AI-Driven Content Personalization and Its Effect on Consumer Engagement: A Multi-Platform Empirical Review
Abstract
Artificial intelligence (AI)-driven content personalization has transformed how digital platforms interact with their users by delivering tailored content recommendations that improve relevance, retention, and conversion. This paper presents a comprehensive empirical review of AI personalization techniques across streaming, e-commerce, social media, news aggregation, and EdTech platforms, analysing their measurable effects on key engagement metrics. Using secondary data from peer-reviewed studies published between 2016 and 2025, six Python-generated charts visualise platform-specific engagement uplift, algorithm performance on multiple dimensions, privacy-acceptance trade-offs, traditional versus AI-personalised content outcomes, algorithm evolution timelines, and ethical concern severity across sectors. Findings show that AI personalization consistently improves click-through rates (CTR) by 22–45%, session duration by 25–51%, and return visit rates by 26–52% across platforms. However, critical challenges persist, including filter bubble effects, algorithmic bias, data privacy violations, and addictive engagement patterns. The paper derives actionable implications for platform designers, content marketers, regulators, and AI ethicists navigating the complex intersection of personalization, engagement, and responsible AI deployment.
Authors
Ms. Nupur Tripathi, Vipin Kumar, Vishal Kumar, Tanay, Shubhi rani
Institution
NIET Business School, Greater Noida
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