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

Year: 2024

Type: article

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.

Source: Proceedings of Business and Economic Studies

DOI: 10.26689/pbes.v7i6.9105

Authors: Jia Cai, Ronaldo Juanatas, Apollo Portez, Jonan Rose Montaña

Institutions: Technological University of the Philippines, Henan Finance University

Language: English

Topic: E-commerce and Technology Innovations

Subfield: Business and International Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4405689204

FWCI:

Cites: 1

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Related to: 10