The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm
|
|
Author(s): Xiaotong Li (School of Design, Tongmyong University, Busan, South Korea)and Young Sook Lee (School of Design, Tongmyong University, Busan, South Korea)
Copyright: 2024
Volume: 26
Issue: 1
Pages: 16
Source title:
Journal of Cases on Information Technology (JCIT)
DOI: 10.4018/JCIT.336916
Purchase
|
Abstract
Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.
Related Content
|
Zhi Chen, Jie Liu, Ying Wang.
© 2024.
19 pages.
|
|
Ping Zhang, Changrong Lv, Qingying Li, Bori Cong, Jian Liu.
© 2024.
19 pages.
|
|
Lai Xin, Liang Chang Sheng, Jiayu Feng, Hengyan Zhang.
© 2024.
17 pages.
|
|
Abida Ellahi, Yasir Javed, Mohammad Farooq Jan, Zaid Sultan.
© 2024.
20 pages.
|
|
Tongyue Feng, Jiexiang Xu, Zehan Zhou, Yilang Luo.
© 2024.
21 pages.
|
|
Toby Chau, Helen Lv Zhang, Yuyue Gui, Man Fai Lau.
© 2024.
13 pages.
|
|
Andrew J. Setterstrom, Jack T. Marchewka.
© 2024.
22 pages.
|
|
|