Social network analysis for customer churn prediction

• We examine the use of social network information for customer churn prediction.
• We develop an alternative modeling approach using relational learning algorithms.
• We present the results of two large scale real life case studies in the telco industry.
• A large group of classifiers yields comparable performance.
• A significant impact of (higher order) social network effects on the performance of a customer churn prediction model is found.

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.