Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)
Michael C. Mozer, Richard Wolniewicz, David Grimes, Eric Johnson, Howard Kaushansky
Competition in the wireless telecommunications industry is rampant. To main(cid:173) tain profitability, wireless carriers must control chum, the loss of subscribers who switch from one carrier to another. We explore statistical techniques for chum prediction and, based on these predictions. an optimal policy for identify(cid:173) ing 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 subscrib(cid:173) ers, 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, chum prediction and remediation can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Competition in the wireless telecommunications industry is rampant. As many as seven competing carriers operate in each market. The industry is extremely dynamic, with new services, technologies, and carriers constantly altering the landscape. Carriers announce new rates and incentives weekly, hoping to entice new subscribers and to lure subscribers away from the competition. The extent of rivalry is reflected in the deluge of advertise(cid:173) ments for wireless service in the daily newspaper and other mass media. The United States had 69 million wireless subscribers in 1998, roughly 25% of the population. Some markets are further developed; for example, the subscription rate in Fin(cid:173) land is 53%. Industry forecasts are for a U.S. penetration rate of 48% by 2003. Although there is significant room for growth in most markets, the industry growth rate is declining and competition is rising. Consequently, it has become crucial for wireless carriers to con(cid:173) trol chum-the loss of customers who switch from one carrier to another. At present, domestic monthly chum rates are 2-3% of the customer base. At an average cost of $400 to acquire a subscriber, churn cost the industry nearly $6.3 bilIion in 1998; the total annual loss rose to nearly $9.6 billion when lost monthly revenue from subscriber cancellations is considered (Luna, 1998). It costs roughly five times as much to sign on a new subscriber as to retain an existing one. Consequently, for a carrier with 1.5 milIion subscribers, reduc(cid:173) ing the monthly churn' rate from 2% to 1 % would yield an increase in annual earnings of at least $54 milIion, and an increase in shareholder value of approximately $150 million. (Estimates are even higher when lost monthly revenue is considered; see Fowlkes, Madan, Andrew, & Jensen, 1999; Luna, 1998.) The goal of our research is to evaluate the benefits of predicting churn using tech(cid:173) niques from statistical machine learning. We designed models that predict the probability 936 M. C. Mozer, R. Wolniewicz. D. B. Grimes. E. Johnson and H. Kaushansky of a subscriber churning within a short time window, and we evaluated how well these pre(cid:173) dictions could be used for decision making by estimating potential cost savings to the wireless carrier under a variety of assumptions concerning subscriber behavior.