Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

Competition in the wireless telecommunications industry is rampant. To maintain profitability, wireless carriers must control churn, the loss of subscribers who switch from one carrier to another. We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives that should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include: logit regression, decision trees, neural networks, and boosting. Our experiments are based on a data base of nearly 47,000 U.S. domestic subscribers, and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offer incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques which validate our simulation experiments.

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