Analysis of Consumer Behavior through AI: New Horizons of Personalized Marketing

Abstract

Annotation: The integration of artificial intelligence (AI) into marketing analytics systems creates new opportunities for developing behavioral, cognitive, and emotional user profiles in real time. Under these conditions, the study of AI algorithms capable not only of processing data but also of interpreting human intentions becomes a key factor in the development of a competitive business environment. The purpose of the article is to provide a theoretical and methodological justification for the role of artificial intelligence in analyzing consumer behavior and to develop an original model for assessing the effectiveness of personalized marketing under the conditions of hyper-personalization. The research methods include systemic, cognitive, and comparative analysis of classical and modern consumer behavior models (AIDA, 5W, Customer Journey, Theory of Planned Behavior) as well as AI-based analytical tools such as machine learning, NLP, sentiment analysis, generative AI, and reinforcement learning. To construct the author’s concept, the study applies a structural-functional and scenario approach, which makes it possible to combine analytical and ethical aspects of human–technology interaction. As a result, the author proposes the “AI Behavioral Insight Cycle (AIBIC)” model, which describes the cyclical process of consumer cognition by artificial intelligence through four stages: observation, interpretation, response, and reflection. Unlike traditional analytics systems, this model provides a self-learning mechanism in which every user interaction becomes a source of algorithmic improvement. The study identifies three types of personalization – emotional, cognitive, and contextual – which together form the concept of hyper-personalization, aimed at creating adaptive, empathetic, and predictive communications. The paper also outlines the ethical and legal framework of such interaction in line with the principles of the GDPR, Digital Services Act, and forthcoming AI Act, which regulate the balance between personalization, privacy, and developer responsibility. The practical significance lies in the possibility of applying the AIBIC model to design dynamic marketing analytics systems capable of measuring the level of trust, emotional engagement, and cognitive coherence within the “human–AI–brand” interaction. Keywords: artificial intelligence, behavioral analytics, hyper-personalization, cognitive interaction, emotional marketing, ethical analytics, digital trust, AI modeling.