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Demand Estimation Using Managerial Responses to Automated Price Recommendations

Author

Listed:
  • Daniel Garcia

    (Department of Economics, University of Vienna, 1090 Vienna, Austria)

  • Juha Tolvanen

    (Department of Economics, University of Vienna, 1090 Vienna, Austria)

  • Alexander K. Wagner

    (Vienna Center for Experimental Economics, University of Vienna, 1090 Vienna, Austria; Department of Economics, University of Salzburg, 5020 Salzburg, Austria)

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.

Suggested Citation

  • Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:11:p:7918-7939
    DOI: 10.1287/mnsc.2021.4261
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    More about this item

    Keywords

    big data; causal inference; machine learning; revenue management; price recommendations;
    All these keywords.

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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