Application of Data Mining Technology in the Loss of Customers in Automobile Insurance Enterprises
International Journal of Data Science and Analysis
Volume 4, Issue 1, February 2018, Pages: 1-5
Received: Nov. 8, 2017;
Accepted: Dec. 4, 2017;
Published: Jan. 15, 2018
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Han Song, Department of Statistics, Beijing Wuzi University, Beijing, China
Han Qiuhong, Department of Statistics, Beijing Wuzi University, Beijing, China
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This paper is based on the customer churn data of auto insurance, construction of index system in three aspects: the customer information, the subject matter of the insurance information and hold product information; This paper uses decision tree and Logistic regression model to analyze the insurance company's customer data; The results show that: discount, total discount rate, total premium and other variables have a significant impact on customer churn, and get the loss probability of each customer and get some main features of lost customers.
Customer Churn, Decision Tree, Logistic Regression, Auto Insurance Company
To cite this article
Application of Data Mining Technology in the Loss of Customers in Automobile Insurance Enterprises, International Journal of Data Science and Analysis.
Vol. 4, No. 1,
2018, pp. 1-5.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Liu Yunbo. Evolution: from supply and demand chain to the ecology, http: //www.e-prot.cn/gmxx/itzx/352.thm.
LOUIS A C. Data mining and causal modeling of customer [J]. Telecommunication Systems, 2002, 21 (2): 103-112.
YANG Zi-jiang, WANG Ye, MA Tian-yi .Analysis of the Factors Affecting the Reinsurance Rate of Auto Insurance [J]. Business Research, 2011, 107.
Liang Wuchao, Wang Ying, Wang Shuxia. Research on Win - win Strategy of Customer Missing Based on Fuzzy Analytic Hierarchy Process [J]. Management Manager, 2017.
ZHU Zhi-yong, XU Chang-mei, HU Chen-gang. Analysis of Customer Churn Based on Bayesian Networks [J]. Journal of Computer Engineering and Design, 2013,35 (3): 155-158.
Ding Junmei, Liu Guicheng, Li Hui. Application of Improved Stochastic Forest Algorithm in Prediction of Customer Missing in Telecommunication Industry [J]. Research and Application. 2015.
Gui Xiancai, Peng Hong, Wang Xiaohua. Analysis of insurance customers churn based on decision tree [J]. Computer Engineering and Design.2005.
Tian Chong. Data mining technology in China's automobile insurance industry research [D]. Hubei: Wuhan University of Technology master's degree thesis, 2007.
Zheng Yuchen, Lv Wangyong. Early warning analysis of loss of securities firms based on Logistic model [J]. Journal of Zhengzhou Institute of Aeronautical Industry Management. 2016,34 (5): 80-88.
Wang Jichuan, Guo Zhigang. Logistic Regression Model-Methods and Applications [M]. Higher Education Press.
Wang Lei, Chen Songlin, GuXuedao. Customer Missing Early Warning Model and Application in Telecommunication Enterprises [J]. Telecom Operation Support, 2006.
Zhang Liangjun, XieJiabiao, Yang Tan, Xiao Gang. R and Data Mining [M]. Beijing: Mechanical Press, 2016.
Gareth James, Daniela Witten, Trevor Hastie. Introduction to Statistical Learning - Based on R Applications [M]. Wang Xing, translated. Beijing: Mechanical Industry Press, 2016.
DENG Shu-fang. Construction of Portfolio Model for Personal Credit Evaluation Based on Decision Tree - Neural Network [D]. Hunan: Master's Thesis, Hunan University, 2012.