An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach

In this study, we propose an intelligent customer relationship management (CRM) system that uses support vector machine (SVM) ensembles to help enterprise managers effectively manage customer relationship from a risk avoidance perspective. Different from the classical CRM for retaining and targeting profitable customers, the main focus of our proposed CRM system is to identify high-risk customers for avoiding potential loss. Through experiment analysis, we find that the Bayesian-based SVM ensemble data mining model with diverse components and "choose from space" selection strategy show the best performance over the testing samples.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[2]  Jonathan N. Crook,et al.  Credit Scoring and Its Applications , 2002, SIAM monographs on mathematical modeling and computation.

[3]  Kin Keung Lai,et al.  A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates , 2005, Comput. Oper. Res..

[4]  Kin Keung Lai,et al.  A Bias-Variance-Complexity Trade-Off Framework for Complex System Modeling , 2006, ICCSA.

[5]  Kin Keung Lai,et al.  Credit Risk Evaluation with Least Square Support Vector Machine , 2006, RSKT.

[6]  William B. Yates,et al.  Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.

[7]  Zhou Zhi,et al.  Neural Network Ensemble , 2002 .

[8]  Kin Keung Lai,et al.  Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model , 2006, ICANN.

[9]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Marc Toussaint,et al.  Extracting Motion Primitives from Natural Handwriting Data , 2006, ICANN.

[12]  Kin Keung Lai,et al.  A novel support vector machine metamodel for business risk identification , 2006 .

[13]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[14]  Kin Keung Lai,et al.  An integrated data preparation scheme for neural network data analysis , 2006, IEEE Transactions on Knowledge and Data Engineering.

[15]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2006, International Conference, Glasgow, UK, May 8-11, 2006, Proceedings, Part I , 2006, ICCSA.

[16]  Geoffrey I. Webb,et al.  PRICAI 2006: Trends in Artificial Intelligence, 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, August 7-11, 2006, Proceedings , 2006, PRICAI.