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Customer behaviour analytics in a supermarket in Taiwan based on RFM model

Author

Listed:
  • Mei-Wei Huang
  • Hao-Wei Yang
  • Ming-Min Lo
  • Yung-Tai Tang
  • Hsin-Hung Wu

Abstract

Supermarkets need to use a data-driven approach to segment customers based on their purchase transactions to meet different customer needs in this highly competitive retail industry in Taiwan. This empirical study combines clustering techniques and RFM model to analyse member customers' transaction data from a database of a supermarket in Taiwan within a six-week period. The results showed that 5,410 member customers are grouped into loyal, new, and vulnerable customers. A one-way analysis of variance is performed to show these three groups of customers are statistically different. This research further explores the top 10 best-selling merchandise items in both purchase quantity and total money spent. Loyal customers need to focus on five merchandise items. New customers have eight out of ten best-selling merchandise items appeared in both purchase quantity and total money spent. Supermarket management need to pay more attention to these eight items for new customers in this supermarket.

Suggested Citation

  • Mei-Wei Huang & Hao-Wei Yang & Ming-Min Lo & Yung-Tai Tang & Hsin-Hung Wu, 2025. "Customer behaviour analytics in a supermarket in Taiwan based on RFM model," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 50(2), pages 220-233.
    • Handle: RePEc:ids:ijisen:v:50:y:2025:i:2:p:220-233
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