This study examines the use of social network information for customer churn prediction. An alternative modeling approach using relational learning algorithms is developed to incorporate social network effects within a customer churn prediction setting, in order to handle large scale networks, a time dependent class label, and a skewed class distribution. An innovative approach to incorporate non-Markovian network effects within relational classifiers and a novel parallel modeling setup to combine a relational and non-relational classification model are introduced. The results of two real life case studies on large scale telco data sets are presented, containing both networked (call detail records) and non-networked (customer related) information about millions of subscribers. A significant impact of social network effects, including non-Markovian effects, on the performance of a customer churn prediction model is found, and the parallel model setup is shown to boost the profits generated by a retention campaign. 
Social network analysis for customer churn prediction