Predicting user performance at the base station in telecom networks is a critical task that can significantly benefit from advanced machine learning techniques. However, labeled data for user performance are scarce and costly to collect, while unlabeled data consisting of base station metrics, are more readily accessible. Self-supervised learning provides a means to leverage this unlabeled data, and has seen remarkable success in the domains of computer vision and natural language processing, with unstructured data. Recently, these methods have been adapted to structured data as well, making them particularly relevant to the telecom domain. We apply self-supervised learning to predict user performance in telecom networks. Our results demonstrate that even with simple self-supervised approaches, the percentage of variance of the target values explained by the model, in low-labeled scenarios (e.g., only 100 labeled samples) can be improved fourfold, from 15% to 60%. Moreover, to promote reproducibility and further research in the domain, we open-source a dataset creation framework and a specific dataset created from it that captures scenarios that have been deemed to be challenging for future networks.