IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-036-7_264.html

The Application of Big Data Analysis in the Hierarchical Management of Automobile Customers

In: Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

Author

Listed:
  • Haoran Wang

    (Xian Tieyi High School)

Abstract

In recent years, automobiles are becoming one of the mainstream people's travel modes. The automobile industry has benefited from big data analytics to improve their sales and marketing efficiency. With the increasing popularity of network applications, the Internet is changing the business models of traditional industries. Traditional industries are undergoing online and digital transformation. This paper summarizes the importance of bigdata analysis and its application in hierarchical management of automobile customers. In general, automotive big data can be roughly divided into identity data, transaction data, and behavior data. Automobile manufactures can find out more valuable information of these research data in the automobile industry with the help of big data analysis and data mining. In addition, the classification of customers has also been discussed. It could be one of the potential solutions is customer analytics which use the lowest cost value to maintain the stickiness of customers and minimize the loss of customers.

Suggested Citation

  • Haoran Wang, 2022. "The Application of Big Data Analysis in the Hierarchical Management of Automobile Customers," Advances in Economics, Business and Management Research, in: Yushi Jiang & Yuriy Shvets & Hrushikesh Mallick (ed.), Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), pages 1770-1774, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6463-036-7_264
    DOI: 10.2991/978-94-6463-036-7_264
    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:978-94-6463-036-7_264. 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.