Churn Reduction in the Wireless Industry
Authors: Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky Michael Mozer
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: 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 


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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.