Early Warning of Telecom Customer Churn Based on Multialgorithm Model Optimization

Abstract

Background: China telecom is the largest integrated information service provider in China, its business volume all over the world. It is interesting to note that China Unicom and other telecom 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. Methods: This work aims to build a variety of algorithm models for target optimization and use them to predict whether telecom companies will lose customers, respond to the early warning of customer 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 and then the characteristic variable information value so as to optimize the prediction accuracy of the algorithm model. Through calculation, we noted the weight contribution rate of five characteristic variables to be the highest. Including total day charge, total day minutes customer service calls, international plan, and number of voicemail messages, linear regression, decision tree, Bayesian, artificial neural network, and support vector machine are used to predict customer churn on the customer dataset 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 multialgorithm models to predict the churn of telecom customers. Finally, it is found that it is better for the optimized characteristic variables to use the decision tree algorithm model to predict the loss of telecom customers.