Retailers with footprints spanning several markets often outsource site performance and market penetration analysis to consulting firms with GIS experts. However, an increasing number of savvy retailers are turning to Location Intelligence platforms. Instead of relying on 200 page slide decks delivered on an ad-hoc basis, Location Intelligence provides robust and repeatable analyses using location data to fuel insights.

To show this in action, below, we’ll build a spatial data model to maximize coverage in underperforming market areas. In building our model we used publicly available data on Target store locations to simulate the geographical conditions for a national retail network. With our model our goal will be to:

1. Identify market areas underperforming using core-statistical based areas (CBSA)
2. Select market(s) for expansion based on greatest increase in reach of total addressable population
3. Find suitable sites in market area based on geo-demographic insights from location data

Finally, with these results, we’ll consider ways to expand our model’s accuracy with additional location data inputs.