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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