The Problem With Traditional Attribution
For years, marketers have relied on attribution models like last-click or linear to measure campaign success. These models map the customer journey across touchpoints and assign credit to ads based on when and where the interaction happened.
But attribution has a blind spot; it doesn’t tell you whether the ad actually caused the conversion.
Imagine this: A loyal customer sees a retargeting ad for your brand and makes a purchase. Was it the ad that drove the sale, or would that customer have purchased anyway? Traditional attribution models would give credit to the ad, but in reality, it may not have had any impact at all.
Without controlling “what would have happened otherwise,” marketers risk overestimating performance, inflating ROI, and wasting budget on audiences who don’t need persuading.
What Is Incrementality?
Incrementality measures the added value an ad delivers — the difference between conversions that happened because of the ad versus conversions that would have happened anyway.
It’s the science of causation, not correlation. Incrementality asks the counterfactual: “Would this customer have converted without seeing the ad?”
By answering this question, incrementality isolates true ad impact, helping marketers understand what spend is driving growth versus what’s just along for the ride.
Why Incrementality Matters
Incrementality has become a critical metric because it:
- Allocates budget more effectively. Spend goes toward channels and audiences that deliver measurable lift.
- Eliminates wasted spend. You stop targeting “sure things” who would have converted regardless.
- Provides clearer ROI. CFOs and leadership want proof of business lift, not just clicks or impressions.
- Elevates marketing’s credibility. Incrementality shows the business impact of campaigns in terms everyone understands: revenue growth.
How to Measure Incrementality
There’s no single method, but marketers can choose from proven approaches:
Controlled Experiments
- Holdout Tests: Suppress ads to a control group and measure differences in conversion.
- Geo Experiments: Run ads in one region while leaving another untouched.
- PSA Tests: Show neutral public service ads to the control group to balance exposure.
Uplift Modeling
Use predictive analytics to separate audiences into:
- Persuadables: Likely influenced by ads.
- Sure Things: Would convert anyway.
- Lost Causes: Unlikely to convert no matter what.
- Do Not Disturbs: Ads may actually lower their likelihood to buy.
This helps identify where ad dollars drive the most incremental value.
Media Mix Modeling (MMM)
Analyzes aggregate data across multiple channels and time periods to estimate incremental lift.
Best suited for brands with broad media investments and longer purchase cycles.
Incrementality vs Attribution: What’s the Difference?
Attribution is valuable for understanding touchpoints, but it cannot prove causality. Incrementality fills that gap.
What Incrementality Testing Looks Like in Action
Marketers often ask: “How do I know the customer wouldn’t have purchased anyway?” That’s where incrementality testing comes in. By running holdout groups, geo splits, or uplift modeling, you can directly compare exposed audiences against control groups and isolate the lift caused by your ads.
The difference between the two groups is your incremental impact. That’s the proof that your marketing efforts aren’t just reaching people — they’re influencing real outcomes.
Prove, Don’t Just Track
Attribution models will always have their place. They’re useful for mapping journeys and analyzing exposure. But attribution alone is not enough.
To truly prove marketing’s value, you also need to measure incrementality. That’s how you move beyond vanity metrics and show real influence on sales, appointments, and revenue.
In today’s budget-conscious environment, incrementality isn’t just a nice-to-have. It’s the key to smarter spend, stronger credibility, and sustainable growth.
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