Amid rising customer acquisition costs and intensified platform competition, e-commerce operations are shifting their focus from acquiring new customers to retaining existing ones. This paper constructs a data mining-driven framework for user churn prediction and retention strategies, based on behavioral data such as browsing, adding to cart, placing orders, complaints, coupon usage, and repurchase intervals. Combining English-language research from the past three years with publicly available e-commerce churn data, the paper analyzes issues such as sample imbalance, label noise, insufficient model interpretability, and strategy cost constraints, proposing a closed-loop path of "data governance—feature engineering—model training—interpretable analysis—risk stratification—strategy feedback." The study argues that churn prediction can only be transformed from risk identification into effective retention when linked to user value, intervention costs, and response probability.

Chengfeng Jin. Application of Data Mining Technology in Predicting User Churn and Retention Strategies in E-Commerce. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 10-18. https://doi.org/10.38007/SESR.2026.070202.

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