A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.
Choosing a store location based on estimated profits can be the factor that makes or breaks a store—or chain. An extreme example of poor geospatial planning would be putting a ski shop in a tropical climate area. Obviously, anyone can see this is a bad idea, but in the more complicated reality that retailers face, huge amounts of data are relevant to the art of choosing store locations and predicting profit.
Retail chains have the luxury of using data from other stores in their franchise (quarterly or yearly profits, supply costs, marketing costs, fluctuations in income, etc) cross-referenced with mostly external geospatial data. Non-franchise stores, however, will have a harder time utilizing internal data for location planning and profit prediction. They can, however, use internal data regarding their products, like target audience data or manufacturing or supply costs.
A good model should use demographic data such as census and points of interest data, climate data, real estate listings, and contact lists. All this geospatial and demographic data gives an idea of a location’s unique characteristics. These, in accordance with analysis of a store’s performance, should enable building a model that can spot correlations between profit and location, thus enabling profit prediction based on location and location planning.
Analyzing social media and news from a certain location could also help build a more complete picture of the location.
The main challenge in this analysis is asserting causation rather than just correlation between store location and profits. That is to say, a store’s performance is affected not only by its location, but also by a plethora of additional factors.
Geospatial data also includes a large amount of different types of data. Avoiding an overload of data and focusing only on the most important data can be challenging.
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Huge chains like Subway and Wells Fargo use AI to find new store locations—and the practice has been spreading. For example, Japanese convenience store chain Lawson has begun using AI to determine new store locations with geospatial data like “household distribution patterns and traffic volume.”
Is AI the key to finding the right location, location, location?
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