Abstract
Indian cellular phone market is reaching to its natural saturation with tele-density of 89.12. Subscriber's behaviour has become unpredictable and they seek best alternative among many service providers (SP's) with varied offerings. Mobile number portability (MNP) further facilitates switching; causing great revenue loss to the SP's and hence needs to be checked proactively. Industry practitioners maintain huge database containing thousands of variables to be used in their predictive models. This study attempts to identify the variables which might help in predicting attrition by fitting a logistic regression with each individual predictor and judging the usability with Wald Statistics. CART decision tree also suggests similar findings. This descriptive study finds that prior knowledge of 'n-important' variables for model building can greatly reduce the effort, time and cost. Real-time data from geographical cross-section could corroborate the reduction of variables. These variables can be further investigated in future research, while developing any predictive model for customer attrition in cellular service subscription.
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