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Dynamic Pricing with Online Learning and Strategic Consumers: An Application of the Aggregating Algorithm

Author

Listed:
  • Tatsiana Levina

    (School of Business, Queen's University, Kingston, Ontario, Canada K7L 3N6)

  • Yuri Levin

    (School of Business, Queen's University, Kingston, Ontario, Canada K7L 3N6)

  • Jeff McGill

    (School of Business, Queen's University, Kingston, Ontario, Canada K7L 3N6)

  • Mikhail Nediak

    (School of Business, Queen's University, Kingston, Ontario, Canada K7L 3N6)

Abstract

We study the problem faced by a monopolistic company that is dynamically pricing a perishable product or service and simultaneously learning the demand characteristics of its customers. In the learning procedure, the company observes the sales history over consecutive learning stages and predicts consumer demand by applying an aggregating algorithm (AA) to a pool of online stochastic predictors. Numerical implementation uses finite-sample distribution approximations that are periodically updated using the most recent sales data. These are subsequently altered with a random step characterizing the stochastic predictors. The company's pricing policy is optimized with a simulation-based procedure integrated with AA. The methodology of the paper is general and independent of specific distributional assumptions. We illustrate this procedure on a demand model for a market in which customers are aware that pricing is dynamic, may time their purchases strategically, and compete for a limited product supply. We derive the form of this demand model using a game-theoretic consumer choice model and study its structural properties. Numerical experiments demonstrate that the learning procedure is robust to deviations of the actual market from the model of the market used in learning.

Suggested Citation

  • Tatsiana Levina & Yuri Levin & Jeff McGill & Mikhail Nediak, 2009. "Dynamic Pricing with Online Learning and Strategic Consumers: An Application of the Aggregating Algorithm," Operations Research, INFORMS, vol. 57(2), pages 327-341, April.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:2:p:327-341
    DOI: 10.1287/opre.1080.0577
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    as
    1. João L. Assunção & Robert J. Meyer, 1993. "The Rational Effect of Price Promotions on Sales and Consumption," Management Science, INFORMS, vol. 39(5), pages 517-535, May.
    2. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    3. Qian Liu & Garrett J. van Ryzin, 2008. "Strategic Capacity Rationing to Induce Early Purchases," Management Science, INFORMS, vol. 54(6), pages 1115-1131, June.
    4. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    5. Lin, Kyle Y., 2006. "Dynamic pricing with real-time demand learning," European Journal of Operational Research, Elsevier, vol. 174(1), pages 522-538, October.
    6. Nicholas C. Petruzzi & Maqbool Dada, 2002. "Dynamic pricing and inventory control with learning," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(3), pages 303-325, April.
    7. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    8. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    9. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29, January.
    10. David Besanko & Wayne L. Winston, 1990. "Optimal Price Skimming by a Monopolist Facing Rational Consumers," Management Science, INFORMS, vol. 36(5), pages 555-567, May.
    11. Balvers, Ronald J & Cosimano, Thomas F, 1990. "Actively Learning about Demand and the Dynamics of Price Adjustment," Economic Journal, Royal Economic Society, vol. 100(402), pages 882-898, September.
    12. Yossi Aviv & Amit Pazgal, 2008. "Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 339-359, December.
    13. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    14. Wedad Elmaghraby & Altan Gülcü & P{i}nar Keskinocak, 2008. "Designing Optimal Preannounced Markdowns in the Presence of Rational Customers with Multiunit Demands," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 126-148, June.
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    Cited by:

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    2. Le Cadre, Hélène & Pagnoncelli, Bernardo & Homem-de-Mello, Tito & Beaude, Olivier, 2019. "Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices," European Journal of Operational Research, Elsevier, vol. 272(1), pages 270-291.
    3. Wu, Meng & Ran, Yun & Zhu, Stuart X., 2022. "Optimal pricing strategy: How to sell to strategic consumers?," International Journal of Production Economics, Elsevier, vol. 244(C).
    4. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    5. J. Michael Harrison & N. Bora Keskin & Assaf Zeevi, 2012. "Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution," Management Science, INFORMS, vol. 58(3), pages 570-586, March.
    6. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    7. Omar Besbes & Assaf Zeevi, 2011. "On the Minimax Complexity of Pricing in a Changing Environment," Operations Research, INFORMS, vol. 59(1), pages 66-79, February.
    8. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
    9. Renato Matta & Timothy J. Lowe, 2023. "Product price alignment with seller service rating and consumer satisfaction," Annals of Operations Research, Springer, vol. 320(2), pages 695-725, January.
    10. Omar Besbes & Denis Sauré, 2014. "Dynamic Pricing Strategies in the Presence of Demand Shifts," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 513-528, October.
    11. Kazemi, Mohammad Sadegh & Fotopoulos, Stergios B. & Wang, Xinchang, 2023. "Minimizing online retailers’ revenue loss under a time-varying willingness-to-pay distribution," International Journal of Production Economics, Elsevier, vol. 257(C).
    12. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Working Papers hal-01353763, HAL.
    13. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    14. Hélène Le Cadre & Bernardo Pagnoncelli & Tito Homem-De-Mello & Olivier Beaude, 2018. "Designing Coalition-Based Fair and Stable Pricing Mechanisms Under Private Information on Consumers' Reservation Prices," Post-Print hal-01353763, HAL.
    15. Gerrard, Russell & Hiabu, Munir & Kyriakou, Ioannis & Nielsen, Jens Perch, 2019. "Communication and personal selection of pension saver’s financial risk," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1102-1111.
    16. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    17. Jiang, Yuanchun & Liu, Yezheng & Shang, Jennifer & Yildirim, Pinar & Zhang, Qingfu, 2018. "Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives," European Journal of Operational Research, Elsevier, vol. 267(2), pages 612-627.
    18. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    19. Hu, Shu & Ma, Zu-Jun & Sheu, Jiuh-Biing, 2019. "Optimal prices and trade-in rebates for successive-generation products with strategic consumers and limited trade-in duration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 124(C), pages 92-107.
    20. Malo Huard & Rémy Garnier & Gilles Stoltz, 2020. "Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method," Working Papers hal-02794320, HAL.
    21. Kamrad, Bardia & Ord, Keith & Schmidt, Glen M., 2021. "Maximizing the probability of realizing profit targets versus maximizing expected profits: A reconciliation to resolve an agency problem," International Journal of Production Economics, Elsevier, vol. 238(C).

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