Academic Papers about Text Mining for Analytical Customer Relationship Management (CRM) / Business Intelligence in Marketing / Customer Intelligence / Predictive Analytics / Customer Data Mining

by the Modeling Cluster of the

Department of Marketing

at Ghent University, Belgium

(Prof. Dr. Dirk Van den Poel)


Updated March 20th, 2008


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n      NEW – COUSSEMENT K., VAN DEN POEL D. (2008), Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction, Forthcoming in Information and Management

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.


n      COUSSEMENT K., VAN DEN POEL D. (2008), Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, Forthcoming in Decision Support Systems

Abstract: Customer complaint management is becoming a critical key success factor in today’s business environment. This study introduces a methodology to improve complaint handling strategies through an automatic email classification system that distinguishes complaints from non-complaints. As such, complaint handling becomes less time-consuming and more successful. The classification system combines traditional text information with new information about the linguistic style of an email. The empirical results show that adding linguistic style information into a classification model with conventional text-classification variables results in a significant increase in predictive performance. In addition, this study reveals linguistic style differences between complaint emails and others.




The Department of Marketing of Ghent University offers a Master of Marketing Analysis, which is a one-year full-time degree (from October – June) specializing in CRM and market(ing) research and marketing communications. See  for more information. A complete list of publications related to analytical CRM or customer intelligence can be obtained from