Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation
In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting ex...
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| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2018
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| Online Access: | http://hdl.handle.net/1721.1/119156 https://orcid.org/0000-0003-2809-9623 https://orcid.org/0000-0002-4650-1519 https://orcid.org/0000-0001-7444-2053 |
| _version_ | 1826189192685682688 |
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| author | Cheung, Wang Chi Simchi-Levi, David Wang, He |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Cheung, Wang Chi Simchi-Levi, David Wang, He |
| author_sort | Cheung, Wang Chi |
| collection | MIT |
| description | In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst-case regret—i.e., the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log(m)T), or m iterations of the logarithm. Furthermore, we describe an implementation of this pricing policy at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings. |
| first_indexed | 2024-09-23T08:10:59Z |
| format | Article |
| id | mit-1721.1/119156 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:10:59Z |
| publishDate | 2018 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1191562022-09-30T08:09:07Z Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation Cheung, Wang Chi Simchi-Levi, David Wang, He Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society Massachusetts Institute of Technology. Operations Research Center David Simchi-Levi Cheung, Wang Chi Simchi-Levi, David Wang, He In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst-case regret—i.e., the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log(m)T), or m iterations of the logarithm. Furthermore, we describe an implementation of this pricing policy at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings. 2018-11-16T19:11:55Z 2018-11-16T19:11:55Z 2017-11 Article http://purl.org/eprint/type/JournalArticle 0030-364X 1526-5463 http://hdl.handle.net/1721.1/119156 Cheung, Wang Chi, et al. “Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation.” Operations Research, vol. 65, no. 6, Dec. 2017, pp. 1722–31. https://orcid.org/0000-0003-2809-9623 https://orcid.org/0000-0002-4650-1519 https://orcid.org/0000-0001-7444-2053 en_US http://dx.doi.org/10.1287/opre.2017.1629 Operations Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) Prof. Simchi-Levi via Elizabeth Soergel |
| spellingShingle | Cheung, Wang Chi Simchi-Levi, David Wang, He Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title | Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title_full | Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title_fullStr | Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title_full_unstemmed | Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title_short | Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation |
| title_sort | technical note dynamic pricing and demand learning with limited price experimentation |
| url | http://hdl.handle.net/1721.1/119156 https://orcid.org/0000-0003-2809-9623 https://orcid.org/0000-0002-4650-1519 https://orcid.org/0000-0001-7444-2053 |
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