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Text clustering and summary techniques for CRM message management

Text clustering and summary techniques for CRM message management One of customer relationship management (CRM) activities involves soliciting customer feedback on product and service quality and the resolution of customer complaints. Inevitably, companies must deal with large number of CRM messages from their customers either through e‐mails or from work logs. Going through those messages is an important but tedious task for managers or CRM specialists in order to make strategic plans on where to place the resources to achieve better CRM results. In this paper, we present a methodology for making sense out of CRM messages based on text clustering and summary techniques. The unique features of CRM messages are the short message length and frequent availability of correlated CRM ratings. We propose several novel techniques including organizational concept space, Web mining of similarity relationships between concepts, and correlated analysis of text and ratings. We have tested the basic concepts and techniques of CRM Sense Maker in a business setting where customer surveys are used to set strategic directions in customer services. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Enterprise Information Management Emerald Publishing

Text clustering and summary techniques for CRM message management

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
1741-0398
DOI
10.1108/17410390410566715
Publisher site
See Article on Publisher Site

Abstract

One of customer relationship management (CRM) activities involves soliciting customer feedback on product and service quality and the resolution of customer complaints. Inevitably, companies must deal with large number of CRM messages from their customers either through e‐mails or from work logs. Going through those messages is an important but tedious task for managers or CRM specialists in order to make strategic plans on where to place the resources to achieve better CRM results. In this paper, we present a methodology for making sense out of CRM messages based on text clustering and summary techniques. The unique features of CRM messages are the short message length and frequent availability of correlated CRM ratings. We propose several novel techniques including organizational concept space, Web mining of similarity relationships between concepts, and correlated analysis of text and ratings. We have tested the basic concepts and techniques of CRM Sense Maker in a business setting where customer surveys are used to set strategic directions in customer services.

Journal

Journal of Enterprise Information ManagementEmerald Publishing

Published: Dec 1, 2004

Keywords: Knowledge management; Customer relations; Information exchange; Worldwide web

References