Comparison of Classification Algorithm Performance in Predicting Customer Churn in Retail Stores Based on the RFM Model

Mohammad Ghasemi Zadeh; Seyed Abdollah Amin Mousavi; Mohammad  Maleki Nia

Authors

Mohammad Ghasemi Zadeh Department of Information Technology Management,KI.C.,Islamic Azad University, Kish,Iran Author https://orcid.org/0009-0000-4661-0830
Seyed Abdollah Amin Mousavi Department of Management, Ct.C., Islamic Azad University , Tehran, Iran https://orcid.org/0009-0005-3052-5910
Mohammad Maleki Nia Department of Information Technology Management, ST.C., Islamic Azad University , Tehran, Iran Author https://orcid.org/0009-0000-2012-683X

Keywords:

Stacking algorithm, Customer churn prediction, K-Means clustering, RFM model, association rules (FP-Growth)

Abstract

Predicting customer churn is considered one of the fundamental issues in the retail industry because a decline in customer retention rates directly affects acquisition costs and profitability. Accordingly, the present study was conducted with the aim of developing a comprehensive analytical framework for customer churn prediction based on the behavioral RFM model (Recency, Frequency, Monetary) by utilizing various machine learning algorithms. In this study, actual customer transaction data from a retail store were used, and the algorithms of Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naïve Bayes, AdaBoost, and the hybrid Stacking model were evaluated and compared. Following normalization, data cleaning, and feature engineering processes, the models were assessed using the quantitative metrics of Precision, Accuracy, Recall, and F1-Score. The evaluation results demonstrated that the Stacking model outperformed the other algorithms and, with an overall accuracy of approximately 87%, provided a highly accurate framework for predicting churn probability. Variable importance analysis indicated that the Recency indicator plays the most significant role in predicting churn tendency, while the combination of Frequency and Monetary indicators improves model stability. In addition, K-Means clustering and association rule techniques (FP-Growth) were employed for behavioral analysis and customer segmentation, and the results emphasized the effectiveness of combining purchasing pattern analysis with RFM segmentation to improve churn prediction model accuracy. The primary innovation of this study lies in the integration of three analytical approaches—association rules, Stacking classification, and RFM clustering—within a unified empirical framework that establishes a systematic relationship among customer segmentation, purchasing behavior, and churn probability. This framework can serve as a practical basis for designing intelligent customer retention campaigns in the retail industry.

References

[1] M. H. Fathali, Kambiz, R. Zaboli, and M. Khoun Siavash, "A phenomenological study of omni-channel intensity as a key factor influencing omni-channel shopping value," Journal of Marketing Management, vol. 20, no. 3, pp. 1-14, 2025.

[2] D. Chandrakala, "A survey on customer churn prediction using machine learning techniques," International Journal of Computer Applications, vol. 154, no. 10, 2016, doi: 10.5120/ijca2016912237.

[3] M. A. Rahim, "RFM-based repurchase behavior for customer classification and segmentation," Journal of Retailing and Consumer Services, vol. 61, p. 102566, 2021, doi: 10.1016/j.jretconser.2021.102566.

[4] I. Lewaaelhamd, "Customer segmentation using machine learning model: An application of RFM analysis," Journal of Data Science and Intelligent Systems, vol. 2, no. 1, pp. 29-36, 2024, doi: 10.47852/bonviewJDSIS32021293.

[5] A. Harish and C. Malathy, "Evaluative study of cluster based customer churn prediction against conventional RFM based churn model," in 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 2023: IEEE.

[6] S. Khodabandehlou and M. Zivari Rahman, "Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior," Journal of Systems and Information Technology, vol. 19, no. 1-2, pp. 65-93, 2017, doi: 10.1108/JSIT-10-2016-0061.

[7] X. Guoen and H. Qingzhe, "The Research of Online Shopping Customer Churn Prediction Based on Integrated Learning," in Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018), 2018: Atlantis Press.

[8] M. Bogaert and L. Delaere, "Ensemble methods in customer churn prediction: A comparative analysis of the state-of-the-art," Mathematics, vol. 11, no. 5, p. 1137, 2023, doi: 10.3390/math11051137.

[9] S. Awasthi, "Customer churn prediction on e-commerce data using stacking classifier," Authorea Preprints, 2022, doi: 10.36227/techrxiv.20291694.

[10] B. Prabadevi, R. Shalini, and B. R. Kavitha, "Customer churning analysis using machine learning algorithms," International Journal of Intelligent Networks, vol. 4, pp. 145-154, 2023, doi: 10.1016/j.ijin.2023.05.005.

[11] A. Verma, "Consumer behaviour in retail: Next logical purchase using deep neural network," arXiv preprint arXiv:2010.06952, 2020.

[12] J. P. Equihua, "Modelling customer churn for the retail industry in a deep learning based sequential framework," arXiv preprint arXiv:2304.00575, 2023.

[13] M. Mahdi and M. Jabbari, "Predicting customer churn in the fast-moving consumer goods segment of the retail industry using deep learning," Mathematics and Computational Sciences, vol. 5, no. 3, pp. 58-79, 2024.

[14] S. K. Wagh, "Customer churn prediction in telecom sector using machine learning techniques," Results in Control and Optimization, vol. 14, p. 100342, 2024, doi: 10.1016/j.rico.2023.100342.

[15] J. B. Brito, "A framework to improve churn prediction performance in retail banking," Financial Innovation, vol. 10, no. 1, p. 17, 2024, doi: 10.1186/s40854-023-00558-3.

[16] F. Abdi and S. Abolmakarem, "Customer Behavior Mining Framework (CBMF) using clustering and classification techniques," Journal of Industrial Engineering International, vol. 15, no. Suppl 1, pp. 1-18, 2019, doi: 10.1007/s40092-018-0285-3.

[17] H. Casteran, L. Meyer-Waarden, and W. Reinartz, "Modeling customer lifetime value, retention, and churn," in Handbook of Market Research: Springer, 2017, pp. 1-33.

[18] Z. Liang, "Predict customer churn based on machine learning algorithms," HBEM, vol. 10, pp. 270-275, 2023, doi: 10.54097/hbem.v10i.8051.

[19] S. N. Gunesen, "Customer churn prediction in FMCG sector using machine learning applications," in IFIP International Workshop on Artificial Intelligence for Knowledge Management, 2021: Springer, doi: 10.1007/978-3-030-80847-1_6.