A method for identifying pre-churn users

Abstract

The invention discloses a method for recognizing pre-churn users. Firstly, a user parameter set is extracted, various churn user predictive model algorithms are selected, models are established for users on which the user parameters are concentrated by adopting each predictive model algorithm so as to generate various churn user predictive models, the various churn user predictive models are adopted to perform churn user prediction respectively to acquire the prediction results of various churn user predictive models, finally, two or more churn user predictive models closest to a preset condition are selected according to the prediction results of the various churn user predictive models, and combined prediction of pre-churn user is performed. When the method is adopted to perform user churn rate prediction, the method relies on the principle of minimum prediction error and chooses suitable combined algorithm, rather than performing prediction on the user data set according to one prediction method; therefore, the result of prediction of churn users is more accurate, follow-up work to retain users can be launched more conveniently, and the invested cost is reduced.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to a method for identifying pre-lost users among mobile communication users. Background technique [0002] Customer churn has always been a research hotspot in academia and industry. The problem of customer churn includes reasons for churn, churn classification, churn prediction, and customer recovery. Among them, how to accurately predict customer churn is the core of customer churn analysis. the core issue. [0003] The current user churn prediction algorithm mainly uses a certain data prediction algorithm (such as decision tree, neural network, etc.) after extracting and preprocessing the data to perform data mining on the extracted data, and finally outputs the prediction result of the churn rate. [0004] In the current churn prediction process, after preprocessing the data, the prediction result directly using a single algorithm model covers a wider range of user...

AI Technical Summary

Problems solved by technology

[0004] In the current churn prediction process, after preprocessing the data, the prediction result directly using a single algorithm model covers a wider range of users, but its accuracy rate is not satisfactory

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