Customer Churn Prediction Embedded in an Analytical CRM Model

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015 ; Year: 2015 ; Pages: 258-264 ; Zagreb: IRENET - Society for Advancing Innovation and Research in Economy

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods
• Subject: analytical CRM ; predictive analytics ; churn prediction ; logistic regression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lázár, Ede
• Event: Veröffentlichung
• (who): IRENET - Society for Advancing Innovation and Research in Economy
• (where): Zagreb
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/183657
• Last update: 10.03.2025, 11:42 AM CET