Intelligent Machine Learning System for Predicting Customer Churn

Chenggang He, Chris H. Q. Ding, Sibao Chen, Bin Luo

Abstract: Nowadays, customer churn issue is becoming more and more important, which is the key indicator of the business and production success. But how to predict the actual customer churn and take action before customer loss is becoming a difficult issue in the industry. At the same time, how to keep the place of production is the first problem we are facing. After the deep research, we use Artificial Intelligence (AI) and Machine Learning (ML) technology to develop a smart intelligent system and reduce the actual customer churn about the production. This paper will explain the machine learning technology which used in this smart intelligent system and the reader will learn how to use this system to reduce customer loss. In the customer’s churn prediction model aspect, the most popular predictive models have been used, namely, support vector machines, random forests, K-nearest neighbors, and Gradient boosting classifier are applied to check the effect on accuracy, AUC, and F1-score. Through the experiment, it proofs that the Gradient boosting classifier and Random forests give the highest accuracy of 95.32% and 94.29% respectively. The highest AUC score of 91% which achieved by both Gradient boosting classifier and random forests. The highest F1-score of 97.3% is achieved by the Gradient boosting classifier which outperforms over others.