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Learning Demand Curves in B2B Pricing: A New Framework and Case Study
Author(s) -
Qu Huashuai,
Ryzhov Ilya O.,
Fu Michael C.,
Bergerson Eric,
Kurka Megan,
Kopacek Ludek
Publication year - 2020
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13161
Subject(s) - variety (cybernetics) , revenue , computer science , inference , bayesian inference , bayesian probability , dynamic pricing , microeconomics , economics , artificial intelligence , finance
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.