Demand Estimation Using Managerial Responses to Automated Price Recommendations

Daniel Garcia, Juha Tolvanen, Alexander K. Wagner

Publications: Contribution to journalArticlePeer Reviewed


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 ofmidsized 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 modelselection 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 decisionmakers. However, the difference-in-differences estimates aremore 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.

Original languageEnglish
Pages (from-to)7918-7939
Number of pages22
JournalManagement Science
Issue number11
Early online date13 Jan 2022
Publication statusPublished - Nov 2022

Austrian Fields of Science 2012

  • 502013 Industrial economics
  • 502044 Business management
  • 502040 Tourism research


  • big data
  • causal inference
  • machine learning
  • price recommendations
  • revenue management

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