Demand Estimation Using Managerial Responses to Automated Price Recommendations

Daniel Garcia, Juha Tolvanen, Alexander K. Wagner*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 algorith- mic 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 adjust- ments 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 empiri- cal framework can be applied directly to other decision-making situations in which recom- mendation systems are used.
Original languageEnglish
Pages (from-to)7918-7939
Number of pages22
JournalManagement Science
Volume68
Issue number11
Early online date2022
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

Funding Information:
History: Accepted by Omar Besbes, revenue management and market analytics. Open Access Statement: This work is licensed under a Creative Commons Attribution 4.0 International Li-cense. You are free to copy, distribute, transmit and adapt this work, but you must attribute this work as “Management Science. Copyright © 2022 The Author(s). https://doi.org/10.1287/mnsc.2021.4261, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/.” 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.

Publisher Copyright:
© 2022 The Author(s).

Keywords

  • Causal inference
  • Machine learning
  • Demand estimation
  • Revenue management
  • Price recommendations
  • Big data
  • Industrial organization
  • price recommendations
  • big data
  • causal inference
  • machine learning
  • revenue management

Fields of Science and Technology Classification 2012

  • 502 Economics

Cite this