IDEAS home Printed from https://ideas.repec.org/p/bon/boncrc/crctr224_2023_425.html

Sparking curiosity or tipping the scales? Targeted advertising with consumer learning

Author

Listed:
  • Andrei Matveenko

  • Egor Starkov

Abstract

This paper argues, in the context of targeted advertising, that receivers' ability to independently acquire information has a non-trivial impact on the sender's optimal disclosure strategy. In our model, a monopolist has an opportunity to launch an advertising campaign and chooses a targeting strategy - which consumers to send its advertisement to. The consumers are uncertain about and heterogeneous in their valuations of the product, and can engage in costly learning about their true valuations. We discover that the firm generally prefers to target consumers who are either indifferent between ignoring and investigating the product, or between investigating and buying it unconditionally. If the firm is uncertain about the consumer appeal of its product, it targets these two distinct groups of consumers simultaneously but may ignore all consumers in between.

Suggested Citation

  • Andrei Matveenko & Egor Starkov, 2023. "Sparking curiosity or tipping the scales? Targeted advertising with consumer learning," CRC TR 224 Discussion Paper Series crctr224_2023_425, University of Bonn and University of Mannheim, Germany.
  • Handle: RePEc:bon:boncrc:crctr224_2023_425
    as

    Download full text from publisher

    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp425
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jean-Michel Benkert, Ludmila Matyskova, Egor Starkov, 2024. "Strategic Attribute Learning," Diskussionsschriften dp2411, Universitaet Bern, Departement Volkswirtschaft.
    2. Jean-Michel Benkert & Ludmila Matyskova & Egor Starkov, 2024. "Strategic Attribute Learning," Papers 2412.10024, arXiv.org, revised Nov 2025.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bon:boncrc:crctr224_2023_425. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CRC Office (email available below). General contact details of provider: https://www.crctr224.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.