Why do customers return products? Using customer reviews to predict product return behaviors

Product returns are an increasing environmental problem, as an estimated 25% of returned products end up as landfill [10]. Returns are expensive for retailers as well, and it is estimated that 15-40% of all online purchases are returned [34]. The problem could be mitigated by identifying issues with a product that are likely to lead to its return, before many have sold. Understanding and predicting return reasons can help identify manufacturing defects, misleading information in the product description or reviews, issues with a seller or shipping company, and customers who are habitual returners. While there has been much work to identify and predict return volume, little attention has been given to the reasons for the return. In this paper we explore how customer reviews could be used as signals to identify return reasons. We developed a multi-class classifier to predict return reasons, with a fine-tuned BERT-based model to encode customer review text as features. The classifier with customer review text yields an increase of more than 20% average precision over the baseline classifier with no reviews text. We also showed that we can use aggregated review information to predict product return in case the customer returning the product did not write a review. Lastly we show that reviews can be used to identify nuanced return reasons beyond what the customer indicated.