Abstract
Modern customer analytics offers retailers a variety of unprecedented opportunities to enhance customer intelligence solutions by tracking individual clients and their peers and studying clientele behavioral patterns. While telecommunication providers have been actively utilizing peer network data to improve their customer analytics for a number of years, there yet exists a very limited knowledge on the peer effects in retail banking. We introduce modern deep learning concepts to quantify the impact of social network variables on bank customer attrition. Furthermore, we propose a novel deep ensemble classifier that systematically integrates predictive capabilities of individual classifiers in a meta-level model, by efficiently stacking multiple predictions using convolutional neural networks. We evaluate our methodology in application to customer retention in a retail financial institution in Canada.
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References
Backiel, A., Baesens, B., Claeskens, G.: Mining telecommunication networks to enhance customer lifetime predictions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8468, pp. 15–26. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07176-3_2
Backiel, A., Baesens, B., Claeskens, G.: Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. J. Oper. Res. Soc. 67(9), 1135–1145 (2016)
Benoit, D.F., Van den Poel, D.: Improving customer retention in financial services using kinship network information. Expert Syst. Appl. 39(13), 11435–11442 (2012)
Castanedo, F., Valverde, G., Zaratiegui, J., Vazquez, A.: Using deep learning to predict customer churn in a mobile telecommunication network (2014)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Clarke, B.: Comparing Bayes model averaging and stacking when model approximation error cannot be ignored. J. Mach. Learn. Res. 4, 683–712 (2003)
Compensation Force: 2016 turnover rates by industry (2017)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Džeroski, S., Ženko, B.: Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54(3), 255–273 (2004)
Ernst & Young: The customer takes control. Consumer Banking Survey (2012)
Gallo, A.: The value of keeping the right customers. Harv. Bus. Rev. 5, 2–6 (2014)
Han, S.H., Lu, S.X., Leung, S.C.: Segmentation of telecom customers based on customer value by decision tree model. Expert Syst. Appl. 39(4), 3964–3973 (2012)
Hill, S., Provost, F., Volinsky, C.: Network-based marketing: identifying likely adopters via consumer networks. Stat. Sci. 21(2), 256–276 (2006)
HSBC: HSBC launches global ‘social network’ for business customers (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)
Larivière, B., Van den Poel, D.: Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 29(2), 472–484 (2005)
Li, D.C., Dai, W.L., Tseng, W.T.: A two-stage clustering method to analyze customer characteristics to build discriminative customer management: a case of textile manufacturing business. Expert Syst. Appl. 38(6), 7186–7191 (2011)
Macintosh, G., Lockshin, L.S.: Retail relationships and store loyalty: a multi-level perspective. Int. J. Res. Mark. 14(5), 487–497 (1997)
Mao, H., Jin, X., Zhu, L.: Methods of measuring influence of bank customer using social network model. Am. J. Ind. Bus. Manag. 5(4), 155 (2015)
Miguéis, V.L., Van den Poel, D., Camanho, A.S., e Cunha, J.F.: Modeling partial customer churn: on the value of first product-category purchase sequences. Expert Syst. Appl. 39(12), 11250–11256 (2012)
Mozer, M.C., Wolniewicz, R., Grimes, D.B., Johnson, E., Kaushansky, H.: Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Netw. 11(3), 690–696 (2000)
NG Data: Predicting and preventing customer churn by unlocking big data (2013)
Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)
Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6354, pp. 92–101. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15825-4_10
Spanoudes, P., Nguyen, T.: Deep learning in customer churn prediction: unsupervised feature learning on abstract company independent feature vectors. arXiv:1703.03869 (2017)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of CVPR, pp. 1–9. IEEE (2015)
Verbeke, W., Martens, D., Baesens, B.: Social network analysis for customer churn prediction. Appl. Soft Comput. 14, 431–446 (2014)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)
Acknowledgements
This research was partially supported by NSF IIS 1633331 & 1633355, NSF DMS 1736368, and Simons Foundation. The work of V. Lyubchich was supported by Mitacs Accelerate Internship Awards with contributions from Temenos Canada.
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Chen, Y., Gel, Y.R., Lyubchich, V., Winship, T. (2018). Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_30
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