Designing Incrementality Experiments During the Holidays
Learn how to design incrementality experiments that hold up during high-variance, high-stakes periods like Black Friday and the holidays—accounting for seasonality, shifting variance, and confounding events to ensure your results are reliable and actionable.
What to Do When Your Marketing Experiment Isn't Statistically Significant
Marketers often dismiss “inconclusive” test results, missing valuable insight about what’s likely, what’s ruled out, and where to invest next. This article explores how to interpret noisy experiments, use ROI ranges instead of single-point estimates, and make smarter decisions under uncertainty.
How to Use Lift Tests to Calibrate an MMM
Marketers often struggle with how to use incrementality experiments to calibrate their media mix models. This article explains how to combine experimental and modeled data effectively—answering common questions and sharing best practices for making MMMs more accurate and reliable.
How to Prioritize Experiments When You Can't Run Them All
Most marketers want to test everything, but limited time and budget mean you need to focus on tests that unlock the biggest decisions. This article shows how to prioritize experiments using MMM uncertainty, expected value, and real-world constraints to generate maximum strategic impact.
The Hidden Pitfall in Marketing Experiments: Understanding Uncertainty
Marketers often misinterpret A/B test results by focusing on the mean lift, expecting it to fully materialize in the business. This article explains why considering uncertainty intervals is critical for making realistic forecasts and reliable decisions.
Considerations When Designing GeoLift Tests
Experimental results don't always scale—what works in a test market might not work nationally, and what worked in March might not work in August. This article explains how sample size and timing affect whether your incrementality test results will generalize to broader campaigns and future periods.