The smart green nudge: reducing product returns through enriched digital footprints & causal machine learning

  • 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.

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Metadaten
Author:Moritz von ZahnGND, Kevin BauerORCiDGND, Cristina Mihale-WilsonGND, Johanna Jagow, Max Speicher, Oliver HinzORCiDGND
URN:urn:nbn:de:hebis:30:3-690257
URL:https://ssrn.com/abstract=4262656
DOI:https://doi.org/10.2139/ssrn.4262656
Parent Title (English):SAFE working paper ; No. 363
Series (Serial Number):SAFE working paper series (363)
Publisher:SAFE
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2022
Year of first Publication:2022
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/11/08
Tag:Causal Machine Learning; Enriched Digital Footprint; Green Nudging; Product returns
Issue:October 28, 2022
Page Number:39
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht