Predictive Analysis of Customer Behavior in Retail Management Based on XGBoost Algorithm

In the highly competitive retail market, accurate prediction of customer behavior is very important for formulating effective marketing strategy, optimizing inventory management, improving customer satisfaction and maintaining enterprise competitiveness. In today's society, retail management customer behavior prediction analysis has a high error rate and insufficient accuracy. LSTM algorithm in deep learning is an effective behavior prediction and analysis technology. Compared with previous detection algorithms, LSTM algorithm has higher accuracy. In this paper, LSTM algorithm is used to predict the retail management customer behavior, which greatly improves the prediction accuracy. Finally, through experiments, the accuracy of the prediction system established by LSTM algorithm is very high. And reached 96.78%