SYNTHETIC INTELLIGENCE APPLIED TO RETAIL MARKETING: PERSONALIZATION AND CONSUMER BEHAVIOR FROM THE PERSPECTIVE OF SYSTEMATIC BIBLIOGRAPHIC ANALYSIS

Abstract

This article presents a systematic bibliographic analysis of Synthetic Intelligence (SI) applied to retail marketing, with a specific focus on the mechanisms of consumer experience personalization and the reconfiguration of contemporary consumer behavior. Adopting the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), 113 studies published between 2010 and 2024 were identified, screened, and analyzed from the Web of Science, Scopus, SciELO, and Google Scholar databases. The bibliographic corpus was organized into six thematic clusters, revealing that SI produces measurable and positive effects on the personalization of retail offerings, consumer satisfaction, and the construction of loyalty bonds, while simultaneously introducing relevant ethical tensions related to privacy, algorithmic transparency, and what the literature calls the personalization paradox. Based on the integrative synthesis of the analyzed studies, the article proposes the original concept of Adaptive Synthetic Marketing (ASM), a theoretical framework that integrates the predictive, generative, and conversational dimensions of SI into the full lifecycle of the retail customer. The study contributes to the advancement of the field by organizing dispersed knowledge in the international literature, mapping research gaps, and offering a structured agenda for future investigations.