Research on O2O Coupon Usage Prediction Based on Machine Learning

This paper aims to explore the use of advanced machine learning classification models, such as decision trees, gradient boosting, and XGBoost, to conduct in-depth analysis and prediction of coupon usage data on O2O (Online to Offline) platforms, with the goal of enhancing customer engagement and optimizing the efficiency of marketing activities. The study begins by collecting and preprocessing multidimensional data from O2O platforms, including user behavior data, coupon attribute information, and historical consumption records, to construct a comprehensive dataset. Subsequently, decision tree classification models, gradient boosting classification models, and the XGBoost algorithm are employed to compare the performance of different models in predicting coupon usage behavior. Experimental results demonstrate that the decision tree model exhibits significant advantages in prediction accuracy, generalization ability, and computational efficiency, thanks to its efficient boosting strategies, regularization techniques, and ability to handle sparse data. By analyzing the prediction results of the decision tree model, this paper further reveals the key factors influencing coupon usage rates, such as user preferences, coupon amounts, validity periods, and redemption times. This provides a scientific basis for O2O platforms to formulate personalized marketing strategies, promote user conversions, and thereby enhancing the market competitiveness and profitability of O2O platforms.