Disclosing Product Availability in Online Retail

• 成果类型: Article
• 署名作者: Calvo, Eduard; Cui, Ruomeng; Wagner, Laura
• 署名单位: University of Navarra; IESE Business School; Emory University; Universidade Catolica Portuguesa
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2020.0882
• 发表日期: 2023
• 页码: 427-447
• 关键词: online retail; limited inventory information; scarcity; data-driven policy

摘要

Problem definition: Online retailers disclose product availability to influence customer decisions as a form of pressure selling designed to compel customers to rush into a purchase. Can the revelation of this information drive sales and profitability? We study the effect of disclosing product availability on market outcomes-product sales and returns-and identify the contexts where this effect is most powerful. Academic/practical relevance: Increasing sell-out is key for online retailers to remain profitable in the presence of thin margins and complex operations. We provide insights into how their information disclosure policy-something they can tailor at virtually no cost-can contribute to this important objective. Methodology: We collaborate with an online retailer to procure a year of transaction data on 190,696 products that span 1,290 brands and 472,980 customers. To causally identify our results, we use a generalized difference-in-differences design with matching that exploits one policy of the firm: it discloses product availability only for the last five units. Results: The disclosure of low product availability increases hourly sales-they grow by 13.6%-but these products are more likely to be returned-product return rates increase by 17.0%. Because returns are costly, we also study net sales-product hourly sales minus hourly returns-which increase by 12.5% after the retailer reveals low availability. Managerial implications: The positive effects on sales and profitability amplify over wide assortments and when low-availability signals are abundantly visible and disclosed for deeply discounted products whose sales season is about to end. In addition, we propose a data-driven policy that exploits these results by using machine learning to prescribe the timing of disclosure of scarcity signals in order to boost sales without spiking returns.