Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction

Kristof Coussement () and Dirk Van den Poel ()

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: 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 identify customers most prone to switch. Consequently, their customer retention campaigns can be targeted effectively because the prediction method is better at detecting those customers who are likely to leave.

Keywords: customer relationship management (CRM); data mining; churn prediction; text mining; call center email; voice of customers (VOC); binary classification modeling (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ict and nep-mkt
Date: Written

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