Artificial Intelligence In Retail Site Selection

Building "Smart" Retail Performance Models to Increase Forecast Accuracy

Leveraging AI helps untangle the complexity of the many variables influencing sales to give the best possible assessment of potential store performance. Companies that have made the shift toward combining statistics, GIS and AI for location decisions – not just financial estimates – have seen a tremendous benefit on many dimensions, not the least of which is their bottom line. One of the simplest statistics that illustrates the value of this approach is sales volume. In an analysis of more than 7,000 locations for 13 different concepts evaluated using CHARMS, locations that had a Site Quality rating of 65 or higher (the average rating is 50 on a 100-point scale) also had 17% higher sales than the average store.

Reducing the risk of opening an underperforming store is another key benefit associated with this approach. Clients have estimated that the total cost to close a store can be as much as three times the cost of opening a store. When you combine the hard-dollar costs left on the books with the opportunity costs and negative impact on the brand, the risk/reward equation favors an investment in an AI-based modeling approach to increase the probability of accurately predicting the future.