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Learning Demand Curves in B2B Pricing: A New Framework and Case Study

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  • Huashuai Qu
  • Ilya O. Ryzhov
  • Michael C. Fu
  • Eric Bergerson
  • Megan Kurka
  • Ludek Kopacek

Abstract

In business‐to‐business (B2B) pricing, a seller seeks to maximize revenue obtained from high‐volume transactions involving a wide variety of buyers, products, and other characteristics. Buyer response is highly uncertain, and the seller only observes whether buyers accept or reject the offered prices. These deals are also subject to high opportunity cost, since revenue is zero if the price is rejected. The seller must adapt to this uncertain environment and learn quickly from new deals as they take place. We propose a new framework for statistical and optimal learning in this problem, based on approximate Bayesian inference, which has the ability to measure and update the seller’s uncertainty about the demand curve based on new deals. In a case study, based on historical data, we show that our approach offers significant practical benefits.

Suggested Citation

  • Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    • Handle: RePEc:bla:popmgt:v:29:y:2020:i:5:p:1287-1306
    DOI: 10.1111/poms.13161
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    References listed on IDEAS

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    Cited by:

    1. Bo Feng & Jixin Zhao & Zheyu Jiang, 2022. "Robust pricing for airlines with partial information," Annals of Operations Research, Springer, vol. 310(1), pages 49-87, March.

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