Member value quantitative evaluation method based on logistic regression

Abstract

The invention relates to a logistic regression-based member value quantitative evaluation method. The method comprises the steps of 1) obtaining member original data; 2) preprocessing the obtained member original data to obtain preprocessed formatted data; 3) performing data annotation on the preprocessed formatted data; 4) screening effective features used for inputting a model from the original features of the formatted data; 5) constructing a member value evaluation model including variable segmentation, WOE transformation of variables and logistic regression estimation; and 6) inputting the effective features screened in the step 4) into the constructed member value evaluation model to obtain a member value quantitative evaluation result. Compared with the prior art, the method has the advantages of being objective in evaluation, more suitable for e-commerce platforms such as the B2B iron and steel industry and the like, and the like.

Description

technical field [0001] The invention relates to the field of Internet technology, in particular to a method for quantitative evaluation of membership value based on logistic regression. Background technique [0002] With the development of Internet big data and the improvement of computer performance, membership platforms (membership value systems) are increasingly being used in all walks of life. The purpose of building a membership value system is to divide user groups into distinct groups through user tags, and use various means to increase the four rates of different types of users (the four rates of B2B industry: coverage, conversion rate, Repeat purchase rate, penetration rate). The traditional RFM model measures the current user value and user potential value through Rencency (last transaction), Frequency (transaction frequency) and Monetary (transaction amount). Therefore, the three values ​​in the RFM model represent user type, user loyalty, and user value. [00...

AI Technical Summary

Problems solved by technology

[0003] However, the disadvantages of the existing RFM model are: (1) First, the model only contains three dimensions, and the information contained in these dimensions is limited, which cannot meet the actual application in the B2B industry, especially for the B2B industry supplier users

However, as the B2B steel industry is still in the development stage as an emerging Internet industry, it is difficult to divide the user distribution of each stage through the RFM model

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