Information pushing method and device based on deep learning, electronic equipment and medium

Abstract

The embodiment of the invention discloses an information pushing method and device based on deep learning, electronic equipment and a medium. The method comprises the steps: acquiring the insurance related information of a first user who is out of insurance within a specified time and does not purchase an insurance product after the insurance is out of insurance; acquiring the insurance feature information corresponding to an insurance index according to the insurance related information; importing the insurance feature information into a trained target network model to obtain a target insurance grade of the first user; if the target insurance grade is greater than a preset insurance grade, determining target insurance product information of which the number of effective browsing times exceeds a preset number of times of the first user according to the insurance-related information; acquiring user information of the first user; and finally, sending the user information and the target insurance product information to user equipment of the target user. According to the method, the potential insured customers with higher insured levels are screened out from the insured customers, and the potential insured customers effectively browse more insurance product information to push to the product customer manager, so that the insurance marketing success rate and efficiency are improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an information push method, device, electronic device and medium based on deep learning. Background technique [0002] With the rapid development of the national economy, the support of relevant policies and the further upgrading of the awareness of universal insurance, the growth trend of domestic insurance demand is very obvious. However, insurance companies have many difficulties in remarketing to out-of-insurance customers. Out-of-insurance customers may be dissatisfied with some of the company's services or quotations, etc. When marketing to these customers, it takes more energy and cost to keep customers insured. [0003] In the traditional marketing mode, marketers need to visit customers multiple times and offer more favorable prices to attract customers to purchase. Based on this situation, the current selection of high-quality renewable c...

AI Technical Summary

Problems solved by technology

Based on this situation, the current selection of high-quality renewable customers for such out-of-insurance customers is mainly based on traditional classification algorithms, such as SVM, KNN, etc. The lack of data will lead to inaccurate high-quality out-of-insurance customers. Make marketing success rate and insurance marketing inefficient

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