Enhancing Ad Targeting with AI: Leveraging K-Means Clustering and Neural Networks for Advanced Audience Segmentation

Abstract

This research paper explores the integration of K-Means clustering and neural networks to enhance ad targeting through advanced audience segmentation. The primary objective is to address the challenges of precision and personalization in digital advertising by developing a hybrid model that leverages the strengths of both unsupervised and supervised learning approaches. We begin by employing K-Means clustering to segment users into distinct groups based on demographic, behavioral, and psychographic data. This clustering aids in identifying patterns and similarities within large datasets, thereby facilitating the creation of well-defined audience segments. Subsequently, deep neural networks are utilized to refine these segments by predicting user engagement and conversion probabilities, incorporating additional data layers such as historical ad interactions and real-time contextual information. The proposed model is evaluated using a dataset from a leading digital advertising platform, demonstrating improved targeting accuracy and increased return on investment (ROI) compared to traditional segmentation methods. The results indicate that this combined approach not only enhances audience segmentation but also significantly boosts the relevance and effectiveness of ad campaigns. Our findings suggest that integrating AI-driven methodologies in ad targeting can revolutionize personalized marketing strategies, offering advertisers a more nuanced understanding of consumer behavior and preferences.