Predicting Consumer Purchase Frequency: A Hybrid Analysis Using SEM-PLS and Decision Tree on AI based Recommendations, Digital Marketing, and Food Vloggers

This study analyzes the impact of investment in AI-based recommendation systems and food vloggers on consumer purchase frequency. By combining the PLS-SEM method and Data Mining techniques, this study aims to identify the most effective determinants in driving consumption behavior. The dataset includes digital marketing variables and respondent profiles used to measure the efficiency of capital allocation in modern culinary marketing strategies. The research findings show that AI-based recommendations have the most significant influence on increasing purchase frequency, while the food vlogger factor is found to be insignificant. In addition, the Data Mining results confirm that employment status is the most influential demographic variable. This data provides strategic insights for industry players to prioritize AI technology over traditional promotional channels in optimizing sales conversions.