Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors
Kristof Coussement () and
Dirk Van den Poel ()
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration
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.
Keywords: Customer Complaint Handling; Call Center Email; Voice of Customers (VOC); Singular Value Decomposition (SVD); Latent Semantic Indexing (LSI); Automatic Email Classification (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ict and nep-mkt
References: Add references at CitEc
Citations View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:rug:rugwps:07/481
Access Statistics for this paper
More papers in Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration Contact information at EDIRC.
Series data maintained by Nathalie Verhaeghe ().