IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i1id1548.html

Hyper-Personalization at Scale: Using AI and Support History to Revolutionize Customer Interactions

Author

Listed:
  • Mukul Garg

Abstract

Hyper-personalization in customer support—leveraging unified behavioral, transactional, and historical data—significantly elevates customer experience (CX) while reducing operational friction. This article demonstrates how integrating data engineering pipelines with AI models (recommendation engines, NLP, predictive analytics) enables context-aware, dynamically tailored support interactions at scale. Analysis of two enterprise implementations reveals 18–35% gains in Customer Satisfaction (CSAT) and 20–45% reductions in handling time. Key innovations include real-time 360-degree customer profiling, adaptive knowledge delivery, and generative AI for response synthesis. Technical architectures, performance benchmarks, and best practices are dissected, alongside emerging trends like federated learning and emotion AI.

Suggested Citation

  • Mukul Garg, 2025. "Hyper-Personalization at Scale: Using AI and Support History to Revolutionize Customer Interactions," 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. 11(1), pages 3743-3745, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:1548
    DOI: 10.32628/CSEIT25113369
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113369
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25113369
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT25113369/CSEIT25113369
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT25113369?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:v11:y2025:i1:id:1548. 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.