Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry
AbstractNowadays, 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 InfoArticle 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)
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Web page: http://www.univ-ovidius.ro/facultatea-de-stiinte-economice
More information through EDIRC
predictive models; data mining; churn; time series econometrics;
Other versions of this item:
- Slavescu, Ecaterina & Panait, Iulian, 2012. "Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry," MPRA Paper 44250, University Library of Munich, Germany.
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- 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
- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- Panait, Iulian, 2011. "Stock market diagnosis," MPRA Paper 44247, University Library of Munich, Germany.
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