TY  - UNPD
A1  - Zahn, Moritz von
A1  - Bauer, Kevin
A1  - Mihale-Wilson, Cristina
A1  - Jagow, Johanna
A1  - Speicher, Max
A1  - Hinz, Oliver
T1  - The smart green nudge: reducing product returns through enriched digital footprints & causal machine learning
T2  - SAFE working paper ; No. 363
N2  - 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.
T3  - SAFE working paper - 363 
KW  - Product returns
KW  - Green Nudging
KW  - Causal Machine Learning
KW  - Enriched Digital Footprint
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69025
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-690257
UR  - https://ssrn.com/abstract=4262656
IS  - October 28, 2022
PB  - SAFE
CY  - Frankfurt am Main
ER  -