Simultaneous Use of Clustering, Classification, and Association Rules for Customer Churn Analysis in Retail Stores

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:

Customer Churn Prediction, Retail Data Mining, RFM Model Enhancement (RFFML), Stacking Ensemble Method, Clustering–Classification–Association Rules

Abstract

Customer churn analysis plays a critical role in sustaining profitability in competitive retail environments. This study proposes an integrated data mining framework that combines clustering, classification, and association rule mining within an enhanced RFM-based model, referred to as RFFML, which extends the traditional Recency, Frequency, and Monetary indicators by incorporating Frequency Items and Length of Interaction variables. Using large-scale real transactional data from a retail store, customer segmentation is first performed through clustering to identify distinct behavioral profiles. Subsequently, several classification models are evaluated for churn prediction, with an ensemble-based Stacking classifier achieving the highest predictive performance. In parallel, association rule mining is employed to uncover frequent product combinations associated with loyal and churned customer segments. The main contribution of this work lies in the analytical integration of these complementary techniques within a single unified framework, enabling both accurate churn prediction and interpretable behavioral insights. Experimental results demonstrate that the proposed approach outperforms individual classifiers while providing actionable knowledge to support targeted retention strategies in the retail sector.

References

Abbas, J. (2020). Impact of Total Quality Management on Corporate Green Performance through the Mediating Role of Corporate Social Responsibility. Journal of Cleaner Production, 242, 118458. https://doi.org/10.1016/j.jclepro.2019.118458

Abdi, F., & Abolmakarem, S. (2019). Customer Behavior Mining Framework (CBMF) Using Clustering and Classification Techniques. Journal of Industrial Engineering International, 15(Suppl 1), 1-18. https://doi.org/10.1007/s40092-018-0285-3

Adams, C. (2017). The Sustainable Development Goals, Integrated Thinking and the Integrated Report.

Agbemadon, K. B., Couturier, R., & Laiymani, D. (2022). Churn Detection Using Machine Learning in the Retail Industry. International Conference on Computer, Control and Robotics (ICCCR),

Ali, A., Mahfouz, A., & Arisha, A. (2017). Analysing Supply Chain Resilience: Integrating the Constructs in a Concept Mapping Framework via a Systematic Literature Review. Supply Chain Management: An International Journal, 22(1), 16-39. https://doi.org/10.1108/SCM-06-2016-0197

Andersen, A. D., & Gulbrandsen, M. (2020). The Innovation and Industry Dynamics of Technology Phase-Out in Sustainability Transitions: Insights from Diversifying Petroleum Technology Suppliers in Norway. Energy Research & Social Science, 64, 101447. https://doi.org/10.1016/j.erss.2020.101447

Bagul, N., Berad, P., Surana, P., & Khachane, C. (2021). Retail Customer Churn Analysis Using RFM Model and K-Means Clustering. International Journal of Engineering Research and Technology, 10(03).

Bhargava, D., Bhargava, A., Melgarejo-Bolivar, R. P., Montes de Oca-Nina, A. M., & Chaudhury, S. (2024). Enhancing Personalization of Customer Services in E-Commerce System Using Predictive Analytics. Fluctuation and Noise Letters, 23(02), 2440011. https://doi.org/10.1142/S021947752440011X

Casteran, H., Meyer-Waarden, L., & Reinartz, W. (2017). Modeling Customer Lifetime Value, Retention, and Churn. In Handbook of market research (pp. 1-33). Springer. https://doi.org/10.1007/978-3-319-05542-8_21-1

Chen, J., Guo, Z., Zhang, C., Tian, Y., & Li, Y. (2023). Research on the Application of CEEMD-LSTM-LSSVM Coupled Model in Regional Precipitation Prediction. Water, 15(8), 1465. https://doi.org/10.3390/w15081465

Cheng, Z., He, J., Xu, S., & Yang, X. (2023). Coupling Assessment for the Water-Economy-Ecology Nexus in Western China. Ecological Indicators, 154, 110648. https://doi.org/10.1016/j.ecolind.2023.110648

Eccles, R. G., & Klimenko, S. (2019). The Investor Revolution. Harvard business review, 97(3), 106-116.

Eccles, R. G., & Krzus, M. P. (2019). Implementing the Task Force on Climate-Related Financial Disclosures Recommendations: An Assessment of Corporate Readiness. Schmalenbach Business Review, 71(2), 287-293. https://doi.org/10.1007/s41464-018-0060-4

Fu, Q., Gong, Q., Zhao, X. X., & Chang, C. P. (2023). The Effects of International Sanctions on Green Innovations. Technological and Economic Development of Economy, 29(1), 141-164. https://doi.org/10.3846/tede.2022.17782

Gaur, A., & Dubey, R. (2018). Predicting Customer Churn Prediction in Telecom Sector Using Various Machine Learning Techniques. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT),

Hadi, S., & Baskaran, S. (2021). Examining Sustainable Business Performance Determinants in Malaysia Upstream Petroleum Industry. Journal of Cleaner Production, 294, 126231. https://doi.org/10.1016/j.jclepro.2021.126231

Hughes, A. M. (1994). Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program.

Ivanov, D. (2020). Predicting the Impacts of Epidemic Outbreaks on Global Supply Chains: A Simulation-Based Analysis on the Coronavirus Outbreak (COVID-19/SARS-CoV-2) Case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922

Khandelwal, N., & Sakalle, D. V. (2025). TBMOR Customer Churn Predication Using XGBoost Classifier. Journal of Information Systems Engineering and Management. https://doi.org/10.52783/jisem.v10i19s.3111

Kim, Y., & Jun, H. (2022). Exploring Technology Innovation Factors, Government Support and Performance of Development-Related Social Enterprises: Evidence from South Korea. Sustainability, 14(22), 15406. https://doi.org/10.3390/su142215406

Kuhnen, M., & Hahn, R. (2019). From SLCA to Positive Sustainability Performance Measurement: A Two-Tier Delphi Study. Journal of Industrial Ecology, 23(3), 615-634. https://doi.org/10.1111/jiec.12762

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

Lu, M. T., Hsu, C. C., Liou, J. J., & Lo, H. W. (2018). A Hybrid MCDM and Sustainability-Balanced Scorecard Model to Establish Sustainable Performance Evaluation for International Airports. Journal of Air Transport Management, 71, 9-19. https://doi.org/10.1016/j.jairtraman.2018.05.008

Malhotra, R., Shah, S., & Bansal, S. (2025). Customer Churn Analysis Using Machine Learning in Telecom Industry. Proceedings of Data Analytics and Management, Singapore.

Matuszelanski, 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

Mendez, G. A. D. (2020). Data Mining Guided Process for Churn Prediction in Retail: From Descriptive to Predictive Analytics Universidade NOVA de Lisboa]. Portugal.

Mining, W. I. D. (2006). Data Mining: Concepts and Techniques (Vol. 10). Morgan Kaufmann.

Pasquadibisceglie, V., Appice, A., Ieva, G., & Malerba, D. (2024). TSUNAMI: An Explainable PPM Approach for Customer Churn Prediction in Evolving Retail Data Environments. Journal of Intelligent Information Systems, 62(3), 705-733. https://doi.org/10.1007/s10844-023-00838-5

Prabadevi, B., Shalini, R., & Kavitha, B. R. (2023). Customer Churning Analysis Using Machine Learning Algorithms. International Journal of Intelligent Networks, 4, 145-154. https://doi.org/10.1016/j.ijin.2023.05.005

Rahim, M. A., Mushafiq, M., Khan, S., & Arain, Z. A. (2021). RFM-Based Repurchase Behavior for Customer Classification and Segmentation. Journal of Retailing and Consumer Services, 61, 102566. https://doi.org/10.1016/j.jretconser.2021.102566

Santoso, B., & Kasih, T. P. (2024). Green Lean Concept for Measurement of Sustainable Performance Mediated by Organizational Culture in Oil & Gas and Petrochemical Industry. International Review of Management and Marketing, 14(5), 88. https://doi.org/10.32479/irmm.16621

Schaeffer, S. E., & Sanchez, S. V. R. (2020). Forecasting Client Retention: A Machine-Learning Approach. Journal of Retailing and Consumer Services, 52, 101918. https://doi.org/10.1016/j.jretconser.2019.101918

Shirazi, A., Mohammadi Dolat-Abadi, H., Dehghani, J., & Asgari Sooran, M. (2024). Prioritizing Key Parameters for Sustainable Supply Chain Development in the Construction Industry Using a Copeland Hybrid Method. Environmental Energy and Economic Research, 8(4), eS095.

Sony, M., & Naik, S. S. (2019). Ten Lessons for Managers While Implementing Industry 4.0. IEEE Engineering Management Review, 47(2), 45-52. https://doi.org/10.1109/EMR.2019.2913930

Valente, F., Paredes, S., Henriques, J., Rocha, T., de Carvalho, P., & Morais, J. (2022). Interpretability, Personalization and Reliability of a Machine Learning Based Clinical Decision Support System. Data Mining and Knowledge Discovery, 36(3), 1140-1173. https://doi.org/10.1007/s10618-022-00821-8

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

Verma, A. (2020). Consumer Behaviour in Retail: Next Logical Purchase Using Deep Neural Network. ArXiv. https://arxiv.org/abs/2010.06952

Wu, P., Wang, Z., Li, H., & Zeng, N. (2024). KD-PAR: A Knowledge Distillation-Based Pedestrian Attribute Recognition Model with Multi-Label Mixed Feature Learning Network. Expert Systems with Applications, 237, 121305. https://doi.org/10.1016/j.eswa.2023.121305

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the Art and Future Trends. International Journal of Production Research, 56(8), 2941-2962. https://doi.org/10.1080/00207543.2018.1444806

Youssef, A. B., Omrani, N., & Zeqiri, A. (2025). AI for Good: Advancing Sustainability in Smart Manufacturing. Ieee Transactions on Engineering Management. https://doi.org/10.1109/TEM.2025.3640227