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.
Share and Cite:
Xiahou, X. and Harada, Y. (2022) Customer Churn Prediction Using AdaBoost Classifier and BP Neural Network Techniques in the E-Commerce Industry.
American Journal of Industrial and Business Management,
12, 277-293. doi:
10.4236/ajibm.2022.123015.