IDEAS home Printed from https://ideas.repec.org/a/aza/ama000/y2019v4i3p214-221.html
   My bibliography  Save this article

Marketing analytics in the age of machine learning

Author

Listed:
  • Booth, David

Abstract

Machine learning presents unique challenges and tremendous opportunities for today’s marketer, and while many applications have already become common practice, the future holds exciting use cases, some of which are in development and others yet to be imagined. Leveraging the vast amount of data available in the exhaust stream of digital marketing and advertising, and coupling this with almost limitless data storage and processing capacity, the move from rules-based to intelligent analysis is driving efficiencies across a number of marketing initiatives and capabilities. From intelligent bidding and the serving of advertisements across the most common digital channels to advanced segmentation, audience creation, attribution and more, machine learning has already established itself across a large and complex marketing ecosystem. Recent applications in purchase intent and churn modelling, data-driven retargeting and even data-driven creative are using machine learning to provide competitive advantage now and into the future.

Suggested Citation

  • Booth, David, 2019. "Marketing analytics in the age of machine learning," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 4(3), pages 214-221, February.
    • Handle: RePEc:aza:ama000:y:2019:v:4:i:3:p:214-221
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/5014/download/
    Download Restriction: Requires a paid subscription for full access.
    File URL: https://hstalks.com/article/5014/
    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.

    Citations

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


    Cited by:

    1. Marika Parcesepe & Francesca Forgione & Celeste Maria Ciampi & Gerardo Nisco Ciarcia & Valeria Guerriero & Mariaconsiglia Iannotti & Letizia Saviano & Maria Letizia Melisi & Salvatore Rampone, 2023. "Towards the automated evaluation of product packaging in the Food&Beverage sector through data science/machine learning methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2269-2280, June.

    More about this item

    Keywords

    artificial intelligence; machine learning; marketing analytics; predictive; digital analytics; marketing technology;
    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:2019:v:4:i:3:p:214-221. 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.