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Journal of Engineering
Volume 2013 (2013), Article ID 543940, 9 pages
Research Article

Hierarchical Neural Regression Models for Customer Churn Prediction

1Department of Finance Management, Faculty of Humanities and social Sciences, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran
2Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155/4563, Tehran, Iran

Received 25 November 2012; Revised 27 January 2013; Accepted 1 February 2013

Academic Editor: Jie Zhou

Copyright © 2013 Golshan Mohammadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzy c-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.