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Dynamic Pricing with Online Reviews

Author

Listed:
  • Dongwook Shin

    (HKUST Business School, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

  • Stefano Vaccari

    (Global Data Hub, Global Digital Solutions, ENEL Global Services, 00198 Rome, Italy)

  • Assaf Zeevi

    (Graduate School of Business, Columbia University, New York, New York 10025)

Abstract

This paper investigates how the pricing policy of a revenue-maximizing monopolist is influenced by the social learning dynamics of customers who use online reviews to estimate the quality of the product. A salient feature of our problem is that the customers’ willingness to pay, and hence the demand function, evolves over time in conjunction with the online reviews. The monopolist strives to maximize its total expected revenue over a finite horizon by adjusting prices in response to these dynamics. The revenue maximization problem is studied using two different review models: a quality-based review model, where customers report their experienced quality, and a value-based review model, where reviews internalize experienced quality as well as the purchase price. To formulate the problem in tractable form, we derive a fluid model that provides a good approximation of the system dynamics when the volume of sales is large. This formulation lends itself to key structural insights into the interactions between optimal pricing policies and review dynamics. In particular, we identify critical time scales and social learning regimes that sharply separate the efficacy of dynamic pricing vis-à-vis fixed-price strategies. Furthermore, we demonstrate the impact of the quality-based and value-based review models on key structural properties of the optimal pricing policies. These structural insights are also elucidated in an illustrative simulation study based on data from an online marketplace.

Suggested Citation

  • Dongwook Shin & Stefano Vaccari & Assaf Zeevi, 2023. "Dynamic Pricing with Online Reviews," Management Science, INFORMS, vol. 69(2), pages 824-845, February.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:2:p:824-845
    DOI: 10.1287/mnsc.2022.4387
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    References listed on IDEAS

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    1. William L. Cooper, 2002. "Asymptotic Behavior of an Allocation Policy for Revenue Management," Operations Research, INFORMS, vol. 50(4), pages 720-727, August.
    2. Yiangos Papanastasiou & Nicos Savva, 2017. "Dynamic Pricing in the Presence of Social Learning and Strategic Consumers," Management Science, INFORMS, vol. 63(4), pages 919-939, April.
    3. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    4. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    5. 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.
    6. Milgrom, Paul & Roberts, John, 1986. "Price and Advertising Signals of Product Quality," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 796-821, August.
    7. Kenneth L. Judd & Michael H. Riordan, 1994. "Price and Quality in a New Product Monopoly," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 773-789.
    8. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    9. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    10. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    11. André Stenzel & Christoph Wolf & Peter Schmidt, 2020. "Pricing for the Stars Dynamic Pricing in the Presence of Rating Systems," CRC TR 224 Discussion Paper Series crctr224_2020_143, University of Bonn and University of Mannheim, Germany.
    12. He, Qiao-Chu & Chen, Ying-Ju, 2018. "Dynamic pricing of electronic products with consumer reviews," Omega, Elsevier, vol. 80(C), pages 123-134.
    13. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    14. Constantinos Maglaras & Joern Meissner, 2006. "Dynamic Pricing Strategies for Multiproduct Revenue Management Problems," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 136-148, July.
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    Cited by:

    1. Dipankar Das, 2023. "A Model of Competitive Assortment Planning Algorithm," Papers 2307.09479, arXiv.org.

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