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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (4): 83-90    DOI: 10.11925/infotech.2096-3467.2019.0715
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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
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[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.

Key wordsRFM Model      SOM Neural Network      Customer Classification      Marketing Strategy     
Received: 19 June 2019      Published: 01 June 2020
ZTFLH:  F840.32  
Corresponding Authors: Liu Lu     E-mail:

Cite this article:

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.

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指标体系 细分类别
消费近度(R) R1:某客户购买非寿险产品近度与全部客户购买非寿险产品平均近度之比
消费频度(F) F1:某客户购买非寿险产品次数与全部客户平均购买非寿险产品次数之比
消费值度(M) M1:某客户购买非寿险产品总额与全部客户平均消费总额之比
理赔金额(C) C1:某客户所获理赔金额与全部客户所获平均理赔金额之比
Index System of Non-life Insurance Customer Segmentation
Structure of SOM Neural Network
Training Process of Improved SOM Neural Network Model
指标 ω1 ω2
R 0.537 0.463
F 0.476 0.524
M 0.569 0.431
C 1.000 0.000
Weights of Classifying Index of RFMC Model
Distance Between Adjacent Neurons
Classification of Each Neuron
Comparison of Three Models’ Adaptive Time
非寿险客户类别 客户簇编号 R F M C
重要保持客户 1,3,6
重要发展客户 5,7,9
一般重要客户 2,4
重要挽留客户 12,14,15
一般客户 8,13
无价值客户 11,16
高风险客户 10
Classification Label Corresponding to the Category and Purchase Behavior
[1] 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.
[6] 杜修平, 王中 . 基于决策树的证券客户流失模型[J]. 计算机应用与软件, 2009,26(9):230-233.
[6] ( Du Xiuping, Wang Zhong . The Stock Customers Churn Model Based on Decision Tree[J]. Computer Applications and Software, 2009,26(9):230-233.)
[7] 王文贤, 金阳, 陈道斌 . 基于RFM模型的个人客户忠诚度研究[J]. 金融论坛, 2012(3):75-80.
[7] ( Wang Wenxian, Jin Yang, Chen Daobin . A Study of Personal Customer Loyalty Based on RFM Model[J]. Financial Forum, 2012(3):75-80.)
[8] 赵萌, 齐佳音 . 基于购买行为RFM及评论行为RFMP模型的客户终身价值研究[J]. 统计与信息论坛, 2014,29(9):91-98.
[8] ( 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.)
[9] 熊兰, 高炳 . 基于RFM多层级客户价值模型的客户细分研究[J]. 商业经济研究, 2017(5):55-57.
[9] ( 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
[13] 王晓国, 黄韶坤, 朱炜 , 等. 应用C4.5算法构造客户分类决策树的方法[J]. 计算机工程, 2003,29(14):89-91.
[13] ( 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.)
[14] 林盛, 肖旭 . 基于RFM的电信客户市场细分方法[J]. 哈尔滨工业大学学报, 2006,38(5):758-760.
[14] ( 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].
[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
[19] 吴春旭, 鲍满园, 苟清龙 . 自组织映射聚类算法在电信客户细分中的应用[J]. 计算机系统应用, 2010,19(8):168-172.
[19] ( 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.)
[20] 杨于峰, 余伟萍, 田盼 . 基于SOM神经网络的品牌丑闻微博传播分类预测研究[J]. 情报杂志, 2013,32(10):23-28.
[20] ( 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.)
[21] 张连增, 王缔 . 保险大数据条件下车险费率厘定的研究——基于SOM神经网络方法的车险索赔强度建模[J]. 保险研究, 2018(9):56-65.
[21] ( 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.)
[22] 陈伯成, 梁冰, 周越博 , 等. 自组织映射神经网络(SOM)在客户分类中的一种应用[J]. 系统工程理论与实践, 2004,24(3):8-14.
[22] ( 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.)
[23] 刘铭, 张双全, 何禹德 . 基于改进SOM神经网络的异网电信用户细分研究[J]. 广西师范大学学报: 自然科学版, 2018,36(3):21-28.
[23] ( 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.)
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