Property Insurance Customer Classification And Lifetime Value Research Based On Hesitant Fuzzy Theory

The property insurance industry has experienced rapid growth in recent years,and it's market value has continued to increase.This has brought development opportunities for insurance companies as well as more severe competition and challenges.Customers are the direct source of corporate interests.Achieving better customer relationship management can greatly enhance the ability of companies to cope with market risks.Customer classification and lifetime value assessment are the basic functions and core objectives of customer relationship management.Customer classification runs through the entire process of customer relationship management and is one of the foundations of a customer's lifetime value assessment.Therefore,this paper focuses on the study of customer classification and customer lifetime value assessment,and provides advice on how to build a customer relationship management system from qualitative and quantitative aspects.Combining the main features of property insurance clients and the important factors of their lifetime value assessment,we use the RFM model to determine indicators.The RFM model can measure the lifetime value of the property insurance customers from the three dimensions such as the degree of nearness,frequency,and value.But due to the special nature of the property and casualty insurance industry,the amount of claims constitutes the most cost and risk.It is also an important factor in reducing the lifetime value of customers.Therefore,this article adds the claim amount indicator C(indicating the amount of claims incurred by a certain customer in a certain period of time)to the RFM model to expands to RFMC model,which is more objective and diverse.Data mining technology provides powerful method support for customer classification.However,there is a large amount of uncertain information in massive information.Hard classification technology can easily cause information loss.Therefore,this paper focuses on how to combine hesitant fuzzy set theory with classification techniques to achieve hesitant fuzzy clustering,and explore the improvement of hesitant fuzzy entropy in the determination of attribute weights.This paper firstly uses the random forest algorithm to analyze customers from three aspects of customer life cycle,value level,and risk level,and obtains each customer's class attribute.Secondly,the hesitant fuzzy set theory is used to process the original data,and the hesitant fuzzy decision matrix is obtained.The heuristic fuzzy information-based network clustering is proposed to obtain four types of customer groups,and compared with the K-Means clustering effect,which shows that the paper proposes Hesitant fuzzy clustering results in higher accuracy.Finally,calculate the lifetime value score of each type of property insurance client,and provide targeted management suggestions based on the score.