The Challenges of Deploying an Algorithmic Pricing Tool: Evidence from Airbnb

We examine the challenges of deploying an artificial intelligence pricing tool, Smart Pricing (SP), developed by Airbnb to assist hosts in pricing their properties. SP was made available free of charge and was intended to benefit hosts that rarely adjusted their prices in response to temporal demand fluctuations. However, SP’s adoption rate was low, particularly among hosts who rarely changed their prices prior to SP’s introduction. We estimate a structural model to (i) identify the extent to which adoption costs, hosts’ pessimistic prior beliefs about SP’s performance, and suboptimality of SP contributed to its low adoption rate and (ii) suggest ways to improve SP’s adoption rate that would in turn increase Airbnb’s and hosts’ profitability. We find that (a) the main reason for SP’s low adoption rate is hosts’ pessimistic prior beliefs about SP’s performance, which are stronger among hosts who rarely changed their prices; (b) the benefits of SP for hosts and Airbnb can be increased if Airbnb were to educate the hosts about SP’s actual performance; and (c) because Airbnb does not have information on hosts’ marginal costs, retraining SP using the structural estimates of marginal costs can substantially increase Airbnb’s and hosts’ profitability.

History: Tat Chan served as the senior editor for this article.

Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 506588]; TD Management Data and Analytics Lab Research Grant; BEAR/BI-Org Research Grant.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2024.0964.