(untitled) (UNIZIK Journal of Marketing, 2026)
Retailers face increasing difficulty in aligning segmentation strategies across online and offline environments amid rapid technological change. This study adopts a qualitative design using PRISMA guidelines, reviewing 46 peer-reviewed articles (2015–2024) from an initial pool of 68. Findings show that online segmentation is driven by real-time behavioral data and AI-based personalization, while offline segmentation remains largely demographic and geographic. However, cross-channel consumer behavior is creating overlap, with emerging technologies such as IoT, edge computing, and federated learning narrowing the data gap between both environments. The paper also revealed several major obstacles, including data silos, excessive personalization, and privacy threats, as well as potential convergence in the form of unified CRM platforms. The implication of this results is that both theory and practice must shift toward holistic, cross-channel frameworks that better capture dynamic consumer behavior and support more effective strategic decision-making. It was therefore concluded that retail segmentation is evolving from distinct online and offline approaches toward integrated, data-driven strategies shaped by omnichannel behavior. Hence, retailers should invest in unified data systems, advanced analytics, and context-sensitive models. The originality of this study lies in its contribution towards clarifying the evolving divide in retail segmentation and identifying pathways toward integrated, technology-driven segmentation frameworks.