IDEAS home Printed from https://ideas.repec.org/a/ids/ijpdev/v28y2024i4p288-300.html
   My bibliography  Save this article

The application of AI technology to upgrade retailers' traditional marketing means

Author

Listed:
  • Lu Zhang
  • Ruixue Dong

Abstract

In order to improve the conversion rate of users' purchase and the personalisation of marketing push, the application of AI technology in upgrading traditional marketing methods of retailers was studied. Firstly, it analyses the limitations of traditional retailers' marketing methods. Secondly, aiming at the existing limitations, AI technology is introduced to upgrade marketing means, big data analysis technology is used to mine user behaviour data, collaborative filtering algorithm in machine learning algorithm is used to recommend products individually, and natural language processing technology is used to evaluate user satisfaction. Finally, the application effect of this method is evaluated through a case study. The results showed that the conversion rate of this method is high, with the highest value of 48.3% and the highest score of personalisation degree of 1.0, which showed that it can predict users' purchasing behaviour more accurately and provide more personalised recommendation results.

Suggested Citation

  • Lu Zhang & Ruixue Dong, 2024. "The application of AI technology to upgrade retailers' traditional marketing means," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 28(4), pages 288-300.
  • Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:288-300
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=143262
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijpdev:v:28:y:2024:i:4:p:288-300. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=36 .

    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.