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Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry

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  • Slãvescu Ecaterina Oana

    ()
    (The Bucharest Academy of Economic Studies)

  • Panait Iulian

    ()
    (The Bucharest Academy of Economic Studies)

Abstract

Nowadays, when companies are dealing with severe global competition, they are making serious investments in Customer Relationship Management (CRM) strategies. One of the cornerstones in CRM is customer churn prediction, the practice of determining a mathematical relation between customer characteristics and the likelihood to end the business contract with the company. This paper focuses on how to better support marketing decision makers in identifying risky customers in telecom industry by using Predictive Models. Based on historical data regarding the customer base for a telecom company, we proposed a Predictive Model using Logistic Regression technique and evaluate its efficiency as compared to the random selection. In the future, we will focus on extending our study by integrating more business considerations and mining models in order to adjust the churn models or redesign marketing activities for the telecom industry.

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Bibliographic Info

Article provided by Ovidius University of Constantza, Faculty of Economic Sciences in its journal Ovidius University Annals, Economic Sciences Series.

Volume (Year): XII (2012)
Issue (Month): 1 (May)
Pages: 1156-1160

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Handle: RePEc:ovi:oviste:v:xii:y:2012:i:12:p:1156-1160

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Web page: http://www.univ-ovidius.ro/facultatea-de-stiinte-economice
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Keywords: predictive models; data mining; churn; time series econometrics;

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  1. 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.
  2. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
  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.
  4. Panait, Iulian, 2011. "Stock market diagnosis," MPRA Paper 44247, University Library of Munich, Germany.
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