  Customer scoring models are the core of scalable direct marketing. Uplift
models provide an estimate of the incremental benefit from a treatment that is
used for operational decision-making. Training and monitoring of uplift models
require experimental data. However, the collection of data under randomized
treatment assignment is costly, since random targeting deviates from an
established targeting policy. To increase the cost-efficiency of
experimentation and facilitate frequent data collection and model training, we
introduce supervised randomization. It is a novel approach that integrates
existing scoring models into randomized trials to target relevant customers,
while ensuring consistent estimates of treatment effects through correction for
active sample selection. An empirical Monte Carlo study shows that data
collection under supervised randomization is cost-efficient, while downstream
uplift models perform competitively.
