اعتبارسنجی یک چارچوب یکپارچه برای شناسایی و تحلیل رفتار مشتریان با استفاده از شاخص‌های LRFFM و تکنیک‌های داده‌کاوی

محمد قاسمی زاده; سید عبدالله امین موسوی; محمد ملکی نیا

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 Author 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:

Framework Validation, Customer Behavior Analysis, LRFFM Indicators, Data Mining, Customer Relationship Management, CRISP, DM

Abstract

Objective: This study aimed to validate an integrated framework for identifying and analyzing retail customer behavior using extended LRFFM indicators combined with clustering, association-rule mining, and machine-learning classification techniques.

Methodology: This applied, quantitative, data-driven study was conducted according to the CRISP-DM process. The dataset consisted of 541,013 cleaned transactional records obtained from customers of a retail store. Transaction-level data were aggregated at the customer level, and Recency, two Frequency indicators, Monetary value, and Length of relationship were extracted. Customers with more than 93 days since their last purchase were defined as churners. K-Means was used for customer segmentation, FP-Growth and association rules were applied to identify basket-purchase patterns, and ten classification algorithms were compared, including decision tree, k-nearest neighbors, Naive Bayes, neural network, logistic regression, support vector machine, linear discriminant analysis, AdaBoost, bagging, and stacking.

Findings: Clustering of 26,428 customers identified three distinct segments. The most valuable cluster had a Recency of 19.29, an average of 144.35 transactions, a Monetary value of 95,455,030.90, an average of 260.49 purchased items, and a Length of 131.32. Among 12,433 churned customers, 880 were identified as valuable churners. Association-rule analysis revealed different basket structures between valuable churners and non-churners, with several rules reaching a confidence of 1.00. In churn classification, stacking achieved the best overall performance, with 86.47% accuracy, 84.07% precision, 90.00% recall, and an F1-score of 86.93%. Logistic regression and Naive Bayes ranked second and third, respectively.

Conclusion: The integrated LRFFM and data-mining framework successfully combined customer value assessment, churn identification, market-basket analysis, and predictive modeling, and can support more targeted retail decisions concerning customer retention, reactivation, and relationship management.

Downloads

Download data is not yet available.

References

Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., Abhulimen, A. O., & OneAdvanced, U. (2024). Implementing Machine Learning Techniques for Customer Retention and Churn Prediction in Telecommunications. Computer Science & It Research Journal, 5(8), 2011-2025. https://doi.org/10.51594/csitrj.v5i8.1489

Bogaert, M., & Delaere, L. (2023). Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art. Mathematics, 11(5), 1137. https://doi.org/10.3390/math11051137

Brito, J. B. G., Bucco, G. B., & Heldt, R. (2024). A Framework to Improve Churn Prediction Performance in Retail Banking. Financial Innovation, 10, 17. https://doi.org/10.1186/s40854-023-00558-3

Chaubey, G., Gavhane, P. R., Bisen, D., & Arjaria, S. K. (2023). Customer Purchasing Behavior Prediction Using Machine Learning Classification Techniques. Journal of Ambient Intelligence and Humanized Computing, 14(12), 16133-16157. https://doi.org/10.1007/s12652-022-03837-6

Chaudhary, M., Afaq, A., Singh, G., & Kapoor, S. (2024). Unboxing the Mystery: Employee Churn in the Retail Industry Using Machine Learning Approach. International Journal of System Assurance Engineering and Management, 1-11. https://doi.org/10.1007/s13198-024-02490-w

Chou, P., Chuang, H. H. C., Chou, Y. C., & Liang, T. P. (2022). Predictive Analytics for Customer Repurchase: Interdisciplinary Integration of Buy-Till-You-Die Modeling and Machine Learning. European Journal of Operational Research, 296(2), 635-651. https://doi.org/10.1016/j.ejor.2021.04.021

Das, L., Salman, R., Sabeer, S., Ansari, S. K., Aarif, M., & Rana, A. (2023). Customer Retention Using Machine Learning 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), https://doi.org/10.1109/UPCON59197.2023.10434812

Dursun-Cengizci, A., & Caber, M. (2025). Using Machine Learning Methods to Predict Future Churners: An Analysis of Repeat Hotel Customers. International Journal of Contemporary Hospitality Management, 37(1), 36-56. https://doi.org/10.1108/IJCHM-06-2023-0844

Equihua, J. P., Nordmark, H., Ali, M., & Lausen, B. (2023). Modelling Customer Churn for the Retail Industry in a Deep Learning Based Sequential Framework.

Espinoza-Vega, A., & Roa, H. N. (2024). Boosting Customer Retention in Pharmaceutical Retail: A Predictive Approach Based on Machine Learning Models. In Science and Information Conference (pp. 97-117). Springer Nature. https://doi.org/10.1007/978-3-031-62277-9_7

Faritha Banu, J., Neelakandan, S., Geetha, B. T., Selvalakshmi, V., Umadevi, A., & Martinson, E. O. (2022). Artificial Intelligence-Based Customer Churn Prediction Model for Business Markets. Computational Intelligence and Neuroscience, 2022, 1703696. https://doi.org/10.1155/2022/1703696

Jung, S. K., Tsang, S. S., & Phumchusri, N. (2023). Predicting Customer Churn: Revolutionizing Retail with Machine Learning. International Journal for Applied Information Management, 3(1), 46-57. https://doi.org/10.47738/ijaim.v3i1.50

Kimura, T. (2022). Customer Churn Prediction with Hybrid Resampling and Ensemble Learning. Journal of Management Information and Decision Sciences, 25(1).

Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer Churn Prediction System: A Machine Learning Approach. Computing, 104(2), 271-294. https://doi.org/10.1007/s00607-021-00908-y

Liu, R., Ali, S., Bilal, S. F., Sakhawat, Z., Imran, A., Almuhaimeed, A., & Sun, G. (2022). An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms. Applied Sciences, 12(18), 9355. https://doi.org/10.3390/app12189355

Louro, A. C., Pugirá, C. G., & Murari, R. S. (2024). A Scoping Review for Churn Prediction: Step-by-Step Tutorial and Reproducible R Code. International Journal of Business Forecasting and Marketing Intelligence, 9(2), 160-178. https://doi.org/10.1504/IJBFMI.2024.137643

Mahdi, M., & Jabbari, M. (2024). Predicting Customer Churn in the Fast-Moving Consumer Goods Segment of the Retail Industry Using Deep Learning. Mathematics and Computational Sciences, 5(3), 58-79.

Manzoor, A., Qureshi, M. A., Kidney, E., & Longo, L. (2024). A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners. IEEE Access, 12, 70434-70463. https://doi.org/10.1109/ACCESS.2024.3402092

Matuszelański, K., & Kopczewska, K. (2022). Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 165-198. https://doi.org/10.3390/jtaer17010009

Mena, G., Coussement, K., De Bock, K. W., De Caigny, A., & Lessmann, S. (2024). Exploiting Time-Varying RFM Measures for Customer Churn Prediction with Deep Neural Networks. Annals of Operations Research, 339(1), 765-787. https://doi.org/10.1007/s10479-023-05259-9

Mouli, K. C., Raghavendran, C. V., Bharadwaj, V. Y., Vybhavi, G. Y., Sravani, C., Vafaeva, K. M., & Hussein, L. (2024). An Analysis on Classification Models for Customer Churn Prediction. Cogent Engineering, 11(1), 2378877. https://doi.org/10.1080/23311916.2024.2378877

Phumchusri, N., & Amornvetchayakul, P. (2024). Machine Learning Models for Predicting Customer Churn: A Case Study in a Software-as-a-Service Inventory Management Company. International Journal of Business Intelligence and Data Mining, 24(1), 74-106. https://doi.org/10.1504/IJBIDM.2024.135146

Saleh, S., & Saha, S. (2023). Customer Retention and Churn Prediction in the Telecommunication Industry: A Case Study on a Danish University. SN Applied Sciences, 5(7), 173. https://doi.org/10.1007/s42452-023-05389-6

Sana, J. K., Abedin, M. Z., Rahman, M. S., & Rahman, M. S. (2022). A Novel Customer Churn Prediction Model for the Telecommunication Industry Using Data Transformation Methods and Feature Selection. PLoS One, 17(12), e0278095. https://doi.org/10.1371/journal.pone.0278095

Shobana, J., Gangadhar, C., Arora, R. K., Renjith, P. N., Bamini, J., & Chincholkar, Y. D. (2023). E-Commerce Customer Churn Prevention Using Machine Learning-Based Business Intelligence Strategy. Measurement: Sensors, 27, 100728. https://doi.org/10.1016/j.measen.2023.100728

Sina Mirabdolbaghi, S. M., & Amiri, B. (2022). Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions. Discrete Dynamics in Nature and Society, 2022, 5134356. https://doi.org/10.1155/2022/5134356

Sousa, C. F. F. (2023). Categories' Churn: A Machine Learning Approach in Retail Universidade do Minho].

Srivastava, S., & Sinha, S. (2024). Machine Learning Techniques: Predictive Modeling for Customer Churn in Telecommunications. Nanotechnology Perceptions, 20(7), 1151-1166. https://doi.org/10.62441/nano-ntp.v20iS7.95

Sweidan, D., Johansson, U., Gidenstam, A., & Alenljung, B. (2022). Predicting Customer Churn in Retailing 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), https://doi.org/10.1109/ICMLA55696.2022.00105

Taherkhani, L., Daneshvar, A., Amoozad Khalili, H., & Sanaei, M. R. (2023). Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm. Advances in Civil Engineering, 2023, 6029121. https://doi.org/10.1155/2023/6029121

Tran, H., Le, N., & Nguyen, V. H. (2023). Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models. Interdisciplinary Journal of Information, Knowledge, and Management, 18. https://doi.org/10.28945/5086

Vemulapalli, G. (2024). AI-Driven Predictive Models Strategies to Reduce Customer Churn. International Numeric Journal of Machine Learning and Robots, 8(8), 1-13.

Zelenkov, Y. A., & Suchkova, A. S. (2023). Predicting Customer Churn Based on Changes in Their Behavior Patterns. Business Informatics, 17(1), 7-17. https://doi.org/10.17323/2587-814X.2023.1.7.17