Machine Learning Models for Predicting Order Returns in Cross-Border E-Commerce

Cai Jia, Ronaldo Juanatas, Apollo Portez and Montaña, Jonan Rose

Economics and Management Innovation, 2025, vol. 2, issue 1, 10-18

Abstract: This study investigates the application of machine learning models to address after-sales service issues in cross-border e-commerce, focusing on predicting order returns to reduce return costs and optimize customer experience. Using H cross-border e-commerce company as a case study, the research employs Random Forest and XGBoost models to identify high-risk return orders. By comparing the performance of these two models, the study highlights their respective strengths and weaknesses and proposes optimization strategies. The findings provide a valuable reference for e-commerce companies to refine their business models, reduce return rates, improve operational efficiency, and enhance customer satisfaction.

Keywords: random forest model; XGBoost model; after-sales issues; prediction (search for similar items in EconPapers)
Date: 2025
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