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

Author

Listed:
  • 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.

Suggested Citation

  • Slãvescu Ecaterina Oana & Panait Iulian, 2012. "Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 1156-1160, May.
    • Handle: RePEc:ovi:oviste:v:xii:y:2012:i:12:p:1156-1160
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    References listed on IDEAS

    as
    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. Panait, Iulian, 2011. "Stock market diagnosis," MPRA Paper 44247, University Library of Munich, Germany.
    3. 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.
    4. 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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    More about this item

    Keywords

    predictive models; data mining; churn; time series econometrics;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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