IDEAS home Printed from https://ideas.repec.org/a/aza/ama000/y2024v10i2p158-175.html
   My bibliography  Save this article

Machine learning and AI in marketing analytics: Leveraging the survey data to find customers

Author

Listed:
  • Fogarty, David

    (Associate Professor, National University, USA)

  • Cui, Xinlei

    (Graduate Research Assistant, New York University, Stern School of Business, USA)

Abstract

The field of marketing analysis in the digital era faces numerous challenges. Despite the availability of vast amounts of structured and unstructured data, practitioners have yet to fully harness the potential of machine learning models. This paper addresses this gap by investigating how to find targeted customers and expand the emerging market by implementing machine learning models to process survey text data and provides empirical evidence through model evaluation experiments. The research problem focuses on demonstrating the effectiveness of machine learning and AI models in optimising value creation and enhancing competitive advantages in marketing practices. The paper employs mixed methods and presents experimental results, leading to conclusions highlighting the benefits of improving data quality to strengthen the performance of machine learning models. This research also provides insights into model selection and offers a foundation for future researchers and marketing analysts to interpret and evaluate machine learning models effectively by multiple efficient metrics.

Suggested Citation

  • Fogarty, David & Cui, Xinlei, 2024. "Machine learning and AI in marketing analytics: Leveraging the survey data to find customers," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 10(2), pages 158-175, September.
  • Handle: RePEc:aza:ama000:y:2024:v:10:i:2:p:158-175
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/8682/download/
    Download Restriction: Requires a paid subscription for full access.

    File URL: https://hstalks.com/article/8682/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    machine learning; marketing; artificial intelligence; automated classification; consumer targeting; analytics;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

    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:aza:ama000:y:2024:v:10:i:2:p:158-175. 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: Henry Stewart Talks (email available below). General contact details of provider: .

    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.