incrementality-and-experimentation

An incrementality test measures the causal effect of advertising by comparing exposed and control groups. You design it by selecting a methodology like geo lift or user-level holdout, then calculating statistical power, significance levels, and sample size to validate the true impact.

Geo lift testing measures advertising impact by holding out entire geographic markets as control groups, while user-level holdout isolates specific individuals. Both evaluate the causal effect of campaigns but differ in their granular approach to controlling exposure.

To calculate sample size and statistical power for a lift test, establish your minimum detectable effect and significance levels. These statistical calculations ensure your advertising test results are mathematically sound and meaningful beyond observed data.

Use brand lift surveys when you need direct user-level feedback on brand perception, whereas marketing mix modeling evaluates overall campaign effectiveness using historical data. Both methodologies measure advertising incrementality but rely on distinct causal effect evaluation approaches.

Yes, interpreting confidence intervals is essential to evaluate campaign effectiveness and validate advertising impact. It helps determine the statistical significance of your incrementality test results within the defined minimum detectable effect.