Learning from Inventory Availability Information: Evidence from Field Experiments on Amazon

Many online retailers provide real-time inventory availability information. Customers can learn from the inventory level and update their beliefs about the product. 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 because of endogeneity concerns. We address this issue by running two randomized field experiments on Amazon in which we create exogenous shocks on the inventory availability information for 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 and use their panel structure to further explore the relative effect of learning. We find evidence of consumers learning from inventory information: a decrease in product availability causally attracts more sales in the future; in particular, a 10% increase in past claims 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; a deep discount weakens the learning momentum, whereas a good product rating amplifies the learning momentum.

This paper was accepted by Serguei Netessine, operations management.