Enhancing Consumer Engagement through AI-powered Marketing Personalization: Leveraging Collaborative Filtering and Neural Networks

Abstract

This research paper investigates the impact of AI-powered marketing personalization on consumer engagement, focusing on the synergistic application of collaborative filtering and neural networks. The study explores how these advanced technologies can provide deeper insights into consumer preferences, leading to more personalized and engaging marketing strategies. By employing collaborative filtering, we analyze historical consumer data to identify patterns and predict future behaviors, while neural networks enhance this process by learning complex, non-linear relationships within the data. The research employs a mixed-method approach, combining quantitative analysis through machine learning algorithms with qualitative insights from consumer feedback and case studies. Results indicate that the integration of collaborative filtering and neural networks significantly improves the accuracy of consumer preference predictions, enabling marketers to tailor content and offerings more effectively. This, in turn, leads to higher consumer satisfaction and increased engagement, as measured by key performance indicators such as click-through rates, conversion rates, and customer retention. The paper concludes with strategic recommendations for marketers on implementing AI-driven personalization techniques and discusses potential ethical considerations and future research directions in the evolving landscape of AI in marketing.