Optimizing marketing budgets for online retailers through artificial intelligence and machine learning techniques

Abstract In order to maximize marketing budget allocation for online merchants, this thesis investigates the use of artificial intelligence (AI) and machine learning (ML) approaches. Traditional budgeting techniques have proven insufficient in light of the retail industry's rapid digitization and the growing complexity of customer contact channels. This study offers a data-driven strategy that forecasts marketing results and maximizes spending efficiency by utilizing predictive modeling. Preprocessing marketing campaign data, exploratory data analysis, and the use of sophisticated machine learning models like Random Forest, Gradient Boosting, and LightGBM within the PyCaret framework are all part of the methodology. Based on accuracy and interpretability, the top-performing model is chosen, and feature importance is determined by SHAP analysis. The findings show that smart budget allocation based on machine learning predictions greatly increases client targeting tactics, decreases unnecessary spending, and boosts marketing efficacy. The results show how AI-driven optimization can revolutionize budget planning procedures and have important ramifications for e-commerce decision-makers.