MEASURE THE TRUE LIFT OF YOUR MEDIA WITH REAL-WORLD EXPERIMENTS

Measure media lift with the precision only the most trusted scientific method can deliver—randomized control trials—and calibrate your attribution model for maximum accuracy.

incrementality experiments

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INCREASE IN ADVERTISING ROI

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HIGHER ROI THAN MMM ALONE

Join over 200 marketing teams making data-driven decisions with confidence

Randomized control trials are the most powerful tool in econometrics—the branch of economics that studies cause and effect. When a market is randomly segmented into two geographic regions with similar traits and simultaneously exposed to the same marketing, prices, and promotions, you’ve created ideal conditions for an experiment. Because the only difference between the segments is that one sees your ads and the other doesn’t, you can isolate and measure the impact of that channel using the gold standard of the scientific method.

What makes incrementality experiments so powerful is their ability to uncover true causal relationships—rather than misleading correlations. For example, if you announce a promotion to your email list, some subscribers will search for your brand and click on your Google brand ads. This creates a strong correlation between brand ad spend and sales—but the sales weren’t caused by the ad spend. A third factor—a confounder—drove both: the promotion.

The same logic applies to ad delivery algorithms. When you run a promotion, people are more likely to buy—and platforms serve ads more often to those with the highest likelihood to convert (like your email subscribers). So while these ads may appear to drive results, they often add little incremental value.

Incrementality testing controls for these confounders. If the promotion goes out to all regions, but only some regions see the ads, you can isolate the effect of the ads themselves—separating their impact from the promotion’s. That’s how you get to the *true* lift.

custom audiences

How It Works

incrementality media mix modeling

STEP 1: SELECT THE EXPERIMENT TYPE

Choose between a holdout test (to see how much sales drop when you pause a channel) or a scale-up test (to see how much sales rise when you increase spend). While holdout tests measure ROAS at your current budget, scale-up tests measure ROAS at a higher spend—and calculates the incremental ROAS between the two.

STEP 2: DESIGN YOUR EXPERIMENT

We’ll automatically select the best regions and experiment duration to ensure a large enough sample size for statistically significant results. Regions are chosen to accurately represent your market while accounting for only a small share of revenue—to prevent interfering with your most profitable areas. Because each test runs in a separate geographic market, our platform supports up to three simultaneous tests.

STEP 3: ANALYZE THE RESULTS

Our platform shows how much revenue changed during the experiment—and whether that change is statistically significant. We use the synthetic control method (SCM) to estimate the causal impact of your campaign, comparing actual revenue to the counterfactual: what would have happened without the intervention, based on a weighted average of the control regions.

STEP 4: CALIBRATE YOUR MEDIA MIX MODEL

One of the biggest advantages of incrementality testing is the ability to calibrate causal inference tools like media mix models. While attribution models can work without incrementality tests, companies that combine both see a 59% greater increase in ROAS (35% vs. 22%). When you run incrementality tests with Data Speaks, your attribution models are automatically calibrated for maximum accuracy.

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