AI-Driven SEO Models for Enhancing Digital Marketing Performance - Sarfandi Sarfandi (Politeknik Siber Cerdika Internasional, 2025)
This study aims to examine the impact of AI-driven SEO models on digital marketing performance and to address the growing need for adaptive, data-driven optimization in an increasingly algorithmic search environment. The research adopts a quantitative design involving 18 organizations, utilizing Google Analytics and Search Console datasets, expert surveys, and AI-based predictive modeling outputs. Data were analyzed through descriptive statistics, regression analysis, mediation testing, and SEM-PLS to validate the structural relationships among AI-driven SEO, SEO performance, and digital marketing outcomes. The findings reveal that AI significantly improves keyword ranking stability, organic traffic, user engagement, and conversion metrics. Statistical results confirm strong direct effects of AI-driven SEO on marketing performance, with SEO performance acting as a substantial mediating variable. AI models such as XGBoost and Random Forest demonstrate high predictive accuracy, while automated semantic optimization greatly enhances content relevance and metadata quality. These results imply that AI-driven SEO provides organizations with strategic advantages by enhancing visibility, improving cost efficiency, and strengthening competitive positioning in digital markets. The originality of this study lies in developing and empirically validating an integrated AI-SEO performance model, offering a unified framework that has not been addressed in previous research.