Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce
This addresses the high costs of returns for fashion e-tailers, though it appears incremental as it builds on existing embedding methods.
The paper tackles the problem of predicting product returns before purchase in fashion e-commerce to reduce costs, proposing a deep neural network that uses embeddings for user taste and body shape along with engineered features, achieving results through live experiments on a major platform.
With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of product returns prior to order placement can be critical for companies. It can facilitate e-tailers to take preemptive measures even before the order is placed, hence reducing overall returns. Furthermore, finding return probability for millions of customers at the cart page in real-time can be difficult. To address this problem we propose a novel approach based on Deep Neural Network. Users' taste & products' latent hidden features were captured using product embeddings based on Bayesian Personalized Ranking (BPR). Another set of embeddings was used which captured users' body shape and size by using skip-gram based model. The deep neural network incorporates these embeddings along with the engineered features to predict return probability. Using this return probability, several live experiments were conducted on one of the major fashion e-commerce platform in order to reduce overall returns.