Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors
AbstractCustomer 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.
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Bibliographic InfoPaper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 07/481.
Length: 35 pages
Date of creation: Sep 2007
Date of revision:
Customer Complaint Handling; Call Center Email; Voice of Customers (VOC); Singular Value Decomposition (SVD); Latent Semantic Indexing (LSI); Automatic Email Classification;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-10-20 (All new papers)
- NEP-ICT-2007-10-20 (Information & Communication Technologies)
- NEP-MKT-2007-10-20 (Marketing)
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- 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.
- Text mining in Wikipedia (English)
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