Customer churn prediction in the telecommunication sector using a rough set approach

Amin, A.; Anwar, S.; Adnan, A.; Nawaz, M.; Alawfi, K.; Hussain, A.; Huang, K.

Abstract

Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitioners, interested in forecasting the behavior of customers in order to differentiate the churn from non-churn customers. The primary motivation is the dire need of businesses to retain existing customers, coupled with the high cost associated with acquiring new ones. A review of the field has revealed a lack of efficient, rule-based Customer Churn Prediction (CCP) approaches in the telecommunication sector. This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn. The proposed approach effectively performs classification of churn from non-churn customers, along with prediction of those customers who will churn or may possibly churn in the near future. Extensive simulation experiments are carried out to evaluate the performance of our proposed RST based CCP approach using four rule-generation mechanisms, namely, the Exhaustive Algorithm (EA), Genetic Algorithm (GA), Covering Algorithm (CA) and the LEM2 algorithm (LA). Empirical results show that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available, benchmark telecom dataset. Further, comparative results demonstrate that our proposed approach offers a globally optimal solution for CCP in the telecom sector, when benchmarked against several state-of-the-art methods. Finally, we show how attribute-level analysis can pave the way for developing a successful customer retention policy that could form an indispensable part of strategic decision making and planning process in the telecom sector.

• Journal Article Type: Article
• Acceptance Date: Dec 3, 2016
• Online Publication Date: Dec 7, 2016
• Publication Date: 2017-05
• Deposit Date: Aug 27, 2019
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 237
• Pages: 242-254
• DOI: https://doi.org/10.1016/j.neucom.2016.12.009
• Keywords: Classification, Churn prediction, Data mining, Feature selection, Rough Set theory
• Public URL: http://researchrepository.napier.ac.uk/Output/1792457