IDEAS home Printed from https://ideas.repec.org/a/cxi/jcsit0/v2y2025i1id402.html

Machine Learning Applications in Customer Relationship Management: A Comprehensive Review

Author

Listed:
  • Godwin Abugbilla

    (Ashesi University, Ghana)

Abstract

Customer Relationship Management (CRM) systems have evolved significantly by integrating machine learning (ML) techniques, transforming how businesses understand, predict, and respond to customer behavior. This comprehensive review examines the current landscape of ML applications in CRM, analyzing key techniques, application areas, challenges, and future directions based on recent literature from 2019 to 2025. Our analysis reveals that ML techniques in CRM span from traditional classification and clustering methods to advanced deep learning, natural language processing, and reinforcement learning approaches. Key application areas include churn prediction, customer lifetime value estimation, personalization, sentiment analysis, and customer segmentation. While significant progress has been made, challenges remain in model interpretability, bias mitigation, and production scalability. This review provides insights into emerging trends such as domain-aware language models, graph neural networks, and ethical AI considerations in CRM implementations

Suggested Citation

  • Godwin Abugbilla, 2025. "Machine Learning Applications in Customer Relationship Management: A Comprehensive Review," Journal of Computer Science and Information Technology, Bluemark Publishers, vol. 2(1), pages 41-49, February.
  • Handle: RePEc:cxi:jcsit0:v:2:y:2025:i:1:id:402
    DOI: 10.61424/jcsit.v2i1.402
    as

    Download full text from publisher

    File URL: https://bluemarkpublishers.com/index.php/JCSIT/article/view/402
    File Function: Abstract page
    Download Restriction: no

    File URL: https://bluemarkpublishers.com/index.php/JCSIT/article/download/402/362
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.61424/jcsit.v2i1.402?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:cxi:jcsit0:v:2:y:2025:i:1:id:402. 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: Editorial Team (email available below). General contact details of provider: https://bluemarkpublishers.com/index.php/JCSIT .

    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.