Enhancing Customer Engagement through AI-Powered Marketing Personalization Engines: A Comparative Study of Collaborative Filtering and Natural Language Processing Techniques

Abstract

This research paper examines the efficacy of AI-powered marketing personalization engines in enhancing customer engagement by comparing two prevalent techniques: collaborative filtering and natural language processing (NLP). In response to evolving consumer expectations for tailored experiences, businesses are increasingly adopting AI technologies to deliver personalized marketing strategies. Collaborative filtering, which leverages historical user behavior data, and NLP, which interprets and understands consumer language, are among the most promising methods for generating personalized content. This study employs a mixed-methods approach, integrating quantitative data analysis and qualitative case studies, to assess the impact of these techniques on customer engagement metrics such as click-through rates, conversion rates, and customer satisfaction. Findings reveal that while both approaches significantly improve engagement over traditional methods, NLP demonstrates superior performance in contexts demanding nuanced understanding of customer intent and sentiment. Collaborative filtering, however, excels in scenarios where large datasets of user behavior are available, facilitating precise predictions and recommendations. This paper contributes to the field by offering a nuanced analysis of the strengths and limitations of each approach, providing actionable insights for marketers seeking to harness AI technology to enhance customer experience. Future research directions include exploring hybrid models that integrate both techniques to capitalize on their complementary advantages.