We studied the problem of optimizing the performance of a DSS for churn prediction. In particular, we investigated the beneficial effect of adding the voice of customers through call center emails - i.e. textual information - to a churn-prediction system that only uses traditional marketing information. We found that adding unstructured, textual information into a conventional churn-prediction model resulted in a significant increase in predictive performance. From a managerial point of view, this integrated framework helps marketing-decision makers to better identify customers most prone to switch. Consequently, their customer retention campaigns can be targeted more effectively because the prediction method is better at detecting those customers who are likely to leave.