Demand Estimation Using Managerial Responses to Automated Price Recommendations

We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting and apply it data set containing bookings sample of midsized hotels Europe. Using nonbinding observed delay adjustments by decision makers, we demonstrate that control-function approach, combined with state-of-the-art model-selection techniques, can be used isolate exogenous variation across hotel room types over time. confirm these elasticity estimates difference-in-differences approach leverages the same delays makers. However, are more noisy only yield consistent if pooled hotels. then our two classic questions dynamic pricing literature: evolution effects transitory change future due presence strategic buyers. Finally, discuss how empirical applied directly other decision-making situations which recommendation systems used. This paper was accepted Omar Besbes, revenue management market analytics. Funding: D. Garcia acknowledges financial support from Austrian Science Fund [Single Project “Understanding Consumer Search” FWF-P 30922]. Supplemental Material: The online appendix available at https://doi.org/10.1287/mnsc.2021.4261 .