  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.
