Affiliations: [a] Culture and Sports Department, Zhejiang Wanli University, Ningbo, Zhejiang, China | [b] School of Business, Pingxiang University, Pingxiang, Jiangxi, China | [c] Shandong Marine Economic and Cultural Research Institute, Shandong Academy of Social Science, Qingdao, Shandong, China
Correspondence:
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Corresponding author: Guixian Tian, School of Business, Pingxiang University, Pingxiang, Jiangxi 337055, China. E-mail: [email protected].
Abstract: The internal connection and rule diversification of experience marketing data make it difficult to predict the future trend of data. Therefore, it is necessary to mine sports marketing data to guide future marketing strategies. In order to improve the effect of sports marketing data mining, this paper puts forward the algorithm research of experience sports marketing data mining in the cloud computing environment. In the cloud computing environment, based on the idea of data mining, a sports marketing monitoring system is designed and implemented to obtain a large number of evaluation data. The related data is extracted from the database of sports marketing evaluation system, and the data warehouse is constructed by data preprocessing. Using association rule algorithm to realize the data mining module of sports marketing evaluation system, mining the data in the data warehouse, dividing the data set into various data blocks, and then scanning each data block for association rule mining. The experimental results show that the mining algorithm can effectively mine different factors that affect the marketing status. The customer satisfaction obtained after the practical application of this method reaches more than 90%. Sports marketing enterprises can establish benign interaction between users and enterprises according to the mining results of this method, further meet the personalized and differentiated needs of consumers, thereby expanding the influence of enterprises and promoting the realization of marketing.
Keywords: Cloud computing, experiential, sports marketing, data mining
