Academic Papers about Text
Mining for Analytical Customer Relationship Management (CRM) / Business
Intelligence in Marketing / Customer Intelligence / Predictive Analytics /
Customer Data Mining
by the Modeling
Cluster of the
at Ghent University,
(Prof. Dr. Dirk
Van den Poel)
Updated
March 20th, 2008
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on paper titles to obtain the full electronic version (then continue by
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NEW – COUSSEMENT
K., VAN DEN POEL D. (2008), Integrating the
Voice of Customers through Call Center Emails into a Decision Support System
for Churn Prediction, Forthcoming in Information
and Management
Abstract: We
studied the problem of optimizing the performance of a DSS for churn
prediction. In particular, we investigated the beneficial effect of adding the
voice of customers through call center emails – i.e.
textual information - to a churn prediction system that only uses traditional
marketing information. We found that adding unstructured, textual information
into a conventional churn prediction model resulted in a significant increase
in predictive performance. From a managerial point of view, this integrated
framework helps marketing-decision makers to identify customers most prone to
switch. Consequently, their customer retention campaigns can be targeted
effectively because the prediction method is better at detecting those customers
who are likely to leave.
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COUSSEMENT
K., VAN DEN POEL D. (2008), Improving
Customer Complaint Management by Automatic Email Classification Using
Linguistic Style Features as Predictors, Forthcoming in Decision Support Systems
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.
The Department of Marketing of Ghent University
offers a Master of Marketing Analysis, which is a one-year full-time degree
(from October – June) specializing in CRM and market(ing)
research and marketing communications. See http://www.mma.UGent.be for more information. A complete list of
publications related to analytical CRM or customer intelligence can be obtained
from http://www.crm.UGent.be