This thesis investigates how AI-driven hyper-personalization is transforming branding strategies and consumer relationships. As traditional mass marketing proves increasingly ineffective for engaging diverse, digitally savvy consumers, AI technologies enable brands to create individualized experiences at scale. The research examines how branding can shift from static visual identities into adaptive, dynamic experiences powered by AI, offering new strategies for engaging customers desensitized to generic advertising, fatigued by rapid trends, and overwhelmed by product choice.

Using mixed methods, including case study analysis of Amazon, Netflix, Sephora, and Nike, marketing theory, behavioral economics, and speculative design methodology, the research examines how hyper-personalized branding impacts customer engagement and loyalty. The analysis reveals current personalization strategies and their effectiveness while exploring the behavioral patterns, ethical implications, and systemic shifts occurring as brands evolve from controlled narratives into responsive, data-driven systems.

A WebAR mobile prototype demonstrates hyper-personalization by bridging digital e-commerce with physical shopping experiences, where users scan products to experience AI-generated personalized packaging, imagery, and messaging based on their individual preferences and values. The prototype showcases how hyper-personalization can function as an adaptive brand ecosystem responding to individual contexts while maintaining brand consistency. This reveals that hyper-personalization creates more nuanced consumer connections by adapting brand experiences to individual contexts, but also introduces critical tensions around privacy, data collection, and behavioral manipulation. The research demonstrates that AI enables brands to function as responsive ecosystems rather than fixed identities, fundamentally shifting the designer's role from creating static deliverables to architecting adaptive systems.