Demand Estimation Using Managerial Responses to Automated Price Recommendations

Abstract

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

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