Retail Location Assessment with Urban Scaling

Abstract

Location is an impactful but irrevocable driver of retail store performance. Unless retailers rely on their gut feelings for finding high potential locations, they have to invest in extensive location research, calibrating performance models on expensive rich data (e.g., income or education of households in each prospective trading area). To prevent “the death of the high street”, also public administrators care for location potentials. This research proposes a parsimonious new model for location potentials, drawing from emerging urban scaling literature outside of marketing. We show that a measure of the local urban scale explains stores’ sales, local competitive intensity, and defining aspects of store lifecycles (managers’ location choice, sales ramp-up to a steady state after opening, store closure). We demonstrate these capabilities of the scaling approach using six datasets, including data from two retail chains (grocery and variety stores), public data, map data, and an experiment with retail managers. Our parsimonious model compares well to more complex multivariate benchmarks and remains more robust across modeling choices. We put forth a scale measure that can be cheaply obtained from map data, offering accessible applications for retail and public policy managers (e.g., “heat maps” across all potential locations in a city) and to marketing research in general (e.g., as input or control variable for geo or mobile marketing).