SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
This addresses a critical issue for fashion e-commerce platforms, customers, and the environment by reducing returns, though it appears incremental as it builds on existing Bayesian methods with new data integration.
The paper tackled the problem of high size and fit related returns in fashion e-commerce by introducing SizeFlags, a probabilistic Bayesian model that leverages weakly annotated customer data, expert feedback, and computer vision, resulting in robust reduction of size-related returns across 14 countries.
E-commerce is growing at an unprecedented rate and the fashion industry has recently witnessed a noticeable shift in customers' order behaviour towards stronger online shopping. However, fashion articles ordered online do not always find their way to a customer's wardrobe. In fact, a large share of them end up being returned. Finding clothes that fit online is very challenging and accounts for one of the main drivers of increased return rates in fashion e-commerce. Size and fit related returns severely impact 1. the customers experience and their dissatisfaction with online shopping, 2. the environment through an increased carbon footprint, and 3. the profitability of online fashion platforms. Due to poor fit, customers often end up returning articles that they like but do not fit them, which they have to re-order in a different size. To tackle this issue we introduce SizeFlags, a probabilistic Bayesian model based on weakly annotated large-scale data from customers. Leveraging the advantages of the Bayesian framework, we extend our model to successfully integrate rich priors from human experts feedback and computer vision intelligence. Through extensive experimentation, large-scale A/B testing and continuous evaluation of the model in production, we demonstrate the strong impact of the proposed approach in robustly reducing size-related returns in online fashion over 14 countries.