You know the challenge. If you want to understand — and validate — ad spend, you need to prove your ads are driving growth. When you take the ad data from the platform, like your Facebook Ads results, you’re trusting their numbers. But how do you know what part of those conversions would have happened anyway?
This is where advertising incrementality enters the scene.
Incrementality measurement makes the worth of your efforts easier to pinpoint. It allows you to isolate the true lift driven by your campaigns so you can optimize advertising spend, justify marketing budgets and make data-backed decisions that drive real results.
But how do you measure incrementality?
What is advertising incrementality?
Measuring incrementality in advertising is about figuring out how much impact a campaign or channel has had on your overall conversions. It helps you see if a specific channel or campaign drove more sales or actions than if you had done nothing.
A ‘control versus test’ approach is the best way to determine the incremental effects of a marketing action on a result.
Using incrementality in marketing, you can isolate conversions directly driven by a specific campaign, separating them from organic sales. This approach helps identify the actual impact of your marketing efforts, beyond what would happen naturally.
What do we mean by organic sales?
These sales happen because your brand has gained a trusted position in the market over time. Loyal buyers already know how to find you whether or not you run ads.
You may also have other customers who find you through organic search after you have invested years in SEO.
These are sales that would happen even if you did not buy ads. They happen organically, not because of advertising.
Incrementality in action
You’re running a Black Friday campaign across multiple marketing channels. With incrementality measurement, you compare a group exposed to your ads to one that isn’t, allowing you to measure the incremental sales increase driven by your marketing campaign. Without incrementality measurement, it would be difficult to determine which sales would have happened anyway as part of the usual seasonal shopping trends.
With this control and test approach, you also can understand marketing impact at a more granular level. For instance, you can figure out if your display ads or social media posts are the primary drivers of an uplift in your YOY holiday sales.
The goal is clear: determine which campaigns and channels contribute the most to your sales and which part of the sales might have occurred anyway, even if you hadn’t shown any ads.
Understanding of incrementality approaches
Marketers use marketing mix modeling and incrementality testing to measure incrementality. These approaches provide different parts of the story, but both are important. Combining them offers valuable insights into incremental lift.
Marketing mix modeling (MMM)
Marketing mix modeling (MMM) helps uncover your baseline sales while pinpointing how much of your growth comes from your media and marketing campaigns. It’s a powerful way to identify what’s driving results so you can fine-tune your strategy.
However, MMM works best alongside tools like multi-touch attribution (MTA) and incrementality testing. Together, these measurement methods provide a clearer picture of both immediate and sustained impact, helping you make smarter, more confident decisions.
Incrementality testing
Incrementality testing helps you identify what’s driving results by comparing two groups: one that sees your marketing campaign (exposed) and one that doesn’t (control). It’s a simple but useful way to isolate the impact of your marketing.
This approach works especially well for channel-specific evaluations, like measuring the lift from paid social or email campaigns. It’s all about knowing what moves the needle so you can better manage your marketing spend and drive real growth.
Using MMM, MTA and incrementality testing together in measurement triangulation provides a more holistic overview of ad effectiveness, enabling you to make better data-informed decisions about budget allocation.
Key elements of effective incrementality testing
Triangulation (including incrementality testing) requires your data sources to be as accurate as possible.
Other fundamental elements include:
Relying on first-party transaction data for accuracy
Pull data directly from your transaction history to assess campaign impact, avoiding media platform-specific metrics (like conversions from Facebook or Instagram) that may not capture the full customer journey.
For example, an online fashion retailer segments customers into two groups. One receives SMS campaigns (exposed group), and the other doesn’t (control group). Using the sales data from their customer relationship management system (CRM), not data from the SMS tool, they can track conversions, average order value and purchase frequency over time.
By comparing these metrics between the test and control groups, they can see the incremental lift from SMS, isolating its impact from other channels like email and organic search.
This reveals whether SMS truly drives additional sales, allowing the retailer to make better-informed decisions about future SMS investments. This level of insight helps you manage budgets with greater precision, making justifying your ad spend that much easier.
Looking beyond platform lift studies for cross-channel insights
Platform-specific studies might only reflect single-channel campaign performance and overlook cross-channel interactions. But you can also use incrementality measurement to capture the interplay between channels.
Let’s say a fitness brand uses incrementality measurement to assess the combined impact of Instagram and YouTube ads on online course sales. First, they split the audience into three groups:
- One sees only YouTube ads
- Another, only Instagram ads
- And a third sees both in a set sequence (YouTube first, then Instagram)
Splitting groups for individual testing makes it easier to see incremental lift across channels.
By comparing conversion rates across these groups, the fitness brand finds that sequential exposure (YouTube followed by Instagram) boosts conversions by 40% over single-channel exposure.
This insight allows them to focus their budget on coordinated, cross-channel campaigns rather than isolated ones that don’t have as much bang for their buck.
A step-by-step guide to measuring incrementality
Leverage incrementality testing for quick, actionable insights into specific campaigns or tactics. However, start by pairing it with your baseline to create a more comprehensive and robust overview.
1. Model your baseline sales
Before we can start testing, we need a baseline to compare against. This will make creating a holistic view of your results much simpler in later steps.
Gather historical data.
- First, you must collect sales and ad spend data across all marketing channels for a set period.
- Include other potential influencers like seasonality, economic factors and competitor marketing activity to create a full model.
2. Set up incrementality testing
Define your control and treatment groups.
- Choose your platform (e.g., Facebook, Google Ads, in-store promotions).
- Identify a representative audience segment for testing.
- Choose campaigns with immediate calls to action (e.g., promotions, product launches).
Set up your campaign environment.
- Create campaigns within your ad platform that allow you to isolate the test and control groups for online environments.
- Consider geographic split testing (different regions) or timing variations (specific days for ads) for offline or hybrid environments.
- For geo-lift incrementality testing, for example, find two geographic regions in your audience that are similar.
- Then, pause all ads in one of these regions.
- After a few weeks, compare the regions to see how much less sales, if any, the region without ads is driving compared to those with ads.
- Avoid any other changes in marketing or promotions for the test duration to keep the comparison clean.
3. Run the incrementality test
Launch the campaign.
- Run your campaign as planned, showing the ad or marketing intervention only to the test group.
- Keep the control group excluded from any exposure to the test ad.
- You can use comprehensive tracking tools like a central data hub, ad platform pixels and customer relationship management (CRM) software, to monitor responses from each group in real time.
Monitor data collection.
- Track conversions for both test and control groups.
- You can use conversions like purchases, sign-ups, downloads or any specific action you want to measure.
- Confirm that each action is correctly attributed to the relevant group in your tracking setup.
- Track MTA so your interpretation can be more comprehensive and accurate.
- Gather data for a long enough period to capture reliable results (e.g., 2–4 weeks for short campaigns or several months for longer campaigns).
Analyze results to determine incrementality
While you might have a tool that will manage this all for you in a central Data Hub, it pays to understand the numbers you’re going to be working with and how to calculate incrementality on the fly.
Compare conversion rates.
First, calculate the conversion rate for each group:
Let’s assume the following:
- Test group size: 2,000
- Test group conversions: 300
- Control group size: 2,000
- Control group conversions: 250
Step 1: Calculate the conversion rates
The test conversion rate is 300 / 2000 = 0.15 (15%)
The control conversion rate: 250 / 2000 = 0.125 (12.5%)
The test group conversion rate (15%) is higher than the control group conversion rate (12.5%).
Step 2: Calculate the absolute difference
First, express your conversion percentages as decimals. Then, calculate the absolute difference.
Absolute difference = 0.15 - 0.125 = 0.025 (2.5 percentage points)
Step 3: Calculate relative uplift and incremental lift
When your exec team asks you how much your tests improved sales, you can express it as relative uplift:
Relative uplift (%) = (0.025 / 0.125) × 100 = 20%
Or incremental uplift:
Subtract the control group’s conversion rate from the test group’s conversion rate:
Incremental lift = Test group conversion rate - Control group conversion rate
Incremental Lift: 300−250 = 50 conversions
Step 4: Interpret the results
The absolute improvement is 2.5 percentage points.
The relative uplift is 20%, meaning the test group's ad increased conversions by 20% compared to the control group.
The incremental lift is 50 conversions.
From these simple calculations, you can determine not only if your test was successful but also by how much.
5. Triangulate your data for a holistic overview of your results
All mature businesses have baseline sales, meaning they will generate revenue even without running any marketing campaigns. This concept can be difficult for some marketers to accept, but it is an undeniable reality.
When it comes to marketing measurement, there is no definitive ground level of sales. The true incremental impact of a marketing channel would be the actual revenue minus the revenue in an alternative universe where everything is identical, except the brand didn’t run marketing on that channel (or didn’t run the campaign)
Since alternative universes don’t exist (or at least we have to go with that assumption until proven otherwise), we have to rely on statistical modeling.
While modeling provides valuable insights and can increase our confidence in the results, it doesn't offer absolute certainty. This is why triangulation (using multiple data sources and methods to validate findings) is so important. It brings you that much closer to exactitude.
Step 1: Triangulate findings
- Use data from both the incrementality test and MMM as a cross-check.
- If the lift is similar across both, your results are likely accurate.
- If there are discrepancies, analyze variables and conditions in each test to determine any reasons for variations.
Step 2: Combine data for holistic insights
- Blend results from MMM (long-term, multi-channel view) and MTA (attributions) with incrementality test insights (specific campaign impact).
6. Make decisions based on results
Now, you can produce your data narratives based on your triangulated data and make data-informed decisions about future campaigns.
Consider expanding or maintaining the campaign if the test shows a strong incremental lift. For minimal impact, experiment with creative, targeting or timing adjustments.
Then, implement your findings in future campaigns and monitor them closely. You can use your insights from successful tests to inform similar future efforts.
Refine your approach based on results to continuously optimize your marketing strategy.
This structured approach allows you to systematically test, validate and optimize your campaigns, leading to more effective marketing strategy decisions.
Data that paints the whole picture
Stop wasting your budget on ads that aren’t moving the needle and start making data-informed decisions that show the true value of your marketing. Use incrementality measurement as part of your triangulation process to cut through the noise of overinflated metrics and see what’s really driving your results.