A method for assessing artist viscosity based on big data analysis

Abstract

The invention provides a method of analyzing and evaluating the artist loyalty based on big data. The method comprises the steps that the target behavioral data of a network user are obtained; at least one behavioral loyalty characteristic data of the carried basic behavioral characteristics in the target behavioral data are extracted; the characteristic input variables and the target variable of the properties of the behavioral loyalty characteristic data information are extracted, and the behavioral loyalty characteristic data are quantified; a model is built upon the quantified behavioral loyalty characteristic data; the sample data are divided into a training set and a testing set, the training set is inputted into a decision tree model to do model parameter training, the trained results of the model parameters are applied to the testing set to test the trained results of the model parameters which satisfy the user defined stability conditions; the output data from the built model are solidified. Through the obtained loyalty degree of a certain basic behavioral characteristics of the user behavior, when the user is targeted for product marketing, the user can be more accurately defined, and the marketing conversion rate is increased.

Description

technical field [0001] The invention relates to the field of e-commerce, in particular to a method for assessing artist viscosity based on big data analysis. Background technique [0002] In user-based industries such as e-commerce or social networking, there is often an assessment of user loyalty. User loyalty is also called customer loyalty, and it can also be called customer viscosity. A good impression has been formed, forming a "attachment" preference, and then a tendency to repeat purchases. Through this indicator, users can understand the loyalty of users to e-commerce or social platforms, and provide data support and guidance for optimizing user experience and improving the service quality of enterprises. [0003] In the process of research, the inventor found that in the prior art, there is no mention of the user's loyalty to a third party other than the enterprise. For example, in the performance and entertainment industry, users are artist-oriented. The importan...

AI Technical Summary

Problems solved by technology

[0003] In the process of research, the inventor found that in the prior art, there is no mention of the user's loyalty to a third party other than the enterprise. For example, in the performance and entertainment industry, users are artist-oriented. The important indicator of users' loyalty to artists can positively classify users' consumption behaviors, which is conducive to the formation of the company's core competitiveness and will have a significant impact on the company's business processes and organizational structure

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