IDEAS home Printed from https://ideas.repec.org/a/aza/ama000/y2017v3i3p199-205.html
   My bibliography  Save this article

More than science fiction: Using artificial intelligence and machine-learning techniques to supercharge your marketing

Author

Listed:
  • Thurber, Korey

    (Harte Hanks, USA)

Abstract

With the rise of data creation, channel proliferation and technologies, today’s marketers struggle to find actionable insights to optimise their marketing spend. In the past few years, machine learning — a form of artificial intelligence — has come to the marketer’s aid by finding patterns, deriving insights and making predictions from tremendous amounts of data. This paper analyses machine-learning capabilities, providers and applicability as they relate to marketing.

Suggested Citation

  • Thurber, Korey, 2017. "More than science fiction: Using artificial intelligence and machine-learning techniques to supercharge your marketing," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 3(3), pages 199-205, August.
  • Handle: RePEc:aza:ama000:y:2017:v:3:i:3:p:199-205
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/4322/download/
    Download Restriction: Requires a paid subscription for full access.

    File URL: https://hstalks.com/article/4322/
    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; artificial intelligence; analytics; Big Data; predictive analytics; business intelligence;
    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:2017:v:3:i:3:p:199-205. 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.