OPTIMIZING PROGRAMMATIC ADVERTISING: A MACHINE LEARNING APPROACH TO PREDICTIVE AD TARGETING

Abstract

In the ever-evolving landscape of digital advertising, programmatic advertising has emerged as a pivotal tool for automating ad placement and targeting audiences at scale. However, traditional methods often fall short in accurately predicting user behavior and delivering relevant ads to the right audiences. This study explores the potential of machine learning (ML) techniques to enhance predictive ad targeting within the programmatic advertising ecosystem. By applying a range of supervised and unsupervised ML models, including decision trees, neural networks, and clustering algorithms, we assess the ability of these models to improve ad relevance and engagement while optimizing budget allocation. Our findings reveal that ML-driven predictive targeting significantly increases click-through rates (CTR) and conversion rates compared to conventional targeting strategies. This research highlights the implications of using ML to improve ad targeting precision, reduce ad spend wastage, and enhance user experience. These insights contribute to the advancement of programmatic advertising strategies by demonstrating the transformative impact of AI and ML on audience segmentation and predictive ad delivery.