Improving customer complaint management by automatic email classification using linguistic style features as predictors

Authors:
Kristof Coussement;Dirk Van den Poel
Affiliations:
Ghent University, Faculty of Economics and Business Administration, Department of Marketing, Tweekerkenstraat 2, 9000 Ghent, Belgium;Ghent University, Faculty of Economics and Business Administration, Department of Marketing, Tweekerkenstraat 2, 9000 Ghent, Belgium
Venue:
Decision Support Systems
Year:
2008

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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.