Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

Xiaotong Li, Young Sook Lee

Source Title: Journal of Cases on Information Technology (JCIT)26(1)

ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9798369323878|DOI: 10.4018/JCIT.336916

MLA

Li, Xiaotong, and Young Sook Lee. "Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm." JCIT vol.26, no.1 2024: pp.1-16. http://doi.org/10.4018/JCIT.336916

APA

Li, X. & Lee, Y. S. (2024). Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm. Journal of Cases on Information Technology (JCIT), 26(1), 1-16. http://doi.org/10.4018/JCIT.336916

Chicago

Li, Xiaotong, and Young Sook Lee. "Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm," Journal of Cases on Information Technology (JCIT) 26, no.1: 1-16. http://doi.org/10.4018/JCIT.336916

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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.