Customer churn analysis in banking sector: Evidence from explainable machine learning models


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DOI:

https://doi.org/10.53753/jame.1.2.03

Keywords:

customer loyalty, customer retention, customer churn analysis, machine learning models, tree-based predictive models

Abstract

Although large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers.

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References

ABBASIMEHR, H., SETAK, M., & SOROOR, J. (2013). A framework for identification of high-value customers by including social network-based variables for churn prediction using neuro-fuzzy techniques. International Journal of Production Research, 51(4), 1279-1294.

AHMAD, A. K., JAFAR, A., & ALJOUMAA, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1), 1-24.

AHN, J. H., HAN, S. P., & LEE, Y.S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications policy, 30(10-11), 552-568.

AKAY, E.C., SOYDAN, N.T.Y. & GACAR, B.K. (2020). MAKİNE ÖĞRENMESİ VE EKONOMİ: BİBLİYOMETRİK ANALİZ. PressAcademia Procedia, 12 (1), 104-105.

ATHANASSOPOULOS, A.D. (2000). Customer satisfaction cues to support market segmentation and explain switching behavior. Journal of business research, 47(3), 191-207.

BATISTA, G.E., PRATI, R.C., & MONARD, M.C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.

BILAL ZORIĆ, A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 116-124.

BLATTBERG, R. C., KIM, B.D., & NESLIN, S.A. (2008). Churn Management. In Database Marketing (pp. 607-633). Springer, New York, NY.

BOSE, I., & CHEN, X. (2009). Quantitative models for direct marketing: A review from systems perspective. European Journal of Operational Research, 195(1), 1-16.

BRÂNDUŞOIU, I., TODEREAN, G., & BELEIU, H. (2016). Methods for churn prediction in the pre-paid mobile telecommunications industry. In 2016 International conference on communications (COMM) (pp. 97-100). IEEE.

BUETTGENS, M., NICHOLS, A., & DORN, S. (2012). Churning under the ACA and state policy options for mitigation. Prepared for Robert Wood Johnson Foundation, Timely Analysis of Immediate Health Policy Issues, http://www. urban. org/UploadedPDF/412587-Churning-Under-the-ACA-and-State-Policy-Options-for-Mitigation. pdf.

ÇAĞLAYAN AKAY, E. (2018). Ekonometride Yeni Bir Ufuk: Büyük Veri ve Makine Öğrenmesi. Social Sciences Research Journal, 7(2): 41-53.

ÇAĞLAYAN AKAY, E. (2020). Ekonometride Büyük Veri ve Makine Öğrenmesi: Temel Kavramlar, Der Yayınları, İstanbul.

CHAKISO, C.B. (2015). The effect of relationship marketing on customers’ loyalty (Evidence from Zemen Bank). EMAJ: Emerging Markets Journal, 5(2), 58-70.

CHATTERJEE, D., & KAMESH, A.V.S. (2020). Significance of Relationship marketing in banks in terms of Customer Empowerment and satisfaction. European Journal of Molecular & Clinical Medicine, 7(4), 999-1009.

CHATTERJEE, D., SEKHAR, S.C., & BABU, M.K. (2021). Customer Empowerment-A Way to Administer Customer Satisfaction in Indian Banking Sector. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 1621-1629.

CHAWLA, N.V. (2009). Data mining for imbalanced datasets: An overview. Data mining and knowledge discovery handbook, 875-886.

CHEN, T., & GUESTRIN, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

COLGATE, M., STEWART, K., & KINSELLA, R. (1996). Customer defection: a study of the student market in Ireland. International journal of bank marketing.

DENG, X., LIU, Q., DENG, Y., & MAHADEVAN, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340, 250-261.

GANESH, J., ARNOLD, M.J., & REYNOLDS, K.E. (2000). Understanding the customer base of service providers: an examination of the differences between switchers and stayers. Journal of marketing, 64(3), 65-87.

HE, Y., HE, Z., & ZHANG, D. (2009). A study on prediction of customer churn in fixed communication network based on data mining. In 2009 sixth international conference on fuzzy systems and knowledge discovery (Vol. 1, pp. 92-94). IEEE.

JABEUR, S.B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197-202.

JABEUR, S. B., GHARIB, C., MEFTEH-WALI, S., & ARFI, W.B. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166, 120658.

KAWALE, J., PAL, A., & SRIVASTAVA, J. (2009). Churn prediction in MMORPGs: A social influence based approach. In 2009 international conference on computational science and engineering (Vol. 4, pp. 423-428). IEEE.

KERAMATI, A., GHANEEI, H., & MIRMOHAMMADI, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2(1), 1-13.

KHAN, A.A., JAMWAL, S., & SEPEHRI, M.M. (2010). Applying data mining to customer churn prediction in an internet service provider. International Journal of Computer Applications, 9(7), 8-14.

KUNCHEVA, L. (2004). Combining pattern classifiers methods and algorithms. john wiley&sons. Inc. Publication, Hoboken.

LAMBERT, J., & LIPKOVICH, I. (2008). A macro for getting more out of your ROC curve. In SAS Global forum, paper (Vol. 231).

LONG, X., YIN, W., AN, L., NI, H., HUANG, L., LUO, Q., & CHEN, Y. (2012, March). Churn analysis of online social network users using data mining techniques. In Proceedings of the international MultiConference of Engineers and Conputer Scientists (Vol. 1).

LÓPEZ-DÍAZ, M. C., LÓPEZ-DÍAZ, M., & MARTÍNEZ-FERNÁNDEZ, S. (2017). A stochastic comparison of customer classifiers with an application to customer attrition in commercial banking. Scandinavian Actuarial Journal, 2017(7), 606-627.

LUNDBERG, S. M., ERION, G. G., & LEE, S. I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888.

MAI, F., TIAN, S., LEE, C., & MA, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European journal of operational research, 274(2), 743-758.

MUTANEN, T. (2006). Customer churn analysis–a case study. Journal of Product and Brand Management, 14(1), 4-13.

Naveen, N., Ravi, V., & Kumar, D. A. (2009). Application of fuzzyARTMAP for churn prediction in bank credit cards. International Journal of Information and Decision Sciences, 1(4), 428-444.

NIE, G., ROWE, W., ZHANG, L., TIAN, Y., & SHI, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.

OZATAC, N., SANER, T., & SEN, Z.S. (2016). Customer satisfaction in the banking sector: the case of North Cyprus. Procedia Economics and Finance, 39, 870-878.

RAJAMOHAMED, R., & MANOKARAN, J. (2018). Improved credit card churn prediction based on rough clustering and supervised learning techniques. Cluster Computing, 21(1), 65-77.

RISSELADA, H., VERHOEF, P.C., & BIJMOLT, T.H. (2010). Staying power of churn prediction models. Journal of Interactive Marketing, 24(3), 198-208.

SALMINEN, J., YOGANATHAN, V., CORPORAN, J., JANSEN, B.J., & JUNG, S.G. (2019). Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research, 101, 203-217.

SARADHI, V.V., & PALSHIKAR, G.K. (2011). Employee churn prediction. Expert Systems with Applications, 38(3), 1999-2006.

SINGH, S., ANUSHA, B., & RAGHUVARDHAN, M. (2013). Impact of Banking Services on Customer Empowerment, Overall Performance and Customer Satisfaction: Empirical Evidence. Journal of Business and Management (IOSR-JBM), 16(1), 17-24.

SOEINI, R. A., & RODPYSH, K. V. (2012). Applying data mining to insurance customer churn management. International Proceedings of Computer Science and Information Technology, 30, 82-92.

VERBEKE, W., DEJAEGER, K., MARTENS, D., HUR, J., & BAESENS, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European journal of operational research, 218(1), 211-229.

WALEED, A., PASHA, A., & AKHTAR, A. (2016). Exploring the impact of liquidity on profitability: Evidence from banking sector of Pakistan. Journal of Internet Banking and Commerce, 21(3).

WOŹNIAK, M., GRANA, M., & CORCHADO, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3-17.

YEŞILKANAT, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210.

ZHENG, K., ZHANG, Z., & SONG, B. (2020). E-commerce logistics distribution mode in big-data context: A case analysis of JD. COM. Industrial Marketing Management, 86, 154-162.

ZORIĆ, A.B. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 116-124.

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Published

2021-12-29

How to Cite

Guliyev, H., & Yerdelen Tatoğlu, F. (2021). Customer churn analysis in banking sector: Evidence from explainable machine learning models. JOURNAL OF APPLIED MICROECONOMETRICS, 1(2), 85–99. https://doi.org/10.53753/jame.1.2.03

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Original Article