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3G环境下基于客户价值分类的电信客户流失预测研究
Research on Telecom Customer Churn Prediction Based on Customer Value Classification in 3G Environment

DOI: 10.12677/HJDM.2016.61004, PP. 28-36

Keywords: 客户流失,数据挖掘,决策树,混淆矩阵
Customer Churn
, Data Mining, Decision Tree, Confusion Matrix

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Abstract:

电信客户流失问题是电信运营商面临的迫切需要解决的问题。本文针对3G环境下,根据客户三个月平均消费水平进行客户价值划分,综合运用数据挖掘中决策树算法和聚类算法进行建模,引入混淆矩阵对模型进行评估,利用模型输出的规则集有针对性的进行流失客户维系营销,从而达到降低客户流失,提高营销效率,提升电信运营商核心竞争力的目的。
Telecom operators are facing an urgent problem of telecom customer churn that should be solved as soon as possible. This paper, according to the three-month average customer consumption, di-vides the levels of customer value, comprehensively uses decision tree algorithm and clustering algorithm modeling of data mining, introduces confusion matrix model for model evaluation, and uses the model output rules set for targeted customers’ maintaining marketing, so as to reduce customer churn, improve the efficiency of marketing, and enhance the core competitiveness of telecom operators in 3G environment.

References

[1]  许恺. 基于数据挖掘技术的电信客户流失预测[J]. 电脑知识与技术, 2009, 5(13): 3437-3438.
[2]  熊平. 数据挖掘算法与Clementine实践[M]. 北京: 清华大学出版社, 2011.
[3]  夏国恩. 客户流失预测的现状与发展研究[J]. 计算机应用研究, 2010, 27 (2): 413-416.
[4]  Sato, T., Huang, B.Q., Huang, Y., Kechadi, M.-T. and Buckley, B. (2010) Using PCA to Predict Customer Churn in Telecommunication Dataset. Lecture Notes in Computer Science, 6132.
http://dx.doi.org/10.1007/978-3-642-13217-9
[5]  Cox Jr., L.A. (2002) Data Mining and Causal Modeling of Customer Behaviors. Telecommunication Systems, 21, 349- 381.
[6]  Rosset, S. and Neumann, E. (2003) Integrating Customer Value Considerations into Predictive Modeling. Proceedings of the 3rd IEEE International Conference on Data Mining, Washington DC, 19-22 November 2003, 283-290.
[7]  Wojewnik, P., Kaminski, B., Zawisza, M. and Antosiewicz, M. (2011) Social-Network Influence on Telecommunication Customer Attrition. Lecture Notes in Com-puter Science, 6682, 64-73.
[8]  Mozer, M.C, Wolniewicz, R., Grimes, D.B., Johnson, E. and Kaushansky, H. (2000) Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry. IEEE Transactions on Neural Networks, 11, 690-696.
[9]  郭亮. 用CRISP-DM模型来规范企业数据中心建设[J]. 华北科技学院学报, 2008, 5(4): 69-72.
[10]  龙志勇. 数据挖掘在电信行业客户关系管理中的应用[J]. 信系网络, 2003(12): 24-26.
[11]  袁玉波. 数据挖掘与最优化技术及其应用[M]. 北京: 科学出版社, 2007.
[12]  李如平. 数据挖掘中决策树分类算法的研究[J]. 东华理工大学学报(自然科学版), 2010, 33(2): 192-196.
[13]  梁循. 数据挖掘算法与应用[M]. 北京: 北京大学出版社, 2006.
[14]  薛薇. 基于Clementine的数据挖掘[M]. 北京: 中国人民大学出版社, 2012.
[15]  Dunhan, M.H. 数据挖掘教程[M]. 北京: 清华大学出版社, 2005.
[16]  Han, J.W. and Kamber, M., 著. 数据挖掘: 概念与技术[M]. 第二版. 范明, 孟小峰, 译. 北京: 机械工业出版社, 2007: 147-154.

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