用可解释AI分析电商客户流失，实现精准留存
Explainability, risk modeling, and segmentation based customer churn analytics for personalized retention in e-commerce
- 结合可解释AI、生存分析和RFM分群三法
- 精准定位高风险客户与最佳干预时机
- 适合做个性化留存策略的运营团队
在线零售中，客户获取成本通常高于留存成本，促使企业投入流失分析。然而，许多现有流失模型为黑箱，难以揭示流失原因、干预时机及高风险客户群体。本研究提出三组件框架：利用可解释AI量化特征贡献，通过生存分析建模客户流失时间，结合RFM分析按交易行为分群。三者协同实现流失驱动因素归因、干预窗口估算及重点客群识别，支持降低流失率、增强客户忠诚度的个性化留存策略。
原文摘要 · Abstract (English)
In online retail, customer acquisition typically incurs higher costs than customer retention, motivating firms to invest in churn analytics. However, many contemporary churn models operate as opaque black boxes, limiting insight into the determinants of attrition, the timing of retention opportunities, and the identification of high-risk customer segments. Accordingly, the emphasis should shift from prediction alone to the design of personalized retention strategies grounded in interpretable evidence. This study advances a three-component framework that integrates explainable AI to quantify feature contributions, survival analysis to model time-to-event churn risk, and RFM profiling to segment customers by transactional behaviour. In combination, these methods enable the attribution of churn drivers, estimation of intervention windows, and prioritization of segments for targeted actions, thereby supporting strategies that reduce attrition and strengthen customer loyalty.
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