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RFM-based repurchase behavior for customer classification and segmentation

Author

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  • Rahim, Mussadiq Abdul
  • Mushafiq, Muhammad
  • Khan, Salabat
  • Arain, Zulfiqar Ali

Abstract

Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.

Suggested Citation

  • Rahim, Mussadiq Abdul & Mushafiq, Muhammad & Khan, Salabat & Arain, Zulfiqar Ali, 2021. "RFM-based repurchase behavior for customer classification and segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921001326
    DOI: 10.1016/j.jretconser.2021.102566
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    Citations

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    Cited by:

    1. Chen, Yanhong & Liu, Luning & Zheng, Dequan & Li, Bin, 2023. "Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    2. Gabriel Marín Díaz & Ramón Alberto Carrasco & Daniel Gómez, 2021. "RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center," Mathematics, MDPI, vol. 9(19), pages 1-27, September.
    3. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.

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