Financial user classification method based on user portrait model

Abstract

The invention discloses a financial user classification method based on a user portrait model. The method comprises the following steps: performing user tag classification, layering users at the sametime; further guiding user label indexes from the user label classification; guiding a user hierarchical dimension from the user hierarchy; establishing a user portrait model by fusing user label indexes and user hierarchical dimensions and combining user value indexes quantized for each user, performing overall user distribution portraits on a financial customer group by establishing a portrait model of a single user, and performing overall analysis through primary labels or subdivided secondary labels of the users. According to the financial user classification method based on the user portrait model, the users are classified and divided from multiple angles and multiple dimensions, so that the obtained user data is rich, wide in breadth and flexible in standard, user characteristics areaccurately described, high-quality client resources are effectively utilized, and effective and refined operation of the users is realized.

Description

technical field [0001] The present invention relates to a classification method, in particular to a financial user classification method based on a user portrait model. Background technique [0002] Since the 21st century, with the continuous development and innovation of smart phones and Internet technology, the mobile Internet, as a product of the combination of the two, has developed rapidly in recent years. In the era of big data, the consumption behavior of financial users has undergone major changes, and it is difficult for enterprises to contact to customers; user groups are becoming differentiated, and there is rarely one product and one financial service in the market that can meet the needs of all users, and financial products also need to be refined to provide different products for different customers. As people use smartphones for longer and more frequently, their user behavior data increases exponentially. And the data collected from smart terminals has the ba...

AI Technical Summary

Problems solved by technology

[0002] Since the 21st century, with the continuous development and innovation of smart phones and Internet technology, the mobile Internet, as a product of the combination of the two, has developed rapidly in recent years. In the era of big data, the consumption behavior of financial users has undergone major changes, and it is difficult for enterprises to contact to customers; user groups are becoming differentiated, and there is rarely one product and one financial service in the market that can meet the needs of all users, and financial products also need to be refined to provide different products for different customers

[0003] At present, the classification method of financial users is relatively simple. Most financial institutions will use simple deposits to classify users’ identities. The standard is not flexible enough, or they can be classified by asset classification. The data standard is not flexible enough. The identification of high-quality customers ignores user behavior, and cannot accurately describe user characteristics, which affects the effective use of high-quality customer resources.

At present, banks use it to identify VIP customers, but there is still a vague and one-size-fits-all situation. For example, simple judgments based on deposit amounts or financial assets cannot effectively achieve refined operation of users, and the efficiency is low.

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