'Input uncertainty' refers the effect of driving a simulation with input distributions that are based on real-world data. At WSC 2012, Ankenman and Nelson presented a quick-and-easy experiment to assess the overall effect of input uncertainty on simulation output. When their method reveals that input uncertainty is substantial, then the natural follow-up questions are which input distributions contribute the most to input uncertainty, and from which input processes would it be most beneficial to collect more data? To answer these questions Ankenman and Nelson proposed a sequence of additional experiments that are in no sense 'quick.' In this paper we provide a follow-up analysis that requires no additional simulation experiments beyond the overall assessment, and yet provides more information than Ankenman and Nelson. Numerical illustrations are provided.