IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i6id633.html

Building a Dynamic Pricing Engine with Machine Learning for Retail

Author

Listed:
  • Akhilesh Kota

Abstract

This technical article presents a comprehensive analysis of a machine learning-based dynamic pricing engine designed for retail environments. The system leverages advanced microservices architecture, real-time data processing, and sophisticated machine-learning algorithms to optimize pricing decisions across diverse market conditions. We explore the implementation of a scalable solution that combines high-frequency data processing with intelligent price optimization, incorporating competitive analysis, demand patterns, and inventory management. The architecture employs distributed computing principles, featuring robust data ingestion, advanced feature processing, and multi-model machine learning components. Our findings demonstrate significant improvements in revenue optimization, operational efficiency, and market competitiveness while maintaining high system reliability and security standards.

Suggested Citation

  • Akhilesh Kota, 2024. "Building a Dynamic Pricing Engine with Machine Learning for Retail," 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(6), pages 2296-2306, November.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:633
    DOI: 10.32628/CSEIT2410612428
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410612428
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT2410612428
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT2410612428/CSEIT2410612428
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT2410612428?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:i6:id:633. 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.