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Dynamic Pricing through Data Sampling

Author

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  • Maxime C. Cohen
  • Ruben Lobel
  • Georgia Perakis

Abstract

We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue throughout the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show computationally that regret†based objectives can perform well when compared to average revenue maximization and to a Bayesian approach. The modeling approach proposed in this study could be particularly useful for risk†averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling†based solution.

Suggested Citation

  • Maxime C. Cohen & Ruben Lobel & Georgia Perakis, 2018. "Dynamic Pricing through Data Sampling," Production and Operations Management, Production and Operations Management Society, vol. 27(6), pages 1074-1088, June.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:6:p:1074-1088
    DOI: 10.1111/poms.12854
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    Citations

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    Cited by:

    1. Yan Liu & Ningyuan Chen, 2022. "Dynamic Pricing with Money‐Back Guarantees," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 941-962, March.
    2. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.
    3. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    4. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
    5. Qiu, Ruozhen & Sun, Yue & Zhou, Hongcheng & Sun, Minghe, 2023. "Dynamic pricing and quick response of a retailer in the presence of strategic consumers: A distributionally robust optimization approach," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1270-1298.
    6. Sentao Miao & Xi Chen & Xiuli Chao & Jiaxi Liu & Yidong Zhang, 2022. "Context‐based dynamic pricing with online clustering," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3559-3575, September.
    7. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.
    8. Hou, Lihua & Nie, Tengfei & Zhang, Jianghua, 2024. "Pricing and inventory strategies for perishable products in a competitive market considering strategic consumers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    9. Cenying Yang & Yihao Feng & Andrew Whinston, 2022. "Dynamic Pricing and Information Disclosure for Fresh Produce: An Artificial Intelligence Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 155-171, January.
    10. Bo Li & Antonio Arreola‐Risa, 2022. "Minimizing conditional value‐at‐risk under a modified basestock policy," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1822-1838, April.

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