Identifying Customer Churn Patterns with Rough Sets

Yingzhi Yang, Xiaolin Qi, Shuo Sun, Zhen Wang, Hui Jiang and Joohwan Sung
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Yingzhi Yang: Columbia University, New York, United States
Xiaolin Qi: New York University, United States
Shuo Sun: PricewaterhouseCoopers, Shuangjing, Chaoyang District, Beijing, China
Zhen Wang: Dalian University of Technology, Qanjiangzi Qu, Dalian, Liaoning, China
Hui Jiang: Dongbei University of Finance and Economics, Dalian, Liaoning, China
Joohwan Sung: Seoul International School, Bokjeong-dong, Sujeong-gu, Seongnam, Gyeonggi-do, South Korea

Economics and Applied Informatics, 2018, issue 3, 84-90

Abstract: At the core of business lies customer satisfaction. However, customer retention strategies are often based on individual preferences and conventional protocols. For an advantage in the era of global competition, businesses require state-of-the-art techniques based on information science and machine learning to correctly analyze historical data for the prevention of customer loss. The present paper uses Rough Set theory to analyze customer churn data for a telecom service provider. While this dataset has been analyzed in previous research, this paper adds to the literature by taking a systematic and comprehensive approach to the selection of significant features, using them to infer a set of rules clearly describing customer groups that are most likely to churn, and drawing appropriate conclusions from the rules.

Keywords: Customer Relationship Management; Customer Churn; Feature Selection; Prediction; Rough Set Theory; Rules (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2018:i:3:p:84-90

DOI: 10.26397/eai1584040921

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