AI for Audience Targeting & Segmentation: Emerging trends in Data Analytics

Abstract

Artificial intelligence is reshaping audience targeting by enabling organizations to move beyond broad demographic categories toward behavioral, contextual, and intent-driven segmentation.
However, many existing segmentation approaches remain static, depend heavily on historical profiles, and inadequately address behavioral drift, privacy constraints, and changes in user intent over time.
This study identifies a research gap in the development of privacy-aware adaptive audience segmentation systems capable of updating segment membership continuously as new behavioral signals emerge. A conceptual AI framework is proposed that combines behavioral representation learning, temporal feature weighting, soft clustering, and privacy-sensitive targeting to create dynamically evolving audience groups. Unlike conventional customer segmentation models based predominantly on demographic or RFM variables, the framework incorporates interaction sequences, content engagement, purchase propensity, channel response, and recency-sensitive behavioral indicators. The study further considers explainability and segment stability as critical analytical objectives rather than assessing segmentation quality solely through conventional clustering measures.
The proposed research design is intended to examine whether adaptive AI segmentation can improve behavioral coherence, targeting precision, campaign responsiveness, and robustness to audience drift while reducing dependence on personally identifiable attributes.