Evaluating the Impact of Generative Adversarial Networks (GANs) on Real-Time Personalization in Programmatic Advertising Ecosystems

The integration of Generative Adversarial Networks (GANs) into programmatic advertising has the potential to revolutionize real-time personalization by generating dynamic, contextually relevant ad creatives. This paper explores the impact of GANs on enhancing user engagement, improving ad targeting accuracy, and driving higher returns on investment (ROI) in programmatic advertising ecosystems. By analyzing existing literature and utilizing a GAN-based simulation framework, this study evaluates the performance of GAN-generated advertisements compared to traditional programmatic advertising approaches. The findings suggest that GAN-powered ad personalization can significantly increase click-through rates (CTR) and user interaction while providing advertisers with an innovative tool to optimize ad creatives in real-time. However, the research also highlights challenges, such as bias in training data, the risk of overfitting models, and the need for stronger privacy regulations. This paper concludes with strategic recommendations for advertisers and platforms aiming to leverage GAN technologies in a sustainable, ethical manner.