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

39 Pages Posted: 1 Nov 2022 Last revised: 30 Nov 2022

See all articles by Moritz von Zahn

Moritz von Zahn

Goethe University Frankfurt

Kevin Bauer

University of Mannheim; Leibniz Institute for Financial Research SAFE

Cristina Mihale-Wilson

Goethe University Frankfurt

Johanna Jagow

Independent

Maximilian Speicher

Jagow Speicher

Oliver Hinz

Goethe University Frankfurt - Faculty of Economics and Business Administration

Date Written: November 24, 2022

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 European 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.

Keywords: Product returns, Green Nudging, Causal Machine Learning, Enriched Digital Footprint

Suggested Citation

von Zahn, Moritz and Bauer, Kevin and Mihale-Wilson, Cristina and Jagow, Johanna and Speicher, Maximilian and Hinz, Oliver, The Smart Green Nudge: Reducing Product Returns through Enriched Digital Footprints & Causal Machine Learning (November 24, 2022). SAFE Working Paper No. 363, Available at SSRN: https://ssrn.com/abstract=4262656 or http://dx.doi.org/10.2139/ssrn.4262656

Moritz Von Zahn

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Kevin Bauer (Contact Author)

University of Mannheim ( email )

L15
1-6
Mannheim, 68131
Germany

HOME PAGE: http://https://www.bwl.uni-mannheim.de/bauer/

Leibniz Institute for Financial Research SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Cristina Mihale-Wilson

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Johanna Jagow

Independent

Maximilian Speicher

Jagow Speicher ( email )

Barcelona, 08037
Spain

HOME PAGE: http://www.maxspeicher.com

Oliver Hinz

Goethe University Frankfurt - Faculty of Economics and Business Administration ( email )

Mertonstrasse 17-25
Frankfurt am Main, D-60325
Germany

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