Personalized Marketing through Machine Learning – Uncovering Consumer Perceptions and Effective Implementation

Abstract

This thesis explores the role of machine learning (ML) in enhancing personalized marketing strategies. It seeks to answer how personalized marketing through ML can be implemented effectively while mitigating potential negative consumer responses, biases and unintended outcomes. This literature review synthesizes key insights from existing research to provide practical guidance. The findings suggest that the use of ML in personalized marketing offers significant benefits for both marketers and consumers. It is effective in identifying consumer preferences and enables real-time actions, but consumers’ reservations about artificial intelligence (AI) and data privacy concerns remain critical challenges. Additionally, ML has certain limitations that need to be considered when designing algorithms and interpreting results, as overlooking these limitations can lead to severe consequences for companies. This research provides actionable recommendations for marketers to implement personalized marketing through ML effectively, emphasizing the importance of fostering consumer trust, ensuring transparency, and addressing ethical considerations.