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Abstract:
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 statistical techniques for churn prediction
and, based on these predictions, an optimal policy for identifying
customers to whom incentives should be offered to increase
retention. 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.
We show that under a wide variety of assumptions concerning the
cost of intervention and the retention rate resulting from
intervention, churn prediction and remediation can yield
significant savings to a carrier. We also show the importance of a
data representation crafted by domain experts.
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