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Modeling customer churn in a non-contractual setting: the case of telecommunications service providers created by Ali Tamaddoni Jahromi, Mohammad Mehdi Sepehri, Babak Teimourpour and Sarvenaz Choobdar

By: Contributor(s): Material type: TextTextSeries: Journal of Strategic Marketing ; Volume 18, number 7,Abingdon Taylor and Francis 2010Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): Online resources: Summary: The telecommunications industry with an approximate annual churn rate of 30% can nowadays be considered as one of the top sectors on the list of those suffering from customer churn. Although different studies have focused on developing a predictive model for customer churn under contractual settings, the mobile telecommunications industry, performing in a non-contractual setting in which customer churn is not easy to define and trace, has always been neglected in such investigations. In this study, we have developed a dual-step computer-assisted model in which a clustering model and a classification model are employed for defining and predicting customer churn. Results indicate the promising performance of the proposed models in identifying future churners.
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Item type Current library Call number Vol info Status Notes Date due Barcode
Journal Article Journal Article Main Library - Special Collections HF5415.13 JOU (Browse shelf(Opens below)) Vol.18, No.7, pages 587-598 Not for loan For in-house use only

The telecommunications industry with an approximate annual churn rate of 30% can nowadays be considered as one of the top sectors on the list of those suffering from customer churn. Although different studies have focused on developing a predictive model for customer churn under contractual settings, the mobile telecommunications industry, performing in a non-contractual setting in which customer churn is not easy to define and trace, has always been neglected in such investigations. In this study, we have developed a dual-step computer-assisted model in which a clustering model and a classification model are employed for defining and predicting customer churn. Results indicate the promising performance of the proposed models in identifying future churners.

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