BEHAVIOR ANALYTICS OF THE DIGITAL AGE AUDIENCE: PERSPECTIVES, METHODS, AND CONSEQUENCES FOR MARKETING AND MEDIA

Abstract

Audience behavior analytics has become an important tool for analyzing and anticipating consumer preferences, impacting tactics in media, marketing, e-commerce, and public policy. This article investigates the transition of audience analysis from traditional methods to sophisticated digital metrics, facilitated by advances in AI, machine learning, and big data. It looks at essential approaches like quantitative tracking and qualitative insights, as well as major difficulties like data saturation, privacy issues, and technology constraints. This article uses case studies such as Netflix's content customization, Amazon's recommendation engine, and the Cambridge Analytica incident to demonstrate the potential and ethical implications of audience analytics. The analysis also discusses the implications for stakeholders: media companies use insights to shape content strategy and revenue models, marketers optimize campaigns for higher ROI, consumers benefit from enhanced personalization but have privacy concerns, and regulators work to protect data rights while encouraging innovation. Future trends point to more integration of AI and blockchain to improve customization and data security, while augmented and virtual reality open up new channels for audience involvement. By addressing ethical concerns and modifying legal frameworks, stakeholders may responsibly use audience analytics to foster meaningful connections in an increasingly data-centric society.