Intelligent Upgrade of Consumer Behavior Analysis: An Interdisciplinary Study of Business and AI

Abstract

This research explores the merging of artificial intelligence and classical business administration to design an all-inclusive model aimed at analyzing intelligent consumer behavior. This study constructs an interdisciplinary theoretical framework which incorporates computational intelligence methods with behavioral economics through a layered “four-tier” analysis of theories, technological integration, synthesis, and application systems. Supervised learning and optimizations algorithms coupled with mathematical models outperform traditional statistical models, and consumer behavior prediction tasks are forecasted using deep learning convolutional neural networks which achieve 96.3% accuracy. Advanced machine learning techniques are critically important when analyzing complex non-linear, high-dimensional datasets containing consumer data. The framework developed gives practitioners step-by-step instructions on how to systematically apply the AI-driven analytics frameworks while ensuring synchrony between theoretical and practical efficiency. By showing the need for interdisciplinary frameworks to enhance organizational capabilities in decision-making, this research deepens the understanding of academic markets alongside the practical realm. It brandishes new evidence on the importance of analyzing sophisticated consumer behavior in the fast-paced digital economy.