RFM-based repurchase behavior for customer classification and segmentation

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

Journal of Retailing and Consumer Services, 2021, vol. 61, issue C

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.

Keywords: Behavior modeling; Customer classification; Artificial neural network; RFM Analysis; Customer segmentation (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921001326

DOI: 10.1016/j.jretconser.2021.102566

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