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

Author

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  • K. Coussement

    (LEM - Lille - Economie et Management - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • 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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • K. Coussement & D. van den Poel, 2008. "Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors," Post-Print hal-00788087, HAL.
    • Handle: RePEc:hal:journl:hal-00788087
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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. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    3. Jae-hyuck Lee & HaeOk Choi, 2020. "An Analysis of Public Complaints to Evaluate Ecosystem Services," Land, MDPI, vol. 9(3), pages 1-11, February.
    4. Yan, Nina & Xu, Xun & Tong, Tingting & Huang, Liujia, 2021. "Examining consumer complaints from an on-demand service platform," International Journal of Production Economics, Elsevier, vol. 237(C).
    5. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    6. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    7. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    8. 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, vol. 8(12), pages 1-21, December.
    9. Vairetti, Carla & Aránguiz, Ignacio & Maldonado, Sebastián & Karmy, Juan Pablo & Leal, Alonso, 2024. "Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1108-1118.
    10. HaeOk Choi, 2020. "Geospatial Data Approach for Demand-Oriented Policies of Land Administration," Land, MDPI, vol. 9(1), pages 1-12, January.
    11. Piera Centobelli & Roberto Cerchione & Emilio Esposito & Shashi, 2020. "Evaluating environmental sustainability strategies in freight transport and logistics industry," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1563-1574, March.
    12. 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.
    13. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.

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