Social, Behavioral and Experimental Economics Seminar: Kevin Bauer

The Smart Green Nudge: Reducing Product Returns through Enriched Digital Footprints & Causal Machine Learning

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Abstract
With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers’ online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by “smartly” administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers’ profits.

Speaker bio
Kevin Bauer is a post-doctoral researcher at the Leibniz Institute for Financial Research SAFE and a designated assistant professor for e-business and e-government at the University of Mannheim (starting 01.01.2023). His research focuses on the interaction between humans and artificial intelligence and the employment of machine learning systems to tackle economic problems. Current projects leverage lab and field experiments to study how explainable and causal machine learning affect human behavior and economic outcomes.

Kevin received his PhD in economics from Goethe University. During his PhD he also completed a master’s degree in information systems with a focus on artificial intelligence. Kevin regularly gives lectures on artificial intelligence and machine learning for European financial supervisors.