RETRACTED: Early Warning of Telecom Customer Churn Based on Multialgorithm Model Optimization - Xu, Jingxiu; Li, Xueguang; He, Zhonglin; Zhou, Jing (Frontiers in Energy Research, 2022)
Background: At present, the business of telecom companies is distributed to every member of the family at an amazing speed. As mobile, China Unicom and other companies have carried out similar businesses one after another, how to prevent the loss of existing customers in the fierce competition is an important issue for telecom companies to think about. Method: This work is to build a variety of algorithm models for target optimization, and use them to predict whether the telecom customers will lose, respond to the early warning of customers churn, and then implement active retention measures. Data characteristics affect the final loss prediction effect. In this study, the weight contribution rate of each characteristic variable is obtained by calculating the evidence weight (WE) and then the characteristic variable information value (CVIV), so as to optimize the prediction accuracy of the algorithm model. Through calculation, the weight contribution rate of five characteristic variables is the highest. Including total day charge (TDC), total day minutes (TDM), customer service calls (CSC), international plan (IP) and number vmail messages (NVM). Linear Regression, Decision Tree, Bayesian, ANN and Support Vector Machine (SVM) are used to predict customer churn on the customer data set published by telecom companies, the experimental results are used to test the performance of the algorithm model. Results: It is found that the characteristic variables calculated after optimization are put into multi-algorithm models to predict the churn of telecom customers. Finally, it is found that the optimized characteristic variables are better to use the decision tree algorithm model to predict the loss of telecom customers.