Cross-Border E-Commerce User Churn Prediction Model Based on Decision Tree Algorithm

In the cross-border e-commerce field, predicting user churn is essential for maintaining customer relationships and enhancing corporate profits. However, existing prediction processes face challenges such as data imbalance, model overfitting, and improper feature selection, which significantly reduce prediction accuracy. To address these issues, this paper proposes a user churn prediction model based on the decision tree algorithm. We first employ data preprocessing techniques to tackle data imbalance, followed by feature selection using the decision tree's feature importance scores. The model's performance is further optimized by adjusting the tree depth and the minimum number of split samples. Finally, multiple decision trees are constructed and combined through voting or averaging to produce the final prediction. The results demonstrate that the decision tree algorithm excels in feature extraction, achieving an average feature correlation score of 0.75 and a maximum of 0.89. Additionally, it offers a clear advantage in prediction speed. Notably, the maximum prediction accuracy reaches 98.9%, which is significantly higher than traditional methods such as statistical analysis, user life cycle analysis, and social network analysis. In conclusion, the user churn prediction model based on the decision tree algorithm presented in this paper outperforms traditional approaches in terms of both accuracy and efficiency, providing an effective tool for predicting user churn in cross-border e-commerce.