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

Moritz von Zahn, Kevin Bauer, Cristina Mihale-Wilson, Johanna Jagow, Max Speicher and Oliver Hinz

No 363, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

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: Electronic commerce; Nudging; Causal forest; Artificial intelligence; Digital footprint; Consumer returns (search for similar items in EconPapers)
Date: 2022, Revised 2022
New Economics Papers: this item is included in nep-big, nep-cbe, nep-cmp, nep-ene, nep-env and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/268751/1/1837095035.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:363

Access Statistics for this paper

More papers in SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().