Journal article
Latent Stratification for Incrementality Experiments
19 Nov 2019
Abstract
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.
Applying the procedure to 5 catalog experiments shows a reduction of 30-60% in
the variance of the overall ATE. Analytical results and simulations show that
the method decreases the variance of the ATE most when (1) there are large
differences in the treatment effect between latent strata and (2) the model is
well-identified.
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Details
- Title
- Latent Stratification for Incrementality Experiments
- Creators
- Ron BermanElea McDonnell Feit
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Marketing
- Identifiers
- 991019189163404721