 Identification of micro-segments in target audiences for personalised content delivery – a literaturereviewMariana Marinova ()
Business & Management Compass, 2026, issue 1, 55-60 Abstract: The growing reliance on data-driven marketing has increased the need for more precise approaches to audience identification and content personalisation. Traditional segmentation models, largely based on broad demographic and geographic criteria, are becoming less effective in digital environments characterisedby dynamic user behaviour, multi-channel interaction, and continuous data generation. This paper aimsto review the literature on the identification of micro-segments in target audiences for the provision of personalisedcontent. The review focuses on the role of artificial intelligence, machine learning, clustering methods, behaviouraldata, and intelligent automation in contemporary digital marketing. Its scope includes studies on digital marketing strategy, AI-enabled personalisation, customer analytics, Google-based advertising ecosystems, natural language processing, large language models, and intelligent agents. The study is limited by its conceptual and review-based design and does not include empirical testing of a clustering model. Methodologically, the paper applies content analysis, comparison, and synthesis of relevant academic and applied literature. Theliteraturereview shows that micro-segmentation has substantial potential to improve relevance, engagement, and campaign efficiency by enabling more adaptive and context-sensitive content delivery. At the same time, the literature remains fragmented, as most studies examine segmentation, personalisation, AI, campaign optimization, or intelligent agents separately rather than as an integrated system. The originality of the paper lies in bringing these strands together within a unified conceptual perspective. The practical value of the study concerns the applicability of such models to startups and small and medium-sized enterprises seeking scalable and cost-effective digital marketing solutions. Keywords: micro-segmentation; target audience; personalized content; digital marketing; Google Analytics 4; Google Ads; artificial intelligence; machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:vrn:journl:y:2026:i:1:p:55-60 Access Statistics for this article Business & Management Compass is currently edited by Julian Vasilev More articles in Business & Management Compass from University of Economics Varna Contact information at EDIRC. |
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