Close a Store to Open a Pandora’s Box? The Effects of Store Closure on Sales, Omnichannel Shopping, and Mobile App Usage

Taotao Ye () and Venkatesh (Venky) Shankar ()
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Taotao Ye: Costello College of Business, George Mason University, Fairfax, Virginia 22030
Venkatesh (Venky) Shankar: Brierley Institute of Customer Engagement, Cox School of Business, Southern Methodist University, Dallas, Texas 75275

Marketing Science, 2025, vol. 44, issue 4, 820-837

Abstract: What are the causal effects of the closure of a store on a retail chain’s overall, offline, and online sales? We address this central research question using sales, shopper transaction, and mobile app usage data from a large retail chain that closed 34 stores across states in a month. We use the difference-in-differences approach, creating propensity score-matched control counties and controlling for selection. We examine potential moderators of these effects and underlying mechanisms using individual shopper-level and mobile app user-level data, including analyzing app engagement through topic modeling. Our findings reveal that closing a store opens a Pandora’s box in that it triggers significant net monthly sales loss of $209,317 (more than the average monthly sales of the closed store), representing 18.7% of the retailer’s net sales in the county with the closed store because of spillover effects on other channel purchases by the retailer’s customers in that county. The numbers of the retailer’s active customers, new customers, active app users, new app users, and their mobile app engagement all decline postclosure. Store closure has a negative spillover effect on even nearby shoppers who never shopped at the closed store. Loyal shoppers among nonvisitors to the closed store and app users are more tolerant of store closure than other shoppers. To mitigate adverse effects, retail chains can strategically choose stores closer to other stores in the chain, with a high percentage of in-store discounts and online sales, and a low value of product returns to close. Additionally, they can redirect shoppers in affected counties to the chain’s nearby stores and online (in particular, the mobile app) by offering discounts and promoting store and product information in the app.

Keywords: retailing; store closure; mobile marketing; omnichannel marketing; quasiexperiment; treatment effects; difference in differences; machine learning; topic modeling; heterogeneity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:4:p:820-837

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