Customer segmentation research in marketing through clustering algorithm analysis

Article type: Research Article

Authors: Zheng, Yulan^{; *}

Affiliations: College of Economic and Management, Fuzhou University, Fuzhou, Fujian, China

Correspondence: [*] Corresponding author. Yulan Zheng, College of Economic and Management, Fuzhou University, Fuzhou, Fujian 350108, China. E-mail: [email protected].

Abstract: In marketing, customer segmentation is a very critical element. This paper focuses on clustering algorithms. First, the commonly used K-means algorithm was introduced, and then, it was optimized using the improved Lion Swarm Optimization (ILSO) algorithm and the Calinski-Harabasz (CH) index. The results of the experiment for the UCI dataset showed that the CH indicator obtained an accurate number of clusters, and the clustering accuracy of the ILSO-K-means algorithm was higher, both above 90%. Then, in customer segmentation, the customers of an enterprise were divided into four groups using the ILSO-K-means algorithm, and different marketing suggestions were given. The experimental analysis proves the usability of the ILSO-K-means algorithm in customer segmentation, which can be further applied in practice.

Keywords: Clustering algorithm, marketing, customer segmentation, lion cluster optimization algorithm, marketing methods

DOI: 10.3233/JIFS-232589

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5441-5448, 2023