Mapping large-scale brand networks: A consumers’ foot traffic-based approach

Abstract

Commercial brands are increasingly interconnected, forming a complex network. Analyzing brand networks is crucial to understanding brand interactions and developing data-driven marketing strategies. Traditional methods utilize consumer behavior surveys and social media databases to construct brand networks, but few have considered consumers' spatial mobility - their movements between brand points of interest (POIs). We proposed a novel method to connect brands through massive POI foot traffic data. We demonstrated this method using SafeGraph's POI dataset in Florida, USA, comprising approximately 270 thousand POIs (of 4976 unique brands) over 44 weeks. By deriving common visitors between any two brands, we constructed four brand networks with two types of links (directed and undirected) and at two temporal scales (daily and weekly). We identified influential brands, brand pairs, and subgroups in these networks, and examined how these network properties changed over different temporal scales. We also found that these network properties exhibited only minor shifts during COVID-19 pandemic as compared to the pre-pandemic period, suggesting a resilient underlying network of brand connections. Finally, we proposed mobility-based marketing strategies to leverage brand network properties and foster a sustainable economy.