Find and fix underperforming retail markets with spatial data models

Retailers are struggling to make informed decisions about store locations due to outdated "four-wall economics" approach, but spatial data models can help. By analyzing location data and consumer behavior insights, retailers can identify underperforming markets and optimize their store network. A spatial data model was built using publicly available data on Target's store locations, CBSA areas, census block group boundaries, and socio-demographic measures. The model identified Nashville as a potential market for expansion, but with low addressable population and limited suitable sites. In contrast, Houston had a higher addressable population and more dispersed High-High locations, making it the most suitable choice for Target's expansion. Further analysis could involve integrating additional data sources to gain a more granular view of performance in each market area.