Treatment Targeting by Scaled Behavioral Measurement

Kevin Bauer, Andreas Grunewald, Florian Hett, Johanna Jagow and Maximilian Speicher

No 12772, CESifo Working Paper Series from CESifo

Abstract: We study how behavioral economics and machine learning can jointly construct effective treatment-targeting rules. In a large field experiment at an online fashion retailer with approximately 500,000 consumers, we test a loss-framed discount message. We elicit individual loss aversion in a nested incentivized behavioral measurement experiment (N=582) and use machine learning to impute it from digital footprints. Targeting based on scaled behavioral measurement yields statistically significant revenue gains and outperforms causal forests. The results show how scaling behavioral measurement can improve algorithmic treatment assignment relative to purely data-driven approaches, especially when pilot data are unavailable, noisy, or costly.

Keywords: treatment targeting; behavioral measurement; machine learning (search for similar items in EconPapers)
JEL-codes: C55 C93 D91 L81 M31 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ifo.de/DocDL/cesifo1_wp12772.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:ces:ceswps:_12772

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().