Research on E-Commerce Customer Churning Modeling and Prediction

This paper discusses the customer churning prediction problem in electronic commerce. In electronic commerce the customer data change is non-linear and time-varying and other characteristics, using a single prediction model to accurately predict e-commerce customer loss is difficult. In order to improve the prediction accuracy rate of electronic commerce churning, the model first uses the genetic algorithm for the screening of effecting factors, and extracts the important influence factors which affect the predicting results. Then support vector machine and neural network are respectively used to carry out the forecast. Finally, using support vector machine fuses the two prediction results to acquire the prediction results of the combination model. Simulation results show that the combined model can improve the prediction accuracy rate of the electronic commerce customer churning, and provides a new prediction method for the electronic commerce customer churning.