Predicting customer repurchase likelihood in e-commerce using machine learning on discrete behavioral data - Hồ Trung, Thành; Lê, Thị Kim Hiền; Lưu, Yến Nhi (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2026)
Due to short product life cycles and rapidly changing trends, e-commerce in the fashion industry requires accurate prediction of the timing and probability of repurchase to enhance the effectiveness of personalized marketing strategies. However, a research gap persists as most studies prioritize long-term repurchase prediction, often overlooking short-term purchase likelihood, especially when dealing with discrete behavioral data. This study proposes a model to predict short-term repurchase likelihood, quantified as a probability over a 30-day period by integrating Recency-Frequency-Monetary (RFM) features with product category diversity and session interaction metrics. Validated using LightGBM and XGBoost algorithms on an online fashion dataset, the proposed models achieved strong classification performance, yielding an ROC-AUC score of approximately 0.8830. Furthermore, R and F were identified as the most influential predictors of short-term purchasing behavior. These findings not only extend the literature on purchase timing prediction but also enable businesses to identify high-potential customers for implementing more effective remarketing strategies.