Return management in e-commerce firms: A machine learning approach to predict product returns and examine variables influencing returns

Abstract

The advent of technology with internet penetration has led to exponential growth in sales through online mediums worldwide. Although the growth has increased sales and created opportunities for many firms and sellers to flourish, it also brings in the challenge of customer returns, making it a formidable challenge for businesses. For effective return handling and control, predicting product returns correctly is of utmost importance for firms. The study comprehensively examines the return management process, elucidating its multiple features and characteristics, focusing on the types of returns and the involvement of various stakeholders. Logistic regression, random forest, and gradient-boosting machine learning techniques were used on a data set to scrutinize critical variables influencing returns. Comparing the models using the ROC curve and AUC, gradient boosting performed better in predicting returns than other models. The study identifies significant variables affecting e-commerce return rates, such as total order amount, product category, and cash on delivery charge, an additional fee charged on this mode of payment. Managerial insights recommend that firms conduct due diligence and thorough assessments before introducing products from specific vendors. Quality improvement in certain categories of products, incentivizing card payments, and nominalizing the cash on delivery fees, which could be seen as burdensome by customers, are some of the steps firms need to reduce returns. Strategies like confirming weekend orders and updating replenishment times regularly by vendors can help firms mitigate returns effectively. The study contributes to the academic understanding of return management in e-commerce and offers actionable insights for practitioners.