Published Paper Details:

In today's digitally driven marketplace, personalized marketing has emerged as a pivotal strategy for businesses aiming to engage customers on a deeper level and foster long-term relationships. With the advent of Artificial Intelligence (AI), personalized marketing has reached unprecedented levels of customization and effectiveness. This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction. The research explores various facets of personalized marketing, including data collection, analysis, and implementation of tailored campaigns. Through a comprehensive literature review, this thesis examines the theoretical underpinnings of personalized marketing and the evolution of AI technologies in this domain. Moreover, empirical studies and case analyses are conducted to illustrate real-world applications and outcomes of AI-driven personalized marketing strategies across diverse industries. Key themes investigated include the role of machine learning algorithms in deciphering consumer behavior patterns, the ethical considerations surrounding data privacy and consent, and the implications of AI-generated content on brand- consumer interactions. Additionally, the study investigates the impact of personalized recommendations, dynamic pricing, and predictive analytics on enhancing customer satisfaction and loyalty. Furthermore, this research highlights the challenges and opportunities associated with integrating AI into personalized marketing initiatives, such as algorithm bias, algorithmic transparency, and the need for continuous adaptation to evolving consumer preferences. Insights gathered from interviews with industry experts and surveys of consumers contribute to a comprehensive understanding of the practical implications and future directions of AI-enabled personalized marketing.