Optimizing Digital Content Strategy Based on User Interaction Patterns Using Machine Learning Algorithms - Septiani, Riska Endah (International Journal of Research and Applied Technology (INJURATECH), 2024)
In the attention economy era, generic digital content strategies are no longer effective in increasing user engagement. This study aims to optimize content strategies by analyzing user interaction patterns through a machine learning approach. Interaction data including engagement metrics, access time, and topic preferences are processed using the K-Means Clustering algorithm for audience segmentation and Random Forest to predict future content performance. The results show that automatically identifying user behavior patterns can increase the accuracy of content type recommendations by up to [X]% and upload time efficiency by [X]%. These findings prove that integrating intelligent algorithms in creative decision-making can minimize speculation in content production. This study provides practical contributions for digital marketers in designing more personalized, relevant, and data-driven strategies to achieve sustainable organic growth on digital platforms.