Reducing Returns in Fashion & Electronics E-Commerce
A Clustering-Based Framework for Identifying High-Risk Orders and Products A Design Science Research at PwC
Q.C. Japikse (TU Delft - Civil Engineering & Geosciences)
J.M. Vleugel – Mentor (TU Delft - Transport, Mobility and Logistics)
J.J. van den Dobbelsteen – Mentor (TU Delft - Medical Instruments & Bio-Inspired Technology)
A.J. van Binsbergen – Graduation committee member (TU Delft - Transport, Mobility and Logistics)
E. Youx – Mentor (PwC Consulting)
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Abstract
The rapid growth of e-commerce has led to escalating product return volumes, generating
substantial economic costs and environmental impact. Existing research largely focuses on either
advanced predictive modelling or process optimisation, yet it commonly overlooks the role of
stakeholders in designing actionable return reduction strategies. This study introduces a practical
and interpretable framework that clusters products and orders features, finding the return risk to support targeted interventions. Using a Design Science Research (DSR) approach, the framework integrates expert interviews, a structured literature review, and extensive data analysis. Statistical tests reveal significant return rate variation across product attributes (category, color, size, price) and order characteristics (order value, quantity, shipping carrier). Three clustering techniques, K-Prototypes, CAVE, and LCC, were evaluated, with LCC providing the most distinctive segmentation of high-risk groups. High-risk products were predominantly found in the Fashion Category, particularly in black and red variants and in sizes other than “One Size”. Furthermore, high-risk orders were associated with large, high-value purchases shipped via DHLDE. The resulting framework enables retailers to implement validated strategies such as improved product information, category-specific return policies, and targeted marketing adjustments. Overall, the approach offers a scalable, stakeholder-aligned foundation for reducing return flows.