User behavior modeling control method and device based on big data

Abstract

The invention provides a user behavior modeling control method based on big data. The method comprises the steps of a. calculating a principal component index zi based on a linear combination formula zi=Li1x1+Li2x2+......Likxk; b. obtaining information amount <lambda>i, captured by principal component variables corresponding to the principal component index zi, of a user behavior index variable set X; c. determining the number N of the principal component variables based on a threshold value <theta>, and (img file as shown in the description); and d. calculating a behavior intuitive index I based on a formula I=F(z1, z2, z3 ... zN). The invention further provides a corresponding device. The invention can be used for allowing business and service staff to have a good knowledge of customers and providing the customers with their favorite products or services while in contact with the customers.

Description

technical field [0001] The present invention relates to the field of big data analysis, especially the application of big data analysis in the insurance field, and specifically relates to a control method and a corresponding device for modeling user behavior based on big data. Background technique [0002] In the traditional insurance industry, thousands of people are placed on the same level of risk, but in fact this is impossible. The traditional insurance industry uses actuarial techniques to measure the expected sales volume and risk of a certain type of insurance From the point of view of the insurance industry, traditional actuarial techniques only quantify estimated sales and risk levels at a certain latitude, and it is difficult to fully reflect the complexity of the actual operation process. At the same time, traditional actuarial research focuses on evaluation numbers and rarely involves individual cases. For example, when an insurance company sells auto insurance,...

AI Technical Summary

Problems solved by technology

[0002] In the traditional insurance industry, thousands of people are placed on the same level of risk, but in fact this is impossible. The traditional insurance industry uses actuarial techniques to measure the expected sales volume and risk of a certain type of insurance From the point of view of the insurance industry, traditional actuarial techniques only quantify estimated sales and risk levels at a certain latitude, and it is difficult to fully reflect the complexity of the actual operation process. At the same time, traditional actuarial research focuses on evaluation numbers and rarely involves individual cases. For example, when an insurance company sells auto insurance, it considers factors such as age, gender, marital status, driving record, income, occupation, education, background, etc. Specific individuals carry out follow-up purchase behavior prediction and risk prediction, and with the accumulation of big data on the number of insurance companies' customers, customer background information, and customer purchase behavior, it is necessary to use big data to formulate personalized insurance policies for customers, use social networks, and improve Products and services affect target customers, and through the analysis of big data, insurance companies can obtain more accurate pricing models and provide personalized solutions

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