JOURNAL ARTICLE

Data mining algorithm of experiential sports marketing based on cloud computing technology.

  • Published In: Journal of Computational Methods in Sciences & Engineering, 2023, v. 23, n. 6. P. 3315 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chen, Mengzhong; Tian, Guixian; Tao, Yongchao 3 of 3

Abstract

This article focuses on the development and implementation of an experiential sports marketing data mining algorithm within a cloud computing environment to enhance sports marketing strategies. It details the design of a sports marketing monitoring system that collects extensive evaluation data, constructs a data warehouse through preprocessing, and applies association rule mining to identify key factors influencing marketing effectiveness. Experimental results demonstrate that this method achieves over 90% customer satisfaction and enables sports marketing enterprises to better meet personalized consumer needs, thereby improving marketing outcomes. The study also discusses challenges in China's sports industry, including limited innovation, brand awareness, and big data talent shortages, while emphasizing the role of data-driven marketing models in addressing these issues.

Additional Information

  • Source:Journal of Computational Methods in Sciences & Engineering. 2023/12, Vol. 23, Issue 6, p3315
  • Document Type:Article
  • Subject Area:Marketing
  • Publication Date:2023
  • ISSN:1472-7978
  • DOI:10.3233/JCM-226908
  • Accession Number:174523522
  • Copyright Statement:Copyright of Journal of Computational Methods in Sciences & Engineering is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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