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Persuading Customers to Buy Early: The Value of Personalized Information Provisioning

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  • Kimon Drakopoulos

    (Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Shobhit Jain

    (Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Ramandeep Randhawa

    (Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

We study a pricing and information provisioning game between a better-informed seller (such as a retailer) and its customers. The seller is (ex post) better informed about product availability and can choose how to communicate this information to the customers. The customers are heterogeneous in their valuation for the product. The firm optimizes on publicly posted prices (which are the same for all customers) and its information provisioning (which can be personalized). Using a Bayesian persuasion framework, we find that public information provisioning, in which the firm sends the same information to all customers, has limited value. However, personalized information provisioning, in which the firm can share different information with different customers, has significant value and has attributes very similar to personalized pricing. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Suggested Citation

  • Kimon Drakopoulos & Shobhit Jain & Ramandeep Randhawa, 2021. "Persuading Customers to Buy Early: The Value of Personalized Information Provisioning," Management Science, INFORMS, vol. 67(2), pages 828-853, February.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:2:p:828-853
    DOI: 10.1287/mnsc.2020.3580
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    References listed on IDEAS

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    4. Zheng, Shiyuan & Jia, Rongwen & Shang, Wen-Long & Fu, Xiaowen & Wang, Kun, 2023. "Promote transport facility Resilience: Persuasion or Subsidy?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    5. Can Küçükgül & Özalp Özer & Shouqiang Wang, 2022. "Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms," Management Science, INFORMS, vol. 68(7), pages 4899-4918, July.

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