A new feature set with new window techniques for customer churn prediction in land-line telecommunications

Authors:
B. Q. Huang;T. -M. Kechadi;B. Buckley;G. Kiernan;E. Keogh;T. Rashid
Affiliations:
School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;Eircom Limited, 1 Heuston South Quarter, St. Johns Road, Dublin 8, Ireland;Eircom Limited, 1 Heuston South Quarter, St. Johns Road, Dublin 8, Ireland;Eircom Limited, 1 Heuston South Quarter, St. Johns Road, Dublin 8, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In order to improve the prediction rates of churn prediction in land-line telecommunication service field, this paper proposes a new set of features with three new input window techniques. The new features are demographic profiles, account information, grant information, Henley segmentation, aggregated call-details, line information, service orders, bill and payment history. The basic idea of the three input window techniques is to make the position order of some monthly aggregated call-detail features from previous months in the combined feature set for testing be as the same one as for training phase. For evaluating these new features and window techniques, the two most common modelling techniques (decision trees and multilayer perceptron neural networks) and one of the most promising approaches (support vector machines) are selected as predictors. The experimental results show that the new features with the new window techniques are efficient for churn prediction in land-line telecommunication service fields.