“Reducing Net Product Returns through Green Nudges and Causal Machine Learning” by Moritz von Zahn (Goethe University Frankfurt, vzahn@wiwi.uni-frankfurt.de); Kevin Bauer, (Leibniz SAFE Frankfurt, bauer@safe-frankfurt.de); Cristina Mihale-Wilson (Goethe University Frankfurt, mihale-wilson@wiwi.uni-frankfurt.de); Maximilian Speicher (Jagow Speicher Consulting, max@maxspeicher.com ); Johanna Jagow (Jagow Speicher Consulting, johanna@maxspeicher.com); Oliver Hinz (Goethe University Frankfurt,ohinz@wiwi.uni-frankfurt.de).
As free customer deliveries are becoming a standard in E-commerce, product returns pose a growing challenge to online retailers and society. For retailers, product returns create considerable costs associated with transportation, labor, disposal and infrastructure to manage returns. From a societal perspective, increasing product returns contribute to increased pollution, additional trash, and often a waste of natural resources. Due to these costs, companies and society are interested in reducing product returns. However, despite strong entrepreneurial and public interest in minimizing product returns, retailers on a micro level possess only very few effective instruments to minimize product returns without harming customer demand and net sales. In this work, we propose a novel product return prevention instrument (Smart Green Nudging) that leverages Causal Machine Learning (CML) and the availability of rich customer and contextual data sources to identify and nudge selected customers towards better shopping choices that will yield reduced product returns without diminishing customer demand and net sales. We evaluate the performance of the proposed returns prevention instrument with real-world data from the German online shop of a large European retailer. We demonstrate in a randomized field experiment with ~1 million visitors that showing a green nudge to all customers (Naïve Green Nudging) can reduce product returns but also incurs a decrease in demand, which however ultimately translates to higher net profits. Further, we demonstrate the superiority of Smart Green Nudging over Naïve Green Nudging in terms of both product returns and firm profits. Specifically, in our randomized field experiment, when compared to Naïve Green Nudging, the Smart Green Nudging strategy curtails product return by additional +1.23% and increases profits by additional +3.61%. Overall, this paper demonstrates the efficacy of using state-of-the-art CML to customize minimally invasive behavioral nudges in the digital environment as a means to tackle business and societal problems.