Abstract
Customer segmentation plays a pivotal role in customer relationship management (CRM) and in optimizing marketing strategies. This study applies the RFM model (Recency, Frequency, Monetary) in combination with the K-Means algorithm to analyze shopping behavior from 9,994 retail transactions. The results reveal four distinct customer groups with clear differences in loyalty, transaction frequency, and spending value. Compared with previous studies that primarily focused on applying RFM–KMeans in e-commerce or banking (Chen et al., 2012; Rahman & Khan, 2021), this research offers two main contributions: (i) enhancing clustering quality through Box-Cox transformation and outlier treatment, which improved the Silhouette Score by nearly 44%; and (ii) extending behavioral analysis across multiple dimensions such as product categories, revenue–profit, customer lifecycle, and shipping methods. These findings provide practical implications for businesses to design personalized customer care strategies, optimize resource allocation, and sustain competitive advantage in the context of digital transformation.
Keywords: RFM model; K-Means; customer segmentation; consumer behavior.
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Ban biên tập Tạp chí Kinh tế & Quản trị Kinh doanh
Phòng 514, Nhà điều hành, trường Đại học Kinh tế & Quản trị Kinh doanh
Địa chỉ: Phường Tân Thịnh, thành phố Thái Nguyên
Email: tapchikt-qtkd@tueba.edu.vn; Điện thoại: 0208.3903373
