The Effect of the Length of the Customer Event History and the Staying Power of the Predictive Models in the Customer Churn Prediction: Case Study of Migros Sanal Market

The customer churn prediction problem is studied for various sectors under several aspects. In this study, we consider the effect of the length of the customer event history and the staying power of the predictive models for the churn prediction problem of a leading online fast-moving consumer goods retailer in Turkey. These are important aspects of the churn prediction models as they help decision makers to determine the optimal length of the past data for predicting the customer churn as well as lifespan of the predictive models. We find that the length of the customer event history logarithmically increases the predictive power of models, validating findings in the literature in the newspaper subscription sector. Regarding the staying power of the predictive models, we conclude that the models in online fast-moving consumer goods retailing has a slightly longer lifespan that the models discussed in the literature for an Internet service provider and an insurance company.

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[1] M. Ballings and D. Van den Poel, 2012. Customer event history for churn prediction: How long is long enough? Expert Syst Appl, 39(18), 13517-13522.

[2] L. Breiman, 2001. Random forests. Mach Learn, 45(1), 5-32.

[3] W. Buckinx and D. Van den Poel, 2005. Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Oper Res, 164(1):252-268.

[4] D. R. Cox, 1958. The regression analysis of binary sequences (with discussion). J R Stat Soc Series B Stat Methodol, 20, 215-242.

[5] J. H. Friedman, 2001. Greedy function approximation: A gradient boosting machine. Ann Stat, 29(5), 1189-1232.

[6] J. H. Friedman, 2002. Stochastic gradient boosting. Comput Stat Data Anal, 38(4), 367-378.

[7] N. Glady, B. Baesens, and C. Croux, 2009. Modeling churn using customer lifetime value. Eur J Oper Res, 197(1), 402-411.

[8] M. Hernant and S. Rosengren, 2017. Now what? Evaluating the sales effects of introducing an online store, J Retail Consum Serv, Volume 39, 305-313.

[9] K. Kim, C.-H. Jun, and J. Lee, 2014. Improved churn prediction in telecommunication industry by analyzing a large network. Expert Syst Appl, 41(15), 6575-6584.

[10] Y.-H. Lee, C.-P. Wei, T.-H. Cheng, and C.-T. Yang, 2012. Nearest-neighbor-based approach to timeseries classification. Decis Support Syst, 53(1), 207-217.

[11] A. Lemmens and C. Croux, 2006. Bagging and boosting classification trees to predict churn. J Mark Res, 43(2), 276-286.

[12] A. Martínez, C. Schmuck, S. Pereverzyev, C. Pirker, and M. Haltmeier, 2018. A machine learning framework for customer purchase prediction in the non-contractual setting. Euro J Oper Res, https://doi.org/10.1016/j.ejor.2018.04.034.

[13] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, 2011. Scikit-learn: Machine learning in Python. J Mach Learn Res, 12, 2825- 2830.

[14] H. Risselada, P. C. Verhoef, and T. H. Bijmolt, 2010. Staying power of churn prediction models. J Interact Market, 24(3), 198-208.

[15] R. T. Rust and A. J. Zahorik, 1993. Customer satisfaction, customer retention, and market share. J Retailing, 69(2), 193-215.