Article: Customer segmentation for marketing and business management in electronic retailing Journal: International Journal of Business Innovation and Research (IJBIR) 2026 Vol.40 No.5 pp.53 - 71 Abstract: Electronic retailing generates rich transaction data, yet converting these records into actionable customer groups remains challenging. This study develops a data driven segmentation approach by integrating recency-frequency-monetary (RFM) modelling with K-means clustering. Using a retail marketing dataset of 2,240 customers, data were cleaned and standardised, and internal validation was used to select the clustering solution. The results identify four distinct customer segments with clear differences in purchase recency, buying intensity, and spending contribution. Segment interpretation is strengthened by incorporating customer lifetime value and loyalty indicators, highlighting high value customers at risk of inactivity and low value customers with growth or reactivation potential. The findings demonstrate that RFM based clustering supports targeted electronic marketing actions, customer relationship management optimisation, and more efficient allocation of promotional resources in retail distribution. Inderscience Publishers - linking academia, business and industry through research

Title: Customer segmentation for marketing and business management in electronic retailing

Authors: Hoan Thi Duong; Tinh Duc Pham

Addresses: School of Economics, Hanoi University of Industry, No. 298 Cau Dien Street, Tay Tuu Ward, Hanoi, Vietnam ' School of Information and Communications Technology, Hanoi University of Industry, No. 298 Cau Dien Street, Tay Tuu Ward, Hanoi, Vietnam

Abstract: Electronic retailing generates rich transaction data, yet converting these records into actionable customer groups remains challenging. This study develops a data driven segmentation approach by integrating recency-frequency-monetary (RFM) modelling with K-means clustering. Using a retail marketing dataset of 2,240 customers, data were cleaned and standardised, and internal validation was used to select the clustering solution. The results identify four distinct customer segments with clear differences in purchase recency, buying intensity, and spending contribution. Segment interpretation is strengthened by incorporating customer lifetime value and loyalty indicators, highlighting high value customers at risk of inactivity and low value customers with growth or reactivation potential. The findings demonstrate that RFM based clustering supports targeted electronic marketing actions, customer relationship management optimisation, and more efficient allocation of promotional resources in retail distribution.

Keywords: customer segmentation; electronic retail marketing; RFM modelling; recency frequency monetary; RFM; K-means clustering; machine learning.

DOI: 10.1504/IJBIR.2026.153810

International Journal of Business Innovation and Research, 2026 Vol.40 No.5, pp.53 - 71

Received: 03 Apr 2026
Accepted: 08 Apr 2026
Published online: 26 May 2026 *

Title: Customer segmentation for marketing and business management in electronic retailing

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