Learning from Inventory Availability Information: Field Evidence from Amazon

Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about product quality. Thus, consumer purchasing behavior may be impacted by the availability information. Based on a unique setting from Amazon lightning deals, which displays the percentage of inventory consumed in real time, we explore whether and how consumers learn from inventory availability information. Identifying the effect of learning on consumer decisions has been a notoriously difficult empirical question due to endogeneity concerns. We address this issue by running a randomized field experiment on Amazon. In our experiment, we create exogenous shocks on the inventory availability information to a random subset of Amazon lightning deals. In addition, we track the dynamic purchasing behavior and inventory information for 23,665 lightning deals offered by Amazon in August 2016 and exploit their panel structure to further explore the underlying mechanisms behind learning. We find evidence of consumer learning from inventory information: a decrease in product availability causally attracts more sales in the future; in particular, a 10% increase in past sales leads to a 2.08% increase in cart add-ins in the next hour. Moreover, we show that buyers use observable product characteristics to moderate their inferences when learning from others, and customers primarily learn about the deal’s value rather than the product quality from the availability information.