Author(s)

ZhuoRan Li

Affiliation(s)

School of Economics, Nanjing University of Finance & Economics, Nanjing 210023, Jiangsu, China.

Corresponding Author

ZhuoRan Li

ABSTRACT

This study aims to segment customers using the application of the K-means clustering algorithm and predict customer churn using the random forest method. Transactional data were used, including the order date, customer name, region, logistics company, quantity bought, payment amount, and frequency bought. K-means clustering was applied to group customers into segments, while a random forest model was constructed to predict customer churn. K-means clustering could determine four customer segments with different purchasing habits. Random forest model could predict customer churn and could find that attributes such as payment value and region were the most significant to use while determining the probability of churn. Results of this study verify that employing K-means clustering and random forest simultaneously for customer segmentation and customer churn prediction is efficient and assists in obtaining considerable insights for precision marketing.

KEYWORDS

K-means; Random forest; Customer segmentation; Churn prediction

CITE THIS PAPER

ZhuoRan Li. Customer segmentation and churn prediction based on K-means and random forest: a case study of e-commerce data. Eurasia Journal of Science and Technology. 2025, 7(2): 14-19. DOI: https://doi.org/10.61784/ejst3071.

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