DocumentCode :
3470548
Title :
Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank´s VIP Customer Churn as the Example
Author :
Jing Zhao ; Xing-Hua Dang
Author_Institution :
Sch. of Bus. Adm., Xi´an Univ. of Technol., Xi´an
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank´s customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank´s VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank´s customer churn prediction.
Keywords :
bank data processing; customer relationship management; support vector machines; commercial bank VIP customer churn prediction; customer relationship management; support vector machine; Artificial neural networks; Bayesian methods; Customer relationship management; Decision trees; Large-scale systems; Logistics; Predictive models; Regression tree analysis; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.2509
Filename :
4680698
Link To Document :
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