Enhancing Product Safety in E-Commerce with NLP
This work addresses product safety for e-commerce platforms like Zalando, but it is incremental as it applies existing NLP methods to a specific business problem.
The paper tackles the problem of identifying unsafe products in e-commerce by using NLP to classify customer claims, resulting in a deployed model that assists in timely investigations with a focus on KPI-driven evaluation.
Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform. Despite stringent quality and safety checking of products listed on these platforms, occasionally customers might receive a product that can pose a safety issue arising out of its use. In this paper, we present an innovative mechanism of how a large scale multinational e-commerce platform, Zalando, uses Natural Language Processing techniques to assist timely investigation of the potentially unsafe products mined directly from customer written claims in unstructured plain text. We systematically describe the types of safety issues that concern Zalando customers. We demonstrate how we map this core business problem into a supervised text classification problem with highly imbalanced, noisy, multilingual data in a AI-in-the-loop setup with a focus on Key Performance Indicator (KPI) driven evaluation. Finally, we present detailed ablation studies to show a comprehensive comparison between different classification techniques. We conclude the work with how this NLP model was deployed.