This study examines the COVID-19 pandemic's impact on customer preferences for shopping destinations and its persistence using a quantitative approach. It uses a modified version of the Huff gravity model to quantify temporal shifts in customer preferences when it comes to choosing a store for shopping purposes. The study uses large-scale mobility and place datasets and census information to analyze department stores in New York City. Using clustering techniques and statistical inference models, this study estimates the immediate and long-term impact of the COVID-19 pandemic on customer behavioral shifts, highlighting the heterogeneity of response among different socioeconomic communities. The findings of this research, show that the proposed model effectively captures the dynamics of shopping location decisions and temporal visit patterns, allowing managers and marketers to understand customer preferences and adapt strategies accordingly. Retailers should use quantitative methods to update assumptions based on new consumption patterns, rather than expecting a complete return to pre-pandemic consumer behavior.
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Xu et al. (2024) studied this question.
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