IDEAS home Printed from https://ideas.repec.org/a/pal/buseco/v58y2023i1d10.1057_s11369-023-00303-9.html
   My bibliography  Save this article

Science of price experimentation at Amazon

Author

Listed:
  • Joe Cooprider

    (Amazon Retail Pricing Science and Research)

  • Shima Nassiri

    (Amazon Retail Pricing Science and Research)

Abstract

In order to improve prices at Amazon, we created Pricing Labs, a price experimentation platform. Since we do not price discriminate, we must run product-randomized experiments. We discuss how we randomize to prevent spillovers, run different experimental designs (i.e., crossovers) to improve precision, and control for demand trends and differences in treatment groups to get more precise treatment effect estimates.

Suggested Citation

  • Joe Cooprider & Shima Nassiri, 2023. "Science of price experimentation at Amazon," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 58(1), pages 34-41, January.
    • Handle: RePEc:pal:buseco:v:58:y:2023:i:1:d:10.1057_s11369-023-00303-9
    DOI: 10.1057/s11369-023-00303-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s11369-023-00303-9
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s11369-023-00303-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Athey, Susan & Bickel, Peter J. & Chen, Aiyou & Imbens, Guido W. & Pollmann, Michael, 2021. "Semiparametric Estimation of Treatment Effects in Randomized Experiments," Research Papers 3986, Stanford University, Graduate School of Business.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. William Arbour, 2021. "Can Recidivism be Prevented from Behind Bars? Evidence from a Behavioral Program," Working Papers tecipa-683, University of Toronto, Department of Economics.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    5. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    6. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    8. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    9. Stephen Jarvis & Olivier Deschenes & Akshaya Jha, 2022. "The Private and External Costs of Germany’s Nuclear Phase-Out," Journal of the European Economic Association, European Economic Association, vol. 20(3), pages 1311-1346.
    10. Hayakawa, Kazunobu & Keola, Souknilanh & Silaphet, Korrakoun & Yamanouchi, Kenta, 2022. "Estimating the impacts of international bridges on foreign firm locations: a machine learning approach," IDE Discussion Papers 847, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    11. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    12. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    13. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).
    14. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    15. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
    16. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    17. Frondel, Manuel & Kussel, Gerhard & Sommer, Stephan & Vance, Colin, 2019. "Local cost for global benefit: The case of wind turbines," Ruhr Economic Papers 791, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2019.
    18. Xinkun Nie & Stefan Wager, 2017. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects," Papers 1712.04912, arXiv.org, revised Aug 2020.
    19. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    20. Piasenti, Stefano & Valente, Marica & Van Veldhuizen, Roel & Pfeifer, Gregor, 2023. "Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions," Working Papers 2023:7, Lund University, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:buseco:v:58:y:2023:i:1:d:10.1057_s11369-023-00303-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.