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

水准点(测量) 计算机科学 粗集 集合(抽象数据类型) 领域(数学) 订单(交换) 数据挖掘 实证研究 机器学习 决策规则 人工智能 业务 地理 纯数学 程序设计语言 哲学 认识论 数学 大地测量学 财务
作者
Adnan Amin,Sajid Anwar,Awais Adnan,Muhammad Nawaz,Khalid S. Al-awfi,Amir Hussain,Kaizhu Huang
出处
期刊:Neurocomputing [Elsevier]
卷期号:237: 242-254 被引量:154
标识
DOI:10.1016/j.neucom.2016.12.009
摘要

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.
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