Challenging the Location Paradigm: Parsimoniously Predicting Store Performance with Urban Scaling
(A2021-93928)

AUTHORS

Lennard Schmidt, Brandenburg University of Technology; Erik Maier, HHL Leipzig Graduate School of Management; Florian Dost, Brandenburg University of Technology

ABSTRACT

Location is considered the most important driver of retail store performance; hence, retailers invest in extensive location research, utilizing expensive rich data. This research proposes an alternative, singular, freely obtainable predictor for location potentials. Drawing on the system scaling literature outside of marketing, we suggest that measures of the urban scale in a store’s trading area are a substitute for a multitude of traditional location measures, such as demographic or socio-economic variables. We demonstrate that our scale measure, the route factor calculated from road map data, performs on par with a common set of traditional predictors in a large dataset of supermarket sales. Moreover, our theory correctly predicts, and the analysis shows, a collinearity problem that has gone unrecognized in traditional store performance models. We validate our approach on a second variety-store dataset that covers a wider range of location conditions for generalizability.