Pricing with Limited Knowledge of Demand

How should a firm price a new product for which little is known about demand? We propose a pricing rule that can be used if the firm can estimate (even roughly) the maximum price it can charge and still expect to sell some units, and the firm need not know in advance the quantity it will sell. The r...

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Main Authors: Cohen, Maxime C., Perakis, Georgia, Pindyck, Robert S
Other Authors: Sloan School of Management
Format: Article
Published: Association for Computing Machinery (ACM) 2019
Online Access:http://hdl.handle.net/1721.1/120729
https://orcid.org/0000-0002-0888-9030
https://orcid.org/0000-0001-8296-9875
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author Cohen, Maxime C.
Perakis, Georgia
Pindyck, Robert S
author2 Sloan School of Management
author_facet Sloan School of Management
Cohen, Maxime C.
Perakis, Georgia
Pindyck, Robert S
author_sort Cohen, Maxime C.
collection MIT
description How should a firm price a new product for which little is known about demand? We propose a pricing rule that can be used if the firm can estimate (even roughly) the maximum price it can charge and still expect to sell some units, and the firm need not know in advance the quantity it will sell. The rule is simple: Set price as though the demand curve were linear. We show that if the true demand curve is one of many commonly used demand functions, or even a more complex function, and if marginal cost is known and constant, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, calculate expected profit performance for randomly generated demand curves, and evaluate the welfare implications of our pricing rule.
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spelling mit-1721.1/1207292022-09-28T16:50:11Z Pricing with Limited Knowledge of Demand Cohen, Maxime C. Perakis, Georgia Pindyck, Robert S Sloan School of Management Perakis, Georgia Pindyck, Robert S How should a firm price a new product for which little is known about demand? We propose a pricing rule that can be used if the firm can estimate (even roughly) the maximum price it can charge and still expect to sell some units, and the firm need not know in advance the quantity it will sell. The rule is simple: Set price as though the demand curve were linear. We show that if the true demand curve is one of many commonly used demand functions, or even a more complex function, and if marginal cost is known and constant, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, calculate expected profit performance for randomly generated demand curves, and evaluate the welfare implications of our pricing rule. 2019-03-05T16:35:54Z 2019-03-05T16:35:54Z 2016-07 2019-02-27T16:03:35Z Article http://purl.org/eprint/type/ConferencePaper 978-1-4503-3936-0 http://hdl.handle.net/1721.1/120729 Cohen, Maxime C., Georgia Perakis, and Robert S. Pindyck. “Pricing with Limited Knowledge of Demand.” Proceedings of the 2016 ACM Conference on Economics and Computation - EC ’16, 24-28 July, 2016, Maastricht, The Netherlands, ACM, 2016. https://orcid.org/0000-0002-0888-9030 https://orcid.org/0000-0001-8296-9875 http://dx.doi.org/10.1145/2940716.2940734 Proceedings of the 2016 ACM Conference on Economics and Computation - EC '16 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) Other repository
spellingShingle Cohen, Maxime C.
Perakis, Georgia
Pindyck, Robert S
Pricing with Limited Knowledge of Demand
title Pricing with Limited Knowledge of Demand
title_full Pricing with Limited Knowledge of Demand
title_fullStr Pricing with Limited Knowledge of Demand
title_full_unstemmed Pricing with Limited Knowledge of Demand
title_short Pricing with Limited Knowledge of Demand
title_sort pricing with limited knowledge of demand
url http://hdl.handle.net/1721.1/120729
https://orcid.org/0000-0002-0888-9030
https://orcid.org/0000-0001-8296-9875
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