Customer concentration inside a store is of pivotal importance for retail management, acquiring controversial contributions about the best number of consumers in the floor space to ensure an enjoyable and pleasant experience. Indeed, the excessive concentration of people (crowd) might discourage from shopping in that location, while on the other hand, a certain traffic to the store generates profit for retailers. The aim of this paper is to support retailers' informed decisions by refining our understanding of the extent to which store layouts influences consumer density. To this end, we conduct a large field study using a unique dataset covering customers in a real grocery store with agent-based simulations. Results clearly show the extent to which this kind of simulations help predicting the changes in store layout able to affect customer density in the areas, while ensuring the same number of individuals.

Enhancing store layout decision with agent-based simulations of consumers' density

Bilotta, E;Pantano, P
2021-01-01

Abstract

Customer concentration inside a store is of pivotal importance for retail management, acquiring controversial contributions about the best number of consumers in the floor space to ensure an enjoyable and pleasant experience. Indeed, the excessive concentration of people (crowd) might discourage from shopping in that location, while on the other hand, a certain traffic to the store generates profit for retailers. The aim of this paper is to support retailers' informed decisions by refining our understanding of the extent to which store layouts influences consumer density. To this end, we conduct a large field study using a unique dataset covering customers in a real grocery store with agent-based simulations. Results clearly show the extent to which this kind of simulations help predicting the changes in store layout able to affect customer density in the areas, while ensuring the same number of individuals.
2021
Crowd
Store layout
Consumers' density
Retailing
Probability Density Function
Agent-based simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/352958
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