Integrating the voice of customers through call center emails into a decision support system for chum prediction
We studied the problem of optimizing the performance of a DSS for chum prediction. In particular, we investigated the beneficial effect of adding the voice of customers through call center emails - i.e. textual information - to a chum-prediction system that only uses traditional marketing information. We found that adding unstructured, textual information into a conventional chum-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. (c) 2008 Elsevier B.V. All rights reserved.
call center email chum prediction customer relationship management (crm) data mining text mining voice of customers (voc) binary classification modeling