Treatment Targeting by Scaled Behavioral Measurement

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.