IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i5id378.html

Data-Driven Personalization : Revolutionizing User Experience

Author

Listed:
  • Rohit Sharma

Abstract

Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.

Suggested Citation

  • Rohit Sharma, 2024. "Data-Driven Personalization : Revolutionizing User Experience," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(5), pages 868-877, October.
  • Handle: RePEc:jbh:ijsrcs:v10:y2024:i5:id:378
    DOI: 10.32628/CSEIT241051075
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241051075
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT241051075
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT241051075/CSEIT241051075
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT241051075?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:jbh:ijsrcs:v10:y2024:i5:id:378. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.