IDEAS home Printed from https://ideas.repec.org/a/igg/jaeis0/v16y2025i1p1-13.html
   My bibliography  Save this article

Big Data-Driven E-Commerce Marketing Strategies for Agricultural Products: A Case Study

Author

Listed:
  • Hui Cui

    (Qinhuangdao Vocational and Technical College, China)

  • Weijing Wang

    (Qinhuangdao Vocational and Technical College, China)

  • Shunyu Yang

    (Qinhuangdao Vocational and Technical College, China)

  • Tong Meng

    (Qinhuangdao Vocational and Technical College, China)

Abstract

This study investigates the application of big data in the e-commerce marketing of agricultural products, addressing the challenges of information asymmetry and supply-demand mismatch prevalent in traditional agricultural sales. By leveraging the unique characteristics of agricultural e-commerce, this research explores how big data can enhance consumption insights, refine market positioning, and optimize sales channels. Utilizing data from the Theory of Three Represents e-commerce platform over a 12-month period, the study analyzes the impact of big data on market demand forecasting, dynamic pricing, and personalized marketing. The findings indicate that big data can significantly reduce the demand forecast error rate by 13%, lower the unsalable rate by 23%, and boost the conversion rate by 128.6%. This research provides valuable strategic insights for small and medium-sized e-commerce enterprises, facilitating the transition of agricultural products from a “market-seeking” to a “market-accurately-docking” approach.

Suggested Citation

  • Hui Cui & Weijing Wang & Shunyu Yang & Tong Meng, 2025. "Big Data-Driven E-Commerce Marketing Strategies for Agricultural Products: A Case Study," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global Scientific Publishing, vol. 16(1), pages 1-13, January.
    • Handle: RePEc:igg:jaeis0:v:16:y:2025:i:1:p:1-13
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAEIS.389061
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jaeis0:v:16:y:2025:i:1:p:1-13. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.