Machine Learning For Enhanced Digital Marketing: Strategies, Models, and Interpretability

Abstract

The intersection of machine learning (ML) and digital marketing (DM) has revolutionized the way businesses interact with consumers, optimize campaigns, and drive conversions. This paper explores the application of ML algorithms in digital marketing, focusing on model selection, performance evaluation, and explainability. We present a comparative analysis of Random Forest, Logistic Regression, and XGBoost models applied to a synthetic dataset simulating digital marketing scenarios. Metrics such as accuracy, ROC_AUC, F1 Score, precision, and recall are evaluated, providing insights into model performance. Additionally, the study highlights the importance of AI explainability in interpreting model outcomes, enabling better decision-making in marketing strategies. The results demonstrate the potential of ML to enhance digital marketing, providing actionable insights while mitigating the risks associated with algorithmic bias.