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200809 https://hdl.handle.net/2286/R.2.N.200809 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2025-05 37 pages Hodder, Abigael Zhang, Zhongju (John) Lisjak, Monika Barrett, The Honors College Department of Marketing Department of Information Systems This thesis provides a comprehensive exploration of data-driven personalization and its effects on consumer behavior. As brands increasingly rely on artificial intelligence, machine learning, and predictive analytics to tailor digital experiences, personalization has evolved from a competitive advantage to a baseline consumer expectation. Through secondary research, this study examines the foundational technologies that enable personalization, including the types of data collected, the machine learning models employed, and the systems through which personalized content, recommendations, and interactions are delivered. The thesis further investigates how companies operationalize personalization through widely adopted marketing strategies and explores the technologies that likely support them. Beyond implementation, the study focuses on personalization’s psychological and behavioral effects, highlighting its role in increasing engagement, reducing cognitive load, shaping consumer decisions, and deepening brand loyalty. At the same time, it explores how personalization may also contribute to effects such as cognitive dissonance, perceived manipulation, and heightened sensitivities around privacy, autonomy, and algorithmic narrowing. By synthesizing current academic and industry literature with real-world examples, this thesis offers a broad yet nuanced understanding of data-driven personalization’s role in shaping modern consumer experiences and the ethical tradeoffs that accompany its widespread adoption. Data-Driven Personalization Artificial Intelligence Machine learning Machine learning models Personalization Systems Personalization Strategies Consumer Behavior Relevance & Engagement Decision-Making Patterns Perception of Value Algorithmic & Behavioral Narrowing Privacy Sensitivities Cognitive Dissonance Perceived Manipulation Data-Driven Personalization And Its Effects On Consumer Behavior