AIM2 framework for smart marketing innovation using AI driven consumer analytics with SOR neural networks and XGBoost in Saudi retail

Abstract

The study introduces the AIM2 (AI-Integrated Marketing Innovation Model) framework by integrating the Stimulus–Organism–Response (SOR) model with advanced machine learning methods for making sense of consumer analytics in Saudi retail. Using real data from Tamimi Markets, clustering methods put products into budget, intermediate, and luxury categories with a 92% silhouette score. Predictive analysis showed that XGBoost had a 14% smaller error margin than traditional regression and 9% more accuracy than simple Neural Networks. These results go beyond the current retail analytics methods that report less than 80% accuracy and highlight the value of incorporating AI-powered techniques with SOR. The study contributes to both theory and practice by demonstrating the AIM2 framework in a real retail context and providing practical tips for retailers who want to keep up with the modern marketing goals.