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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
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