Leveraging Neural Networks and Collaborative Filtering for Enhanced AI-Driven Personalized Marketing Campaigns

Abstract

This research paper explores the integration of neural networks and collaborative filtering to amplify the effectiveness of AI-driven personalized marketing campaigns. The study addresses the increasing demand for more tailored consumer experiences, emphasizing the importance of leveraging advanced machine learning techniques to analyze consumer behavior and preferences. We propose a hybrid model that combines the predictive power of neural networks with the adaptability of collaborative filtering to create dynamic marketing strategies that resonate more deeply with individual consumers. Our methodology involves training a neural network on historical consumer data to identify complex patterns and preferences, which are then refined through collaborative filtering to enhance recommendation accuracy. We evaluate the performance of the proposed model using a comprehensive dataset from a leading e-commerce platform, demonstrating significant improvements in consumer engagement metrics such as click-through rates, conversion rates, and overall customer satisfaction compared to traditional marketing approaches. The findings suggest that this hybrid approach not only personalizes marketing efforts on a granular level but also adapts to changing consumer behaviors in real-time, ultimately driving higher ROI for businesses. This paper contributes to the field of personalized marketing by offering a scalable and efficient solution for implementing AI-driven strategies that cater to individual consumer needs in the digital marketplace.