Real-Time Prediction of Cross-Border e-Commerce Spike Performance Based on Neural Network and Decision Tree

Abstract

During the spike in the activity of the cross-border e-commerce company, due to the limitation of time and data, the historical activity performance data and some data of this activity are used. Due to the limitation of data and the specificity of the prediction task, the original data are modeled and predicted by using a BP neural network model after a series of processing. This paper proposes a prediction model based on a decision tree and BP neural network model, through this real-time prediction model to predict the performance of the company’s spike activity every minute but also can play an early warning role, which is more helpful to the company’s decision-making. In fact, the company also used this model to detect a trend of lower performance during the May spike and then improved the performance and sell-out rate through email marketing and increased discounts to avoid inventory backlog.