Tối ưu hóa phân khúc khách hàng bán lẻ trực tuyến: Áp dụng các thuật toán phân cụm dựa trên mô hình RFM và các chỉ số đánh giá đa chiều
DOI:
https://doi.org/10.24311/jabes/2025.36.7.01Keywords:
Customer segmentation, Cluster analysis, RFM Model, Data mining, Personalized marketing, Online retailAbstract
This study utilizes transactional data mining techniques to segment customers in the online retail sector. Integrating the CRISP-DM framework with the RFM model, the study evaluates six clustering algorithms including K-Means, Spectral Clustering, Mean Shift, Gaussian Mixture Model, DBSCAN, and Fuzzy C-Means to optimize the customer segmentation solution. Quantitative metrics including Davies-Bouldin, Calinski-Harabasz, Dunn, and Silhouette scores demonstrate that K-Means performs best on the experimental data. The results optimize four distinct customer segments: best customers, loyal customers, potential customers, and churners. The findings provide evidence for data-driven decision making, establishing and implementing targeted marketing strategies through CRM system integration, optimizing resource allocation, and customer retention. Notably, the identified customer segments facilitate personalization, increase revenue potential, and gain competitive advantage. The study also highlights limitations and suggests future directions, such as combining demographic data and deep learning techniques to improve customer segmentation.
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