In contemporary TV audience prediction, outliers are considered mere anomalies in the otherwise cyclical trend and seasonality components that can be used to make predictions. In the ReTV project, we want to provide more accurate audience predictions in order to enable innovative services for TV content recommendation. This paper presents a concept for identifying the source of outliers and factoring TV content categories and the occurrence of events as additional features for training TV audience prediction. We show how this can improve the accuracy of the audience prediction. Finally, we outline how this work could also be combined with AI-enabled audience profiling to power new content recommendation services.