Explainability, risk modeling, and segmentation based customer churn analytics for personalized retention in e-commerce

2025-10-13

AI Summary

To address the limited interpretability of e-commerce customer churn models and their inadequate support for fine-grained retention decisions, this paper proposes a three-stage analytical framework integrating explainable AI (XAI), survival analysis, and RFM-based behavioral segmentation. Methodologically, it synergistically combines SHAP-based feature attribution, the Cox proportional hazards model, and dynamic RFM clustering to enable churn root-cause interpretation, optimal intervention timing prediction, and precise identification of high-risk customers. Its key contribution lies in the first systematic integration of explanation-driven attribution analysis, temporal risk modeling, and behaviorally heterogeneous segmentation—overcoming the opacity limitations of conventional black-box models. Empirical evaluation demonstrates that the framework significantly enhances both predictive transparency and intervention efficacy: customer retention improves by 12.7%. It thus delivers verifiable, actionable, and data-driven decision support for personalized retention strategies.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

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.

Problem

• Developing explainable churn models for e-commerce retention strategies
• Identifying high-risk customer segments through RFM profiling techniques
• Estimating optimal intervention timing using survival analysis methods

Innovation

• Explainable AI quantifies feature contributions to churn
• Survival analysis models time-to-event churn risk
• RFM profiling segments customers by transactional behavior