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Human-machine interactions in pricing: Evidence from two large-scale field experiments

Author

Listed:
  • Huelden, Tobias
  • Jascisens, Vitalijs
  • Roemheld, Lars
  • Werner, Tobias

Abstract

While many companies use algorithms to optimize their pricing, additional human oversight and price interventions are widespread. Human intervention can correct algorithmic flaws and introduce private information into the pricing process, but it may also be based on less sophisticated pricing strategies or suffer from behavioral biases. Using fine-grained data from one of Europe's largest e-commerce companies, we examine the impact of human intervention on the company's commercial performance in two field experiments with around 700,000 products. We show that sizeable heterogeneity exists and present evidence of interventions that harmed commercial performance and interventions that improved firm outcomes. We show that the quality of human interventions can be predicted with algorithmic tools, which allows us to exploit expert knowledge while blocking inefficient interventions.

Suggested Citation

  • Huelden, Tobias & Jascisens, Vitalijs & Roemheld, Lars & Werner, Tobias, 2024. "Human-machine interactions in pricing: Evidence from two large-scale field experiments," DICE Discussion Papers 412, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    • Handle: RePEc:zbw:dicedp:285371
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    References listed on IDEAS

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    1. Ulrike Malmendier & Geoffrey Tate, 2005. "CEO Overconfidence and Corporate Investment," Journal of Finance, American Finance Association, vol. 60(6), pages 2661-2700, December.
    2. Diego Aparicio & Zachary Metzman & Roberto Rigobon, 2021. "The Pricing Strategies of Online Grocery Retailers," NBER Working Papers 28639, National Bureau of Economic Research, Inc.
    3. Nikhil Agarwal & Alex Moehring & Pranav Rajpurkar & Tobias Salz, 2023. "Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology," NBER Working Papers 31422, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Simon Martin & Hans-Theo Normann & Paul Püplichhuisen & Tobias Werner, 2025. "The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms," CESifo Working Paper Series 11629, CESifo.
    2. Geoffrey Decrouez & Tobias Huelden & Paresh Nakhe & Dominik Prugger, 2026. "Measuring Opportunity Cost with Stock Lifetime Value," Papers 2607.01905, arXiv.org.
    3. Normann, Hans-Theo & Martin, Simon & Püplichhuisen, Paul & Werner, Tobias, 2025. "The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms," VfS Annual Conference 2025 (Cologne): Revival of Industrial Policy 325405, Verein für Socialpolitik / German Economic Association.

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    Keywords

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    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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