Insurance product recommendation method and device, computer equipment and storage medium

Abstract

The application relates to data warehouse-based data processing technology, and provides an insurance product recommendation method and device, computer equipment and a storage medium. The method includes: acquiring user basis data and insurance product data corresponding to a user identifier; determining, according to the insurance product data, an insurance product type corresponding to the useridentifier and an insurance product buying data corresponding to the insurance product type; determining insurance feature data according to the insurance product type and the insurance product buying data; inputting the user basis data and the insurance feature data to a trained insurance label prediction model to carry out prediction to obtain corresponding insurance label information; and determining a recommended insurance product according to the insurance label information. By adopting the method, recommendation accuracy and recommendation efficiency of the insurance product can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an insurance product recommendation method, device, computer equipment and storage medium. Background technique [0002] Insurance is life insurance, and its main function is to provide economic security for the insured when the insured is old, or when an accident or disease occurs. With the continuous improvement of living standards, more and more users purchase insurance products, and there are more and more insurance products to choose from. Salespersons usually actively recommend insurance products to users through phone calls, text messages, emails, and face-to-face interviews. If the salesperson blindly recommends insurance products to users, it will not only waste manpower and material resources, but also cause dissatisfaction among users. It can be seen that how to determine the insurance products recommended to users is a problem worth considering. [0003...

AI Technical Summary

Problems solved by technology

[0003] At present, the salesperson usually determines the recommended insurance product based on the existing user data based on his own experience, but this recommendation method is limited by the salesperson's own experience, so the determined insurance product may not be suitable for the corresponding user. That is, the recommendation accuracy of insurance products is low

Moreover, when the number of insurance products and the amount of user data are large enough, the salesperson needs to spend a lot of time on analysis to determine the recommended insurance products, so there is a problem of low recommendation efficiency

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