From Segmentation to Hyper-Personalization: A Systematic Literature Review of AI-Enabled Marketing Strategies

Abstract

To trace how AI-enabled marketing strategies have evolved from traditional segmentation toward AI-enabled hyper-personalization, and to map the current evidence base along this trajectory. Marketing segmentation has progressed from rule-based demographic targeting through data-driven predictive clustering toward AI- and generative-AI-enabled hyper-personalization, yet prior reviews rarely trace this progression as a single evolutionary narrative. A PRISMA-guided review searched five open-access sources (Google Scholar, CORE, DOAJ, SSRN, Semantic Scholar) across 13 batches, screening 71 unique records verified against Crossref, DOAJ, CORE, and publisher metadata; 29 studies were retained across four evolutionary stages. AI-enabled personalization is increasingly linked to gains in engagement, retention, and conversion, but effectiveness-measurement standards remain unharmonized and privacy/algorithmic-bias concerns are largely unresolved. Adopt standardized effectiveness metrics, embed privacy and bias governance into personalization design from the outset, and expand subscription-database coverage of the Traditional and Emerging stages in future reviews.