  Incrementality experiments compare customers exposed to a marketing action
designed to increase sales to those randomly assigned to a control group. These
experiments suffer from noisy responses which make precise estimation of the
average treatment effect (ATE) and marketing ROI difficult. We develop a model
that improves the precision by estimating separate treatment effects for three
latent strata defined by potential outcomes in the experiment -- customers who
would buy regardless of ad exposure, those who would buy only if exposed to ads
and those who would not buy regardless. The overall ATE is estimated by
averaging the strata-level effects, and this produces a more precise estimator
of the ATE over a wide range of conditions typical of marketing experiments.
Analytical results and simulations show that the method decreases the sampling
variance of the ATE most when (1) there are large differences in the treatment
effect between latent strata and (2) the model used to estimate the
strata-level effects is well-identified. Applying the procedure to 5 catalog
experiments shows a reduction of 30-60% in the variance of the overall ATE.
This leads to a substantial decrease in decision errors when the estimator is
used to determine whether ads should be continued or discontinued.
