In the realm of online content retrieval, understanding user intentions based on search behaviors is essential to connecting customers to the content they seek. Adobe Systems recently acquired Fotolia, a digital asset supplier, to more efficiently provide their customers with quality images and videos from inside their ecosystem. Working with Adobe Stock, we accessed all of their query and content engagement data collected to date. In this formative analysis, we applied natural language processing to search terms. Results were paired with multiple metrics of user interaction associated with Adobe’s content. These data were grouped, or clustered, to reveal hidden layers of similarity across queries. These clusters, representing similar user intentions, help identify Adobe’s underperforming segments of customer interests and behaviors. Once identified, treatments such as user interface modifications, search algorithm changes, or query refinement suggestions can be targeted to queries in the same cluster. This will enhance the Adobe Stock user experience¬ increasing customer retention, satisfaction, and spending.