AI for Audience Targeting and Segmentation: Emerging Trends in Data Analytics

Abstract

The proliferation of digital touchpoints has produced volumes of behavioral, transactional, and contextual data that traditional rule-based marketing segmentation methods are increasingly unable to process effectively. Artificial intelligence has emerged as the dominant paradigm for transforming this data deluge into actionable audience insight, enabling marketers to move from static demographic segments toward dynamic, behavior-driven clusters that update continuously as new signals arrive. This paper examines emerging trends in AI-based audience targeting and segmentation, synthesizing findings from recent literature on machine learning clustering techniques, predictive analytics, natural language processing, and autonomous agentic systems applied to marketing. The paper presents a structured framework that categorizes AI-driven segmentation approaches into descriptive, predictive, and prescriptive layers, and discusses how unsupervised algorithms such as K-means and DBSCAN are combined with supervised predictive models to generate segments that are both statistically coherent and commercially actionable. A comparative analysis of AI-driven and traditional segmentation approaches, informed by patterns reported across the reviewed studies, indicates measurable gains in targeting precision and campaign conversion outcomes. The discussion addresses data quality, algorithmic bias, interpretability, and privacy regulation as recurring constraints on responsible deployment, and situates the rise of autonomous AI agents within a broader trajectory toward hyper-personalized, real-time marketing systems. The paper concludes that while AI substantially improves the precision and adaptability of audience targeting, its long-term value depends on organizations pairing technical capability with interpretability safeguards and privacy-conscious data governance.