What’s Incrementality in Marketing?

Picture this: You’re running paid ads for a flash sale, and the results look fantastic. Conversions are up, revenue spikes, and your dashboards paint a picture of success. But then—someone on your team asks a dangerous question:

How many of those buyers would have purchased anyway?

It’s the marketing equivalent of asking, If a tree falls in the forest and no one is there to hear it, does it make a sound? In this case, if a customer would have converted without seeing your ad, did that ad actually drive any value?

This is the heart of incrementality—a way to measure whether your marketing is truly moving the needle or just taking credit for what would’ve happened regardless. Specifically, showing an ad doesn’t always mean causing a purchase.

So, how do you measure true lift? 

That’s where incrementality challenges traditional attribution, and why marketers who ignore it might be wasting millions chasing vanity metrics.

TL;DR

• Most marketers track ROAS, but without incrementality, they’re just guessing at what actually drives growth.
• Not every conversion is a win—incrementality separates real impact from marketing fluff.
• If you’re not testing incrementality, you’re probably paying for customers who would’ve bought anyway.

Let’s Define Incrementality

Incrementality is the measurement of additional outcomes directly caused by a marketing effort that wouldn’t have happened otherwise. It’s about figuring out how much lift in conversions, sales, or sign-ups can be tied to a specific campaign or channel. Put another way, if you turned off a campaign, would you see a drop in conversions? If the answer is yes, that drop is a good indicator of your marketing incrementality.

Why Should You Care About Incrementality?

ROI on Marketing Spend Within Attribution

Traditionally, return on ad spend (ROAS) has long been the default metric for marketers—quick to calculate and easy to explain. But incrementality tells a more complex story. 

As Cindy Meltzer, VP of research and analytics at Dagger, points out, measuring true lift takes more than just looking at the numbers—it involves hands-on testing, controlled experiments, and a deeper dive into whether your ads are actually influencing behavior or just capturing credit for conversions that would’ve happened anyway.

Incrementality helps you allocate resources to marketing campaigns that truly produce net-new gains, rather than simply claiming credit for actions that would have happened anyway. It serves as a sanity—versus vanity—check for your other marketing metrics and turns abstract statistics into actionable insights. 

5 Key Elements of Incrementality That You Need to Know About

1. True Lift: This is the core principle—identifying the actual lift in conversions attributable to your marketing. True lift focuses on real behavior changes rather than surface-level metrics like impressions or clicks.

2. Control vs. Exposed Groups: Most incrementality tests rely on comparing a control group (unexposed to the campaign) with an exposed group (those who see the campaign). The difference in outcomes between these groups reveals genuine campaign impact. It’s critical for accurate insights that only a single variable changes – and this is also what makes incrementality such a challenge when companies are operating complex multi-channel marketing strategies. 

3. Time-Specific Analysis: Incrementality is usually measured within a set timeframe to capture immediate and short-term effects. This avoids conflating your results with external factors like seasonality or unrelated promotions.

4. Scientific Rigor: At its best, incrementality measurement resembles a scientific experiment—randomly splitting your audience, precisely assigning treatments, and checking for differences. This approach helps ensure accurate, data-backed insights.

5. Multi-Channel Considerations: Most modern marketing involves many channels. Incrementality methods can span Facebook, Google, email, and beyond to see which channels truly add value.

How Is Incrementality Different From Attribution?

Unattributed journey to conversion within Attribution

For many marketers, the line between incrementality and attribution can get blurry. They are both common methods of measuring marketing performance (alongside marketing mix modeling), but their approaches are different. 

Attribution tries to assign credit for a conversion across various touchpoints (e.g., first touch, last touch, or multi-touch attribution). Incrementality, on the other hand, is about determining how many of those conversions wouldn’t have happened without your marketing activities in the first place. While attribution splits the pie of overall credit, incrementality asks, “Would there even be a pie without these tactics?”

Here’s another way to see it: Attribution models are often designed to track the sequence of interaction—user clicks on a Facebook ad, visits your website, then signs up. Incrementality, however, focuses on the underlying causal effect. 

This distinction is crucial whenever you need to justify marketing budget or optimize ROI. Because a well-attributed campaign may look successful if it claims plenty of conversions, but if those conversions are not truly incremental, you might be wasting spend.

	Attribution	Incrementality
Core Question	“Which touchpoints get credit for a conversion?”	“How many extra conversions are caused by a specific marketing effort?”
Approach	Assign conversion credit to interactions (e.g., first, last, multi-touch)	Measure the causal lift through experimental or quasi-experimental methods
Outcome	Helps distribute credit along the customer journey	Shows if and how much a campaign truly moved the needle
Primary Focus	Path analysis	Causation

Differences between attribution and incrementality 

How Can You Measure and Test Incrementality?

The foundation of incrementality measurement lies in controlled experimentation. This could be an A/B test—where one group sees your ad (or experiences a channel) while another group doesn’t. By comparing the outcomes, you can isolate the causal effect of that marketing activity. 

Here’s a step-by-step guide to get started:

1. Your Hypothesis and KPIs

Before testing, know what you’re measuring and your best guess at what might be causing the outcome. Are you tracking conversions, revenue, app installs, or engagement rates? The clearer your KPIs, the easier it is to determine whether your marketing is making an impact.

2. Establish a Control Group

Randomly divide your audience so that one part doesn’t see the ads at all. The control group is essential for figuring out what might have happened naturally, without your intervention.

As Cognitive Co-founder, Aaron Andalmen notes in Adweek, “The gold standard method for measuring incremental CPA is to conduct a randomized controlled trial (RCT), a rigorous approach to measuring the number of conversions a particular campaign actually drove.” 

This is where the real test begins. Divide your audience into two groups:

• Test Group → Exposed to your campaign
• Control Group → Not exposed to your campaign

Again, the key is randomization—you need both groups to be as similar as possible so that any differences in performance can be attributed to your marketing, not outside factors. 

3. Run The Campaigns and Measure Outcomes

Watch for changes in conversion rate, revenue, or sign-ups between the exposed group and the control group. The difference in these metrics gives you the incrementality.

Let your campaign run and track how each group performs. For example:

• Test Group Click-Through Rate (CTR): 8%
• Control Group CTR: 3%

This gap between the two groups is the incremental lift—the additional impact driven by your marketing efforts.

4. Calculate Incrementality

Once you have the numbers, the next step is to quantify the lift.

Incrementality Formula:

For example, if your CTR jumps from 3% (control) to 8% (test), your incrementality lift is 167%—meaning your campaign significantly increased engagement.

Other ways to measure impact:

• Net Incremental Revenue (NIR): If test revenue is $20,000 and control revenue is $10,000, then your incremental revenue is $10,000.
  
• Incremental ROAS: Instead of looking at overall return on ad spend (ROAS), incremental ROAS tells you how much additional revenue each marketing dollar truly generated.

That said, not all incrementality looks the same

Here are a few other ways you can measure and test incrementality:

1. A/B Testing – The gold standard. Split your audience randomly, run the campaign, and compare outcomes.
2. Geo-Experiments – Instead of audience splits, use different geographic regions as test and control groups. Great for large-scale campaigns.
3. Automated Platform Tools – Meta (Facebook) and Google offer built-in conversion lift studies to measure incrementality directly.
4. Live Experiments vs. Statistical Models – Live experiments provide real-world results, while model-based approaches (like regression analysis) estimate impact using historical data.

The Key To Driving Business Impact With Incrementality

To drive real business impact with incrementality, marketers need to stop relying on surface-level metrics and start testing everything. 

But beyond testing, effective incrementality comes down to budget reallocation. Once you know what’s working, shift spend toward the highest-impact campaigns. That means cutting out redundant or cannibalistic spend (like bidding on branded search when organic conversions would happen anyway) and doubling down on strategies that generate new demand. 

Because incrementality isn’t just a marketing measurement tactic—it’s a framework for smarter marketing decisions. Ultimately, the power of incrementality lies in its ability to separate the signal from the noise. Every marketer should have access to clear, actionable insights that show the true impact of their efforts—especially when it comes to multi-touch measurement. 

Combine Incrementality with Attribution Modeling for a Complete Understanding of Marketing ROI

Incrementality gives you a detailed but limited view into the impact of certain efforts, but what it doesn’t give you is a holistic view of how all of your efforts are performing as a whole. That’s where attribution modeling comes in. A solid attribution model (linear or position-based in our preference) will give you deep insight into which of your marketing campaigns are contributing to revenue and which aren’t. Then, once you have a baseline understanding of how your current campaigns are performing, you can use incrementality to test out the precise impact of new campaigns and iterate based on what you learn to turbo-charge growth. 

Our platform, Attribution, comes with out-of-the-box attribution modeling that’s fully auditable and transparent: you can track custom journeys across every touch, rather than the black box attribution you get from most tools on the market. The Attribution platform also enables you to create custom models that apportion conversion credit according to the specific needs of your sales funnel and buyer journey.

Sign up and try Attribution today — pinpoint CAC by channel, audit funnels and conversion rates, scale revenue-driven content marketing, measure affiliate LTV and CAC (and more).