Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models
Yan Chun1,2,Liu Lu1()
1 College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China 2 College of Mathematics and System, Shandong University of Science and Technology, Qingdao 266590, China
[Objective] This paper uses neural network algorithms to classify non-life insurance customers, aiming to realize precision marketing. [Methods] We modified the RFM model from the macro and micro perspectives, and then introduced the index of claim amounts to establish the RFMC model. Then, we dynamically set the training speed and weight vector of the SOM neural network model. Finally, we improved the convergence speed of the proposed model and finished customer classification. [Results] We examined our model with information on non-life insurance customers. The proposed model was stable and its self-organization speed increased by 21.6%. [Limitations] All of the non-life insurance customers were from the same insurance company. [Conclusions] This paper divides non-life insurance customers into seven categories, and proposes different strategies for each type, which effectively improve the marketing decisions.
闫春,刘璐. 基于改进SOM神经网络模型与RFM模型的非寿险客户细分研究*[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
Yan Chun,Liu Lu. Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models. Data Analysis and Knowledge Discovery, 2020, 4(4): 83-90.
Hushes A M . Strategic Database Marketing: The Master Plan for Starting and Managing a Profitable Customer-Based Marketing Program[M]. New York: McGraw-Hill, 2000.
[2]
Chang H C, Tsai H P . Group RFM Analysis as a Novel Framework to Discover Better Customer Consumption Behavior[J]. Expert Systems with Applications, 2011,38(12):14499-14513.
[3]
Khobzi H, Akhondzadeh-Noughabi E, Minaei-Bidgoli B . A New Application of RFM Clustering for Guild Segmentation to Mine the Pattern of Using Banks’ e-Payment Services[J]. Journal of Global Marketing, 2014,27(3):178-190.
doi: 10.1080/08911762.2013.878428
[4]
Dursun A, Caber M . Using Data Mining Techniques for Profiling Profitable Hotel Customers: An Application of RFM Analysis[J]. Tourism Management Perspectives, 2016,18:153-160.
doi: 10.1016/j.tmp.2016.03.001
[5]
Yan C, Sun H, Liu W , et al. An Integrated Method Based on Hesitant Fuzzy Theory and RFM Model to Insurance Customers’ Segmentation and Lifetime Value Determination[J]. Journal of Intelligent & Fuzzy Systems, 2018,35(1):159-169.
( Zhao Meng, Qi Jiayin . The Research of Customer Lifetime Value Based on the Combination of Customer Purchase’s RFM and Customer Online Review’s RFMP[J]. Statistics & Information Forum, 2014,29(9):91-98.)
( Xiong Lan, Gao Bing . Research on Customer Segmentation Based on RFM Multi-Level Customer Value Model[J]. Journal of Commercial Economics, 2017(5):55-57.)
[10]
MacQueen J. Some Methods for Classification and Analysis of Multivariate Observations[C]// Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability. 1967: 281-297.
[11]
Quinlan J R . Induction of Decision Trees[J]. Machine Learning, 1986,1(1):81-106.
[12]
Kohonen T . Self-organized Formation of Topologically Correct Feature Maps[J]. Biological Cybernetics, 1982,43(1):59-69.
doi: 10.1007/BF00337288
( Wang Xiaoguo, Huang Shaokun, Zhu Wei , et al. Method of Building Decision Trees of Customer Classification by Using C4.5 Algorithm[J]. Computer Engineering, 2003,29(14):89-91.)
( Lin Sheng, Xiao Xu . A Method of Telecom Customer Market Segmentation Based on RFM Model[J]. Journal of Harbin Institute of Technology, 2006,38(5):758-760.)
[15]
Kiang M Y . Extending the Kohonen Self-organizing Map Networks for Clustering Analysis[J]. Computational Statistics & Data Analysis, 2001,38(2):161-180.
[16]
Lee S C K, Lin X S. Delta Boosting Machine and Its Application in Actuarial Modeling[R/OL]. https://www.soa.org/globalassets/assets/Files/Research/Projects/delta-boosting-report.pdf.
[17]
Wuthrich M V . Covariate Selection from Telematics Car Driving Data[J]. European Actuarial Journal, 2017,7(1):89-108.
doi: 10.1007/s13385-017-0149-z
[18]
Yanık S, Elmorsy A . SOM Approach for Clustering Customers Using Credit Card Transactions[J]. International Journal of Intelligent Computing and Cybernetics, 2019,12(3):372-388.
doi: 10.1108/IJICC-11-2018-0157
( Wu Chunxu, Bao Manyuan, Gou Qinglong . Application of Self Organization Map to Classification of the Telecommunication Company[J]. Computer Systems and Applications, 2010,19(8):168-172.)
( Yang Yufeng, Yu Weiping, Tian Pan . Classification and Prediction of Brand Scandal Spread on Microblogging Based on the SOM[J]. Journal of Intelligence, 2013,32(10):23-28.)
( Zhang Lianzeng, Wang Di . A Research on the Rate Making of Automobile Insurance with Big Data—Modeling of Automobile Insurance Claim Severity Based on SOM Neural Network[J]. Insurance Studies, 2018(9):56-65.)
( Chen Bocheng, Liang Bing, Zhou Yuebo , et al. An Application of SOM Neural Network in Customer Classification[J]. Systems Engineering—Theory & Practice, 2004,24(3):8-14.)
( Liu Ming, Zhang Shuangquan, He Yude . Classification Study of Differential Telecom Users Based on SOM Neural Network[J]. Journal of Guangxi Normal University: Natural Science Edition, 2018,36(3):21-28.)