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Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors

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  • K. COUSSEMENT

    ()

  • D. VAN DEN POEL

    ()

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.

Suggested Citation

  • K. Coussement & D. Van Den Poel, 2007. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/481, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:07/481
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    File URL: http://wps-feb.ugent.be/Papers/wp_07_481.pdf
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    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. Weng-Kun Liu & Chia-Chun Yen, 2016. "Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management," Sustainability, MDPI, Open Access Journal, vol. 8(12), pages 1-21, December.
    3. Stefan Debortoli & Oliver Müller & Jan Brocke, 2014. "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 289-300, October.

    More about this item

    Keywords

    Customer Complaint Handling; Call Center Email; Voice of Customers (VOC); Singular Value Decomposition (SVD); Latent Semantic Indexing (LSI); Automatic Email Classification;

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