Customer Churn Prediction Using AdaBoost Classifier and BP Neural Network Techniques in the E-Commerce Industry

Abstract

In customer relationship management, it is important for e-commerce businesses to attract new customers and retain existing ones. Research on customer churn prediction using AI technology is now a major part of e-commerce management. This paper proposes a churn prediction model based on the combination of k-means clustering and AdaBoost classifier algorithm, allowing the segmentation of customers into three categories. Important customer groups can also be determined based on customer behavior and temporal data. Customer churn prediction was carried out using AdaBoost classification and BP neural network techniques. The results show that the research method of clustering before prediction can improve prediction accuracy. In addition, a comparative analysis of the results suggests that the AdaBoost model has better prediction accuracy than the BP neural network model. The research results of this paper can help B2C e-commerce companies develop customer retention measures and marketing strategies.