Disclosing Product Availability in Online Retail
Manufacturing & Service Operations Management, Forthcoming
34 Pages Posted: 20 Jul 2018 Last revised: 1 Aug 2019
Date Written: June 13, 2018
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 on 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 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. 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.
Keywords: online retail, limited inventory information, generalized difference-in-differences, data-driven policy
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