The human gaze is a powerful cue for determining the subject of a person’s attention. Therefore, the process of estimating a gaze is a crucial component of many human-machine interfaces, providing a more interactive and personal experience. Recent research has demonstrated that deep learning-based approaches to gaze estimation remain highly accurate across a wide range of subjects in unconstrained environments, allowing the technology to be used in a wide range of applications that traditional gaze estimation methods struggle with. One such application for this technology is the growing digital signage industry. This paper proposes the use of real-time deep learning-based gaze estimation in digital signage applications to create a more engaging user experience.