IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-488-4_4.html

Research on Optimization of Taobao Product Sales Forecast Based on ARIMA model

In: Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024)

Author

Listed:
  • Chengyang Li

    (Shanghai Lixin University of Accounting and Finance, School of Information Management)

Abstract

Accurate sales forecast is of great guiding significance for online e-commerce. First of all, merchants can make corresponding marketing strategies and inventory plans in advance according to the predicted sales volume of goods, so as to meet the needs of consumers and achieve profits, while avoiding the stock shortage or backlog of goods, so as to improve operational efficiency and customer satisfaction, and help merchants better understand consumer demand and market trends. By analyzing historical sales data and consumer reviews, merchants can tap into consumers’ shopping habits and preferences, as well as hot items and trends in the market. This information is very important for merchants to formulate marketing strategies and adjust product positioning, which can help merchants better meet market needs and consumer expectations.

Suggested Citation

  • Chengyang Li, 2024. "Research on Optimization of Taobao Product Sales Forecast Based on ARIMA model," Advances in Economics, Business and Management Research, in: Junfeng Liao & Hongbo Li & Edward H. K. Ng (ed.), Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024), pages 27-37, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-488-4_4
    DOI: 10.2991/978-94-6463-488-4_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6463-488-4_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.