A Model for Predicting E-Commerce Product Returns Using Hybrid CNN-GRU

Abstract

The aim of this work is to predict the product return rate of e-commerce using deep learning algorithms. A novel hybrid model combining Convolutional Neural Network(CNNs) and Gated Recurrent Units (GRUs) is proposed to predict e-commerce product return rates. The developed model is trained and validated on a large e-commerce dataset with features like consumer demographic informations, product details, transaction and product return details, and consumer feedbacks. The return history of every product is learned and products least and most sold is analyzed. Based on all this analysis the return of products in the future is predicted. The proposed work demonstrates that the hybrid CNN-GRU model outperforms conventional models and  standalone CNN and GRU architectures with an accuracy of 83%. Also focus is made on understanding the features influencing the product returnsthat help firms to make data driven decisions and minimize product returns . This type of predictive models can be applied to enhance the business strategies in making informed decisions enhancing the overall satisfaction of the consumer and improving the revenue of the business.