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Examining Churn and Loyalty Using Support Vector Machine

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  • Ali Dehghan
  • Theodore Trafalis

Abstract

The present study attempts to contribute to the knowledge of how customer loyalty could be assessed, using support vector machine. Additionally, the relationship between customer loyalty rate and churn rate is studied. After literature review and elaborating the concepts of customer loyalty, churn and support vector machine, a comprehensive data analysis on a telecommunication company's customers' dataset, utilizing support vector machine, has been developed. The proposed dataset consists of 1300 customers of a telecommunication company . The results imply that the proposed data analysis tool could be used for recognizing and determining the strategies leading to higher customer loyalty and lower churn.Keywords- Support Vector Machine, Customer Loyalty, Churn

Suggested Citation

  • Ali Dehghan & Theodore Trafalis, 2012. "Examining Churn and Loyalty Using Support Vector Machine," Business and Management Research, Business and Management Research, Sciedu Press, vol. 1(4), pages 153-161, December.
    • Handle: RePEc:jfr:bmr111:v:1:y:2012:i:4:p:153-161
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    References listed on IDEAS

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    1. Vicki McKinney & Kanghyun Yoon & Fatemeh “Mariam” Zahedi, 2002. "The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach," Information Systems Research, INFORMS, vol. 13(3), pages 296-315, September.
    2. Kim, Moon-Koo & Park, Myeong-Cheol & Jeong, Dong-Heon, 2004. "The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services," Telecommunications Policy, Elsevier, vol. 28(2), pages 145-159, March.
    3. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
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    Cited by:

    1. Steen Nielsen, 2020. "Management accounting and the idea of machine learning," Economics Working Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
    2. Couellan, Nicolas & Wang, Wenjuan, 2017. "Uncertainty-safe large scale support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 215-230.

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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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