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Treatment Targeting by Scaled Behavioral Measurement

Author

Listed:
  • Kevin Bauer
  • Andreas Grunewald
  • Florian Hett
  • Johanna Jagow
  • Maximilian Speicher

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.

Suggested Citation

  • Kevin Bauer & Andreas Grunewald & Florian Hett & Johanna Jagow & Maximilian Speicher, 2026. "Treatment Targeting by Scaled Behavioral Measurement," CESifo Working Paper Series 12772, CESifo.
    • Handle: RePEc:ces:ceswps:_12772
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    Keywords

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    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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