Smart Green Nudging: Reducing Product Returns Through Digital Footprints and Causal Machine Learning

In e-commerce, product returns have become a costly and escalating issue for retailers. Beyond the financial implications for businesses, product returns also lead to increased greenhouse gas emissions and the squandering of natural resources. Traditional approaches, such as charging customers for returns, have proven largely ineffective in curbing returns, thus calling for more nuanced strategies to tackle this issue. This paper investigates the effectiveness of informing consumers about the negative environmental consequences of product returns (“green nudging”) to curtail product returns through a large-scale randomized field experiment ( n = 117,304) conducted with a leading European fashion retailer’s online store. Our findings indicate that implementing green nudging can decrease product returns by 2.6% without negatively impacting sales. We then develop and assess a causal machine learning model designed to identify treatment heterogeneities and personalize green nudging (i.e., make nudging “smart”). Our off-policy evaluation indicates that this personalization can approximately double the success of green nudging. The study demonstrates the effectiveness of both subtle marketing interventions and personalization using causal machine learning in mitigating environmentally and economically harmful product returns, thus highlighting the feasibility of employing “Better Marketing for a Better World” approaches in a digital setting.