Learning Demand Curves in B2B Pricing: A New Framework and Case Study
Huashuai Qu, Ilya O. Ryzhov, Michael C. Fu, Eric Bergerson, Megan Kurka, Ludek Kopacek
Production and Operations Management · 2020 · Volume 29 · Issue 5
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.
Keywords
dynamic pricing, B2B pricing, data-driven pricing, learning and earning, prescriptive analytics
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