AI-Driven Marketing Strategy Optimization in the Digital Economy: A Machine Learning Approach
Year: 2025
Type: conference-paper
Abstract: During digital transformation, AI-powered marketing plan optimization gives the organization a competitive edge. Machine learning models improve consumer targeting, campaign targeting, ROI, and campaign likelihood in this study. We tested these methods using two real-world datasets and supervised learning algorithms like Logistic Regression, Decision Trees, Random Forest, and XGBoost. The data was imputed, one-hot encoded, normalized, and feature-selected to improve model accuracy and relevance. With an accuracy of 90.3% and an AUC-ROC score of 0.927, XGBoost surpasses all models in high-dimensional, difficult-to-characterize marketing data. The report also examines AI adoption's ethical issues, including data protection and model interpretation.
Authors: Yuezhang Chen, Jiacheng Liu
Institutions: Ritsumeikan University, York University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4412822303