Customer Segmentation and Churn Prediction in Express Logistics

Yulin Wu; Han Zhang; Hao Wang

doi:10.54097/p1zxfn50

Authors

Yulin Wu
Han Zhang
Hao Wang

DOI:

https://doi.org/10.54097/p1zxfn50

Keywords:

Customer Segmentation, K-means Clustering, Customer Value Analysis, Express Logistics, Customer Churn Prediction

Abstract

With the rapid growth of e-commerce and increasing competition in the express logistics industry, effective customer management has become critical for improving operational efficiency and revenue stability. This study proposes a data-driven framework for refined customer management based on real-world waybill data. Customer behavior is characterized using multi-dimensional features derived from aggregated transaction records. A hybrid segmentation approach combining a customer value four-quadrant model and K-means clustering is employed to identify customer value levels and behavioral patterns. Based on the segmentation results, a Random Forest model is developed to classify customers into churn and non-churn groups and identify high-risk customers. Experimental results show that the clustering model achieves a silhouette coefficient of 0.8797, while the Random Forest model outperforms other models with an accuracy of 0.825. The results demonstrate that the proposed framework effectively identifies high-value customers with elevated churn risk and supports more informed customer management decisions.

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