Hierarchical Neural Regression Models for Customer Churn Prediction
Joint Authors
Tavakkoli-Moghaddam, Reza
Mohammadi, Golshan
Mohammadi, Mehrdad
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-14
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Mohammadi, Golshan& Tavakkoli-Moghaddam, Reza& Mohammadi, Mehrdad. 2013. Hierarchical Neural Regression Models for Customer Churn Prediction. Journal of Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-480247
Modern Language Association (MLA)
Mohammadi, Golshan…[et al.]. Hierarchical Neural Regression Models for Customer Churn Prediction. Journal of Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-480247
American Medical Association (AMA)
Mohammadi, Golshan& Tavakkoli-Moghaddam, Reza& Mohammadi, Mehrdad. Hierarchical Neural Regression Models for Customer Churn Prediction. Journal of Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-480247
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-480247