Customer Churn Prediction Embedded in an Analytical CRM Model
Keywords:
analytical CRM, predictive analytics, churn prediction, logistic regressionAbstract
This paper presents a practical implementation of an analytical customer relationship (CRM) model, which aims to increase the customer satisfaction, thereby reducing the rate of attrition. The analytical CRM model not only manages and synchronizes customer relationship management processes, but also creates added value regarding to customers by applying mathematical, predictive methods. This presented model was implemented at a Hungarian gas service provider, and estimates the probability of churn for each customer based on the characteristics of former and present customers. The methodological approach is based on econometrical background; the analytical tool is a binomial logistic regression model. As a result this study presents that using logistic regression models as predictive analytic tool we can fulfil multiple CRM goals. Using the theoretical framework of Swift (2001) we can state that the model consists of more CRM dimensions simultaneously. These are the predicted churn probability as a customer retention dimension, and the information about the efficiency of different CRM elements, and CRM channels, as a customer attraction dimension.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Greene, W. (2003), “Econometric analysis”, Fifth Edition, Prentice Hall, New Jersey.
Halper, F. (2011), „The top 5 trends in predictive analytics”, Information & Management, Vol. 21 No. 6, pp. 16-18.
Hosmer, W.D., Lemeshow, S. (2000), “Applied Logistic Regression”, 2nd edition, Wiley, New York.
Kumar, A., Vithala, R.R., Harsh S. (1995), “An Empirical Comparison of Neural Network and Logistic Regression Models,” Marketing Letters, Vol. 61 No. 4, pp. 251-264.
Massey, A.P., Montoya-Weiss, M.M., Holcom, K. (2001), "Re-engineering the customer relationship: leveraging knowledge", Decision Support Systems, Vol. 32, pp. 155-170.
Miguéis, V.L., Van den Poel, D., Camanho, A.S., Falcão, J. (2012), “Modeling partial customer churn: On the value of first product-category purchase sequences”, Expert Systems with Applications, Vol. 39 No. 12, pp. 11250-11256.
Mirzaei, T., Iyer L. (2014), “Application of Predictive Analytics In Customer Relationship Management: a Literature Review and Classification”, Proceedings of the Southern Association for Information Systems Conference, Macon, GA, USA.
Ngai, E.W. T., Xiu, L., Chau, D.C.K. (2009), “Application of data mining techniques in customer relationship management: A literature review and classification”, Expert Systems with Applications, Vol. 36 No. 2, pp. 2592-2602.
Parvatiyar, A., Sheth, J. (2001), “Customer Relationship Management: Emerging Practice, Process, and Discipline”, Journal of Economic and Social Research, pp. 1-34.
Srivastava, J., Wang, J.H., Lim, E.P., Hwang S.Y. (2002), “A Case for Analytical Customer Relationship Management”, Computer Science, Vol. 2336, pp. 14-27.
Swift, R.S. (2001), “Accelerating Customer Relationships”, Prentice Hall, New Jersey.
Xu, M., Walton, J. (2005), "Gaining customer knowledge through analytical CRM", Industrial Management & Data Systems, Vol. 105 No. 7, pp. 955 – 971.
