Users of software applications generate vast amounts of unstructured log-trace data. These traces contain hidden clues to the intentions and interests of those users, but service providers may find it difficult to uncover and exploit those clues. In this paper, we propose a framework for personalizing software and web services by leveraging such unstructured traces. We use 6 months of Photoshop usage history and 7 years of interaction records from 67K Behance users to design, develop, and validate a user-modeling technique (which we call the utilization-to-vector, util2vec model) that discovers highly discriminative representations of Photoshop users. We demonstrate the promise of this approach on three sample applications: (1) a practical user-tagging system that automatically predicts areas of focus for millions of Photoshop users, (2) a two-phase recommendation model that enables cold-start personalized recommendations for many new Behance users who have Photoshop usage data, improving recommendation quality (Recall@100) by 21.2% over a popularity-based recommender, and (3) a novel inspiration engine that provides real-time personalized inspirations to artists. We believe that this work demonstrates the potential impact that unstructured usage log data can have on personalization.