Predict Customer Churn based on Machine Learning Algorithms

Abstract

With the increasingly fierce competition in the market, it is inevitable for companies to find ways to make customer churn analysis and prediction in order to maximize benefits and save costs. This review will start with the application of machine learning in customer churn prediction, introduce the industries using churn analysis, state the principles and processes of prediction and summarize and compare some of the mainstream algorithms used in the prediction such as naive bayes, logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting, adaptive boosting, k-nearest neighbor and artificial neural networks. Even though there are still many shortcomings in the various algorithms that need to be improved, machine learning still plays an important role in making customer churn analysis and prediction. As technology continues to develop and model research gradually deepens, the accuracy of customer churn prediction will continue to improve in the future, providing more comprehensive guidance for companies to develop customer retention programs.