The identification and comprehension of customers’ behavior is of crucial importance in current business environments. Data mining plays an important role in this field, and has been applied widespread for e.g. prediction of churn and response. Social network data has become very valuable for marketing purposes. Addressing network neighbors of current customers can therefore be a very efficient marketing strategy. This idea of using network learners for response modeling has already been successfully applied in literature. However, until now such analyses were largely limited to domains where an explicit social network is available, such as online friendship communities or the telecommunication domain. In this research, we will look for proxies of social ties to build pseudo social network variables in a retail and B2B setting. Concretely, we will enrich existing data with pseudo social network data. Using these enriched datasets, we shall investigate whether adding social network data improves the predictive performance of churn prediction models.