Customer Churn Prediction for Broadband Internet Services

Authors:
B. Q. Huang;M-T. Kechadi;B. Buckley
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, Dublin 8, Ireland
Venue:
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Year:
2009

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Abstract

Although churn prediction has been an area of research in the voice branch of telecommunications services, more focused studies on the huge growth area of Broadband Internet services are limited. Therefore, this paper presents a new set of features for broadband Internet customer churn prediction, based on Henley segments, the broadband usage, dial types, the spend of dial-up, line-information, bill and payment information, account information. Then the four prediction techniques (Logistic Regressions, Decision Trees, Multilayer Perceptron Neural Networks and Support Vector Machines) are applied in customer churn, based on the new features. Finally, the evaluation of new features and a comparative analysis of the predictors are made for broadband customer churn prediction. The experimental results show that the new features with these four modelling techniques are efficient for customer churn prediction in the broadband service field.