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

Explainability matters for business decisions: The paper demonstrates that moving beyond "black box" prediction models to interpretable AI allows companies to understand why customers leave, not just predict that they will. This transparency is essential for designing effective retention strategies rather than applying one-size-fits-all approaches.

Timing is critical for retention efforts: By combining explainable AI with survival analysis, the framework identifies specific intervention windows—the optimal moments to reach out to at-risk customers. This temporal insight helps companies act proactively rather than reactively, potentially improving retention success rates.

Customer segmentation enables personalization: The study uses RFM profiling (Recency, Frequency, Monetary value) to group customers by behavior patterns. This segmentation allows companies to tailor retention strategies to different customer groups rather than treating all customers identically, improving resource efficiency and relevance.

Responsible AI requires interpretability for stakeholder trust: The paper's emphasis on explainable AI reflects a broader principle: when AI systems influence business decisions affecting customers, stakeholders need to understand the reasoning. This transparency supports accountability and helps prevent discriminatory or unfair retention practices.

Balancing prediction with actionability: The framework prioritizes not just accurate predictions but also actionable insights—identifying which churn drivers can actually be addressed through intervention. This practical orientation ensures AI investments translate into real business value and customer benefit.