Retailers evaluating new locations face a complex multi-variable decision that, when approached with incomplete market intelligence, leads to underperforming stores and costly expansion mistakes. This use case leverages BigGeo's geospatial platform to combine demographic profiles, consumer spending behavior, pedestrian and vehicular traffic patterns, competitor proximity, workforce availability, and nearby points of interest to score and rank candidate sites. Analysts can compare multiple potential locations side-by-side within a unified spatial environment, identifying trade areas, estimating revenue potential, and flagging cannibalization risk before committing capital. The result is a data-driven site selection process that dramatically improves the probability of long-term store success and reduces the number of costly site closures driven by poor location choices.