The role of behavioral data in predicting credit card customer churn: a data analytics approach

Nguyễn Hải Anh; Nguyễn Hoàng Thao

Issue: Số 25 - Tháng 9 - 2026 Tài chính - Ngân hàng - Bảo hiểm

The role of behavioral data in predicting credit card customer churn: a data analytics approach

Published: October 3, 2026

Abstract

This study evaluates the predictive power of behavioral data for customer churn in the credit card sector. It examines behavioral factors such as spending and payment habits and their relationships with churn. Drawing on a comprehensive data analytics approach and theoretical frameworks of churn behavior, the authors develop a predictive model to identify customers at risk of leaving in the future. The findings provide useful information for retail banks seeking to improve customer retention strategies and also contribute to the broader field of data analytics by demonstrating the potential of behavioral data in predictive modeling.

Keywords

behavioral data customer churn credit card data analytics retail banking

References

Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94, 290–301. https://doi.org/10.1016/j.jbusres.2018.03.003

DOI: doi.org/10.1016/j.jbusres.2018.03.003

Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252–268. https://doi.org/10.1016/j.ejor.2003.12.010

DOI: doi.org/10.1016/j.ejor.2003.12.010

Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–107.

Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 9–24. https://doi.org/10.1002/dir.10045

DOI: doi.org/10.1002/dir.10045

Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902–2917. https://doi.org/10.1016/j.cor.2005.11.007

DOI: doi.org/10.1016/j.cor.2005.11.007

Lazarov, V., & Capota, M. (2007). Churn prediction. Business Analytics Course, TUM Computer Science.

Lemmens, A., & Croux, C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2), 276–286. https://doi.org/10.1509/jmkr.43.2.276

DOI: doi.org/10.1509/jmkr.43.2.276

Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003

DOI: doi.org/10.1016/j.ijinfomgt.2019.04.003

Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204–211.

10.

Quinlan, B., Chung, J., Liu, G., Wang, D. B., & Fan, M. (2024). From acquisition to activation: Revisiting credit card issuer strategies in Hong Kong. Quinlan & Associates.

11.

Reichheld, F. F., & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105–111.

12.

Shirazi, F., & Mohammadi, M. (2019). A big data analytics model for customer churn prediction in the retiree segment. International Journal of Information Management, 48, 238–253. https://doi.org/10.1016/j.ijinfomgt.2018.10.005

DOI: doi.org/10.1016/j.ijinfomgt.2018.10.005

13.

Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354–2364. https://doi.org/10.1016/j.eswa.2010.08.023

DOI: doi.org/10.1016/j.eswa.2010.08.023