Making hard decisions: Which stores to close?
Many studies have tried to propose methods for finding the best location for new stores and facilities, but there are few studies that address the store closing problem. As a result of the recent COVID-19 pandemic, many companies are facing financial problems. In this situation, one of the most common solutions to save the brand is to downsize by closing one or more stores of the chain. Such decisions are usually made based on the performance of every single store, therefore, the underperforming stores are subject to closures. In this study, we use a variation of the Huff gravity model to predict customer behavior and revenue loss after closing each store of the chain. We particularly study the case of department stores in New York City using SafeGraph mobility and Facteus customer spending data. The case study results suggest that the choice of the store to be closed under our proposed model may not always match with single store performance, but interaction among stores. Our proposed approach provides decision-makers with new insights into store closing decisions and is likely to reduce the consequences of store closures on revenue loss.