A Study on The Impact of AI-Driven Personalization in Digital Marketing on Consumer Satisfaction - G. Ravichandiran; C. Paramasivan (Journal of Applied Taxation and Policy, 2026)
Artificial intelligence (AI) enables firms to personalize digital marketing at scale through predictive analytics, recommender systems, conversational agents, and generative content. However, prior studies report both favorable outcomes and important boundary conditions related to privacy, transparency, perceived intrusiveness, and algorithmic bias. This study synthesizes peer-reviewed research on AI-driven personalization and examines how it influences consumer engagement and satisfaction. An integrative literature review was conducted using purposive searches of Scopus-indexed and publisher databases for studies published mainly between 2019 and 2025. The analysis organized the literature around four mechanisms: relevance, convenience, perceived understanding, and interactive responsiveness. The synthesis indicates that AI-driven personalization can strengthen cognitive, emotional, and behavioral engagement when recommendations are accurate, timely, and contextually appropriate. Satisfaction improves when personalization reduces search costs, increases service convenience, and supports seamless customer journeys. These effects are not automatic. Excessive data collection, opaque targeting, inaccurate recommendations, and over-personalization can reduce trust and create discomfort. The review proposes that consumer trust and perceived value mediate the relationship between personalization and outcomes, while privacy concern, transparency, and human oversight operate as boundary conditions. The study concludes that effective AI personalization requires a value-sensitive approach that balances relevance with consumer autonomy, data protection, explainability, and opportunities for human intervention.