The increasing prevalence of crowdfunding as a financing mechanism necessitates understanding the factors contributing to campaign success. This study explores the application of advanced artificial intelligence (AI) models to predict crowdfunding campaign success through comprehensive customer segmentation. Utilizing the “Crowdfunding Campaigns: Metrics, Success Factors, and Key Insights” dataset, which includes features such as campaign goal amount, duration, number of backers, category, launch month, country, currency, owner experience, video inclusion, social media presence, number of updates, and success indicators, this research compares multiple AI models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and XGBoost. The dataset undergoes preprocessing and exploratory data analysis to prepare it for model training and evaluation. Performance metrics such as accuracy, precision, recall, and F1-score assess these models’ efficacy. Our findings indicate that AI-driven customer segmentation enhances the predictive power and accuracy of outcome forecasting in crowdfunding campaigns. The best performing model, Logistic Regression identified through hyperparameter tuning, provides actionable insights for campaign organizers, enabling them to tailor their strategies for better engagement and funding outcomes. This research highlights the transformative potential of AI in marketing, especially in customer segmentation and personalized campaign management, with practical implications for improving crowdfunding effectiveness. By bridging the gap in existing literature and providing a robust analytical framework, this study contributes to the growing body of knowledge on leveraging AI for marketing optimization and offers a foundation for future research in this dynamic field.
