Identification of micro-segments in target audiences for personalised content delivery – a literaturereview

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 characterised by dynamic user behaviour, multi-channel interaction, and continuous data generation. This paper aims to review the literature on the identification of micro-segments in target audiences for the provision of personalised content. The review focuses on the role of artificial intelligence, machine learning, clustering methods, behavioural data, 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. The literature review 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.