Title: Multi-agent system for customer relationship management with SVMs tool

Authors: Yanshan Xiao, Bo Liu, Dan Luo, Longbing Cao, Feiqi Deng, Zhifeng Hao

Addresses: Faculty of Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia. ' Faculty of Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia. ' Faculty of Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia. ' Faculty of Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia. ' College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, P.R. China. ' College of Computer Science, GuangDong University of Technology, Guangzhou, P.R. China

Abstract: In this paper, we introduce multiple agents, knowledge discovery and data mining into customer relationship management (CRM) to set up the architecture of a multi-agent-based CRM system (MAB-CRM), and then use the SVMs-based approach to build up the decision support model which can classify the patterns obtained by the multiple agents into several decision levels, so that managers can pursue different decision-making activities according to the decision level of a pattern. Substantial experiments in the two-dimensional space show how the SVMs-based approach works. The practical problem from one Chinese company has been resolved by the SVMs-based approach. The results illustrate that this approach has an effective ability to learn the decision rules from the assessors| experience.

Keywords: customer relationship management; CRM; KDD; support vector machines; SVM; multi-agent systems; MAS; agent-based systems; multiple agents; knowledge discovery; data mining; decision support systems; DSS; modelling; China.

DOI: 10.1504/IJIIDS.2010.032438

International Journal of Intelligent Information and Database Systems, 2010 Vol.4 No.2, pp.121 - 136

Published online: 02 Apr 2010 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article