电商常通过优惠或干预措施吸引用户参与游戏、购物、视频等服务，以提升用户获取与留存，从而增加营收并改善体验。然而，当前多采用随机或基于行为倾向的投放策略，可能无法精准触达最具潜力的用户，且忽略实际约束。本文提出一种结合提升建模与约束优化的策略框架，针对特定场景干预，最大化企业收益的同时满足各类约束条件。通过两次大规模实验及生产环境落地验证，该方法在效果上优于现有最优靶向策略。
原文摘要 · Abstract (English)
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.
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