Incrementality Testing: A Marketer’s Guide to Measuring Ad Lift

Key Insights

• Incrementality is a way to measure ad effectiveness by measuring the lift in desired outcome by comparing two audience groups, one that has been exposed to an ad and another that hasn’t.
• Incrementality is one of the best ways that marketers can understand the real-world impact that their ad campaigns are having on their business.
• Attribution tells marketers where credit goes, and incrementality helps show whether the advertising actually changed the outcome.
• By relying on controlled testing instead of third-party tracking alone, incrementality offers a more durable approach to measurement in a changing privacy landscape.
• Viant’s industry leading reporting capabilities help marketers run multiple types of incrementality tests, providing data that can be analyzed to make budgetary and other business decisions

In order to understand the full reach of an ad and how a particular campaign has impacted your brand, incrementality can be incredibly useful.

Incrementality takes a look at positive, negative and neutral impacts of an ad, essentially measuring ad effectiveness. Incrementality measures the lift in desired outcome by comparing two audience groups, one that has been exposed to an ad and another that hasn’t. This helps show the percentage of conversions an ad campaign has created.

With the big emphasis on measurement and proving ROI in marketing today, maximizing one’s budget is more important than ever. Incrementality is one of the best ways that marketers can understand the real-world impact that their ad campaigns are having on their business.

Here’s everything you need to know about incrementality and incrementality testing:

What is Incrementality?

Incrementality is a performance metric that measures the true, causal impact of your advertising on a specific business outcome.

Rather than simply taking credit for every conversion that happens after an ad is served—a common pitfall of traditional attribution models—incrementality reveals the actual lift driven by your marketing efforts.

The core idea is to answer a fundamental question: how many conversions happened because of the campaign, versus how many would have happened anyway?

Suppose you track 100 conversions from users who saw your recent ad campaign. But by observing a comparable unexposed group (a control group), you find that they would have produced 70 conversions on their own during the same period.

This means only about 30 of those conversions were truly incremental, driven directly by the ad. In general terms, incrementality lift is often calculated by comparing these groups using a basic formula: (Test Result – Control Result) / Control Result.

In our scenario, your incremental lift percentage would be (100 – 70) / 70, or roughly 42.8%. Understanding this distinction is vital for effective media allocation.

A campaign might look incredibly strong on paper with a high Return on Ad Spend (ROAS), but if most of those buyers were going to convert regardless of the ad exposure, the campaign is adding little real lift to your bottom line.

Incrementality testing prevents you from wasting budget on organic conversions. Ultimately, as the digital landscape evolves, incrementality provides reliable, future-proof cross-channel and cross-device measurement, ensuring accurate insights even in an increasingly complex and changing privacy environment.

Why Is Incrementality Important For Marketers?

For brands, it’s essential to understand whether or not a campaign strategy is working. By utilizing incrementality, marketers can go beyond traditional metrics that sometimes don’t go deep enough to fully gauge the effectiveness of an ad.

Rather than assuming the only reason shoppers have purchased furniture on sale is because they saw a commercial, incrementality looks at the differences in interactions between people who saw a commercial and people who did not. Incrementality identifies which specific interactions led shoppers to attend the sale, in turn making a purchase.

Instead of assigning all credit to the commercial, incrementality provides a baseline for conversions that occurred. Understanding this baseline can help marketers make smart decisions for their businesses and modify strategies as needed to optimize ROI.

What Questions Can Incrementality Help You Answer?

One of the biggest questions incrementality can answer is whether or not an ad is actually helping to generate revenue, or if revenue comes from unrelated interactions that would have happened naturally on their own.

By answering this question, marketers can then determine an answer as to whether they should increase or decrease the amount of budget they are putting into a specific ad campaign. They can also answer the question of what could happen if a business decides to stop spending money with specific vendors, tools and platforms.

Additionally, incrementality can answer which elements of the ad itself are impacting the desired outcome. For example, is it the talent in the ad? The creative? The placement? Understanding all of these factors and how they intersect can help generate success.

What Is Incrementality Testing?

Incrementality testing evaluates ad spend and measures the impact of your ad. Think of it as an experiment that withholds ad exposure to one group while continuing to run ad exposure for the second group. This data-driven test offers a comparison of conversion rates between the two, essentially gauging how much an ad campaign actually works.

By applying incrementality testing, marketers can calculate how much to increase or decrease ad spend to match what they’re finding in their results. It helps identify a baseline that can then be analyzed to make budgetary and other business decisions.

How Is Incrementality Different From Attribution, MMM, and MTA?

Attribution assigns credit to the various touchpoints a user interacts with before making a purchase. Traditional models like last-click give all the credit to the final interaction, ignoring the rest of the buyer journey.

Multi-touch attribution (MTA) improves upon this by distributing credit across several touchpoints. However, a major flaw remains: neither method tells you if those conversions would have happened anyway without the ad exposure.

Media Mix Modeling (MMM) takes a macro approach. It uses historical, aggregated data to estimate channel contribution over time. While highly useful for high-level budget planning, MMM only estimates impact rather than directly measuring it through an active test.

Incrementality testing actively measures true causality. By comparing an exposed audience against a holdout control group, this method isolates the exact lift the advertising actually caused, removing organic conversions from the equation.

Reinforcing this difference is crucial. A campaign can easily earn high attribution credit simply by targeting users who are already planning to buy. This happens frequently, meaning a campaign looks successful without producing real incremental lift.

Ultimately, these tools work best when used together as they complement each other perfectly. By combining the strengths of MTA and MMM with regular incrementality analysis, marketers can build a robust, holistic measurement strategy that drives true growth.

How Do You Calculate Incrementality?

Calculating incrementality requires comparing the actions of your test group—users exposed to your ad—against a control group that was held back from seeing it.

By looking at the conversion rates of both groups, you can isolate the exact impact of your campaign and remove organic conversions from the equation.

To find the incremental lift percentage, you determine the difference in conversion rates between the two groups and divide it by the control group’s rate.

The correct formula to calculate this is:

Incremental lift = (Test conversion rate − Control conversion rate) ÷ Control conversion rate × 100.

Imagine your campaign data shows a 5% conversion rate for the test group that saw your ad. Meanwhile, the unexposed control group had a 3% conversion rate.

Using our formula, we subtract the control rate from the test rate (5% – 3% = 2%). Then, we divide that 2% difference by the 3% control rate, and multiply by 100.

The math looks like this: (5% − 3%) ÷ 3% × 100 = about 67% incremental lift.

This result means your advertising campaign generated roughly a 67% incremental lift in conversions above what would have happened organically.

Rather than taking credit for all sales, this calculation accurately proves the additional value your media spend actually drove for the business.

What Are The Main Types of Incrementality Tests?

Choosing the right incrementality test depends heavily on your specific campaign goals, the data you have available, and how much control you have over exactly who sees your ads.

User-based testing operates at the individual level. A common method is an audience holdout, where a specific portion of your target audience is intentionally excluded from seeing the ad campaign entirely.

Another user-based method uses ghost ads, which track users who would have seen your ad but were outbid in the auction. Alternatively, PSA tests serve public service announcements to the control group instead of your actual ad.

Geo-based testing, on the other hand, operates at a regional level. This involves running your advertising campaign in select geographic markets while holding back comparable, statistically similar markets as your control group.

Geo-based tests are highly useful when it is technically difficult to create clean user-level holdouts, or when you are heavily reliant on offline sales, market-level metrics, and broader first-party data.

Ultimately, regardless of whether you choose a user-level or geographic methodology, the integrity of the unexposed control group is exactly what makes any incrementality test scientifically rigorous and trustworthy.

How Do You Optimize To Increase Incrementality?

While you should always be sure to account for variables, there are six key areas that you can manipulate within your campaign to affect incrementality. Think of the below as the category that’s being compared:

Device Delivery:

Evaluate incremental lift for consumer engagement based on which device a video ad drives more actions versus those not exposed to the video ad. For example, viewing the ad on a CTV resulted in more consumer website visits versus the same video ad on desktop, and viewing the ad on either device resulted in more visits vs the control (unexposed to ads).

Audience:

Test different targeting groups to see which audiences are more responsive to a specific message. For example, a “parents with school-aged children” target audience made more online purchases than a “heavy viewers of Disney Channel” target audience after being exposed to your ad versus non-exposed control groups.

Creative:

Compare how an image or written message drives incremental actions across audiences exposed to the image or message versus audiences not exposed. For example, if an image of female friends laughing at brunch performs better than an image of a couple watching TV together.

Inventory:

Test your video ad across different publisher categories to see best performers. For example, does your display test ad drive more consumer activity when served on sports or entertainment content categories compared to the control? 

Format:

Explore the type of ad format that is best for specific campaigns.  For example, when looking to drive in-store foot traffic, does video or display get better results for your mobile campaign when evaluating test and control results.

Channel:

Optimize for your best performing channels by testing incrementality of your ad versus control. For example, did the ad outperform the control group on desktop versus DOOH? 

How Do You Know Your Results Are Reliable?

Before reallocating your marketing budget based on an incrementality test, it is crucial to ensure your data is trustworthy. Evaluating a few key factors guarantees your findings are accurate before making major financial decisions.

First, consider your sample size. Both your exposed and control groups must be large enough to provide a meaningful comparison. Without adequate volume, a few outlier conversions could easily skew your entire dataset and invalidate the test.

Next, evaluate the test duration. Your marketing experiment needs to run long enough to adequately capture your customers’ normal purchase or decision cycle, ensuring you gather complete behavioral data rather than just an initial, short-term spike.

You must also rigorously verify statistical confidence. This crucial metric proves whether the observed lift in conversions is real versus just random chance. A confidence level of 90 percent or higher is a widely accepted, reliable industry benchmark.

Additionally, you must maintain a strictly comparable control group. To accurately isolate the ad’s true impact, your exposed and control audiences should be virtually identical in every measurable way except for the actual ad exposure itself.

Ultimately, a reliable incrementality test provides more than just a positive number on a report. The true goal is to uncover a meaningful, scientifically backed lift that confidently informs your next strategic steps and future media investments.

What Are Incrementality Best Practices?

1. Understand your control group’s behavior and how the group of consumers exposed to your ad performs relative to it. The true campaign success metric is how a group of consumers exposed to your ad performs compared to your non-exposed control group.
2. Make sure you have a clearly defined hypothesis – for example, a format that will lead to better conversions or a target group that will increase your reach.
3. Use your test results to scale up KPIs – for example, channels, reach, return on ad spend (ROAS), etc.
4. As you receive results, check first to see if they support your hypothesis. If they don’t, adjust your hypothesis based on your learnings and run the test again.
5. Make sure you’re not rewarding undesired consumer behavior, for instance, don’t run so many coupons that your audience will no longer make purchases without them.
6. Ensure you’re accurately measuring incrementality across platforms and devices, as well as getting a holistic view of your audience, by working with a partner that offers an identity resolution solution.

Regardless of which path you choose, incrementality is a smart way to understand how well an ad campaign works and what steps a business should take to drive growth.

Ready To Measure The True Lift of Your Marketing?

Viant’s DSP gives marketers the advanced reporting and built-in identity resolution needed to measure campaign impact across channels and devices. With a clearer view of incremental lift, marketers can better understand what their media is actually driving and use those insights to make smarter budget decisions.

See the true impact of your advertising. Request a demo or contact us to learn how Viant can help you measure incrementality.