Combining Local and Social Network Classifiers to Improve Churn Prediction

Past research has shown that both social and local features are informative for customer churn, however some studies have found that combining both kinds of data into a single model is ineffective. People who churn based on their neighbors' behavior are a distinct subset of customers from those who churn for personal reasons. However, for an effective retention campaign, it is desired to identify both groups of likely churners, attempt to explain the factors that lead to churn in both cases, and still determine the customers most likely to churn so they can be contacted. The goal of this research is to evaluate different techniques for combining features and models based on customer attributes and customer social networks to identify the best approaches to deal with this problem.