Music streaming platforms dominate digital consumption but offer users limited and superficial insight into their own listening behaviors. This data opacity restricts user autonomy and reinforces a dependency on opaque algorithmic recommendations, creating a distinct need for transparent, user-centric analytical tools. This paper presents Listen, a web-based system developed through a Design Science Research methodology to address this gap. The system securely integrates with the Spotify API to retrieve granular, longitudinal user data and employs advanced data visualization techniques within a fully responsive, intuitive interface. The resulting artifact provides interactive dashboards that empower users to conduct in-depth analysis of their most-listened artists and tracks across customizable timeframes and to explore complex audio features through accessible graphical representations. Our findings demonstrate that by prioritizing usability and analytical depth, Listen successfully provides users with greater transparency and control over their personal data, serving as a robust model for bridging the critical gap between data availability and user insight in digital media platforms.
Lima et al. (Tue,) studied this question.