Semantic Scholar Open Access 2022 122 sitasi

B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM

Xiancheng Xiahou Yoshio Harada

Lihat Sumber DOI

Abstrak

Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction model based on the combination of k-means customer segmentation and support vector machine (SVM) prediction. The method divides customers into three categories and determines the core customer groups. The support vector machine and logistic regression were compared to predict customer churn. The results show that each prediction index after customer segmentation was significantly improved, which proves that k-means clustering segmentation is necessary. The accuracy of the SVM prediction was higher than that of the logistic regression prediction. These research results have significance for customer relationship management of B2C e-commerce enterprises.

Topik & Kata Kunci

Computer Science

Penulis (2)

Xiancheng Xiahou

Yoshio Harada

Format Sitasi

APA MLA BibTeX

Xiahou, X., Harada, Y. (2022). B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM. https://doi.org/10.3390/jtaer17020024

Akses Cepat

Lihat di Sumber doi.org/10.3390/jtaer17020024

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 122×
Sumber Database: Semantic Scholar
DOI: 10.3390/jtaer17020024
Akses: Open Access ✓