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.
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