Computer Simulation of Electronic Commerce Customer Churn Prediction Model Based on Web Data Mining
The traditional forecasting model has the problem of low prediction accuracy and large error when predicting the customer churn of electronic commerce. This paper proposes a method of electronic commerce customer churn prediction model based on the grey system theory. The model extracted customer characteristics at each stage from the electronic commerce customer churn information data for prediction, and converted the different characteristics in the different stages into a feature vector, and calculated the distance between the predicted information and all the sample. On the basis, the paper used the nonlinear mapping methods to convert electronic commerce customer churn structure data, fusing grey theory to set up electronic commerce customer churn prediction model. Simulation results show that the proposed model has some advantages in predicting electronic commerce customer churn, and has some advantages in accuracy and efficiency. Its simulation results are similar to the actual situation.