Incrementality in Marketing: What It Is & How to Calculate It

Which aspect of your last campaign should get the credit for turning an interested prospect into a paying customer? Incrementality is all about measuring the relative contributions of each ad set, media channel, and marketing tactic, so you can understand the true value of every piece of your plan.

Whether you’re trying to dial in ad spend or get a better grip on the efficacy of a top-of-funnel campaign, using incremental measurement can help you boost conversion rates and make the most of your marketing campaigns.

What Is Incrementality?

Incrementality in marketing measures the impact of not only your entire marketing campaign but also individual components of the campaign that help drive traffic. This could manifest as the measurement of an ad’s effectiveness or a landing page’s conversion rate.

With incrementality testing and assessments, you can better attribute and understand the incremental contributions made by each paid tactic or distribution channel. This can be particularly helpful when evaluating campaigns that are otherwise difficult to assess, such as streaming TV.

Why Is Incrementality Important for Marketers?

Marketers typically develop campaigns and measure efficacy using key data points. When those data points are generated in-house, there tends to be more control. But when you’re relying on data provided by a third party, like a social media platform, there’s a significant loss in visibility. Instead of understanding the entirety of a consumer’s journey, reports often rely on “last-touch attribution.”

Last-touch attribution basically gives credit for an action (such as a sale, click-through, or inquiry) to whatever consumer-facing asset was used last. For instance, one could easily assume that someone signing up for a cleaning service did so because they clicked on a banner ad that led them to the company’s website. 

But that assumption ignores the influence of all the other marketing collateral that brought the customer to the site, such as:

Incrementality helps identify which of these touchpoints are actually making solid contributions, and even how much they’re contributing. Once you have that data, you can make educated guesses about the individual impact of each marketing touchpoint. In other words, incrementality is how marketers test things like vendor accountability, the viability of your media buying strategy, and which ads encourage your audience’s continued engagement.

Methods to Measure the Incrementality of Media

Experts all have their own opinions on which methods are best when measuring incrementality, but they all involve a combination of testing and experimentation. It’s kind of like classic A/B testing with a twist. You’re measuring an experimental option against a control group to see how the outcomes differ.

There are two approaches that seem to pop up most often.

Marketing Mix Modeling (MMM)

Marketing mix modeling helps quantify the impact of individual inputs, gauging how each one contributes to a final sale. MMM is a very inclusive approach, as it takes into account not only years’ worth of historical data, but also outside influences like competitor activity and the economy.

“Media Mix Modeling is a top-down approach that evaluates how historical media activity, promotions, pricing, seasonality, and uncontrollable factors such as economic activity impact sales. Additionally, it provides a measured marketing ROI which accounts for external factors such as weather, unemployment, and others.”

— Annica Nesty, Group Director Integrated Intelligence ( Marketing Science & Research) at Tinuiti

MMM is considered “high touch,” meaning it requires lots of hands-on input with little to no automation, and it can take a minimum of 6 months to get the modeling up and running. It’s also a more top-down view of multichannel marketing, delivering that bigger picture.

Because MMM is so labor-intensive and macro, it’s more often used for long-term planning and major portfolio moves.

Multi-Touch Attribution (MTA)

Also known as incrementality testing, multi-touch attribution (MTA) is the “always on” approach that relies on lots of automation. By leveraging existing systems, MTA can pull tons of data on a regular basis, resulting in usable reports in as little as 4-6 weeks.

Whereas last-touch attribution gives all the credit for a sale to whatever touchpoint came immediately before the customer converted, MTA breaks up the credit, assigning a fraction of the total to every touchpoint that impacted the buyer’s journey. Basically, it focuses on those individual user interactions to paint a picture of what’s going into the decision-making process.

The agility, granular assessments and rapid reporting offered by MTA make it a good choice for situations that demand daily data generation. For example, you might use it to rapidly determine how to reallocate your budget as you’re ramping up an intense, short-term campaign.

According to Nesty,

“Multi-touch or multi-channel attribution modeling can be used to improve ROI by showing which channels or campaigns are most effective at driving conversions, allowing marketers to strategically manage their marketing spend across channels/campaigns with deep channel performance insights.”



How to Calculate Incremental Impact

Imagine you’re in charge of a marketing campaign surrounding the launch of a new streaming project. You feel in your gut that your streaming ads are responsible for most of the sign-ups to date, but your boss is convinced that those sign-ups are driven by social media ads.

Understanding how to calculate incremental impact can help you prove the efficacy of your streaming ads and show your boss that your efforts are indeed generating awesome results.

1. Define Your Goal

If marketing is an experiment, then this step is where you offer your hypothesis. Think about what you’re looking to identify, then pinpoint the key performance indicators (KPIs) you’ll use to figure out whether you’re making progress toward your goal.

For example, if your goal is to hit 100,000 streaming service sign-ups within a month of launch, you may want to monitor KPIs that quantify:

Or, your goals might be something much smaller, such as measuring the impact of email retargeting in terms of getting old customers to sign up for service again.

2. Segment a Test Group and Control Group

Comparison testing requires two audiences that have similar characteristics (e.g., similar age group, income, geographic area, pain points, etc.). The control group is typically the group left to find out about your brand or product organically or via an existing campaign, while the test group is served ads or whatever other marketing collateral you’re in the process of testing.

3. Launch a Test Campaign

This is the fun part. It’s time to structure and execute a test campaign. You’ll want to run the experiment for at least a week, but ultimately, the time frame depends on factors like the complexity of the campaign, how long it takes to get traction using the channels you’ve chosen, and how quickly you need to analyze and act on results to achieve your larger goals.

4. Analyze the Outcome

The data that comes in from your experiment is just raw info until you sit down and analyze it. The information won’t mean much until you examine data from the test and control groups side by side. When you’re able to highlight the differences between your test and control group, you’ll be able to glean actionable insights about your campaign’s impact.

Ideally, you want to see an incremental lift in the test group, otherwise known as an increase in desirable outcomes versus the control group. That’s the proof of concept you’ll need to fine-tune your campaign as you get ready to push to a wider audience.

To determine your incremental lift, you’ll need to do a little math. Here’s a formula that marketers can use for simple incrementality tests:

(Test Conversion Rate – Control Conversion Rate) / (Control Conversion Rate) = Incremental Impact

Let’s explain how you could use this formula for a typical marketing activity. Say you ran an experiment involving a new email campaign where the control audience got the same old email copy and the test audience got a new version with fresh copy and visual content as support. Two weeks in, the control group has a 1.4% conversion rate, while the test group’s conversion rate is at 2.5%.

(2.5% – 1.4%) / 1.4% = 78.6%

According to that calculation, the new email copy and visual add-ons resulted in a positive incremental impact of 78.6%. That means that customers were 78.6% more likely to convert after receiving the new and improved email copy. Not too shabby.

Keep in mind, though, that major gaps aren’t necessarily a sign that you’ve knocked your experiment out of the park. Sometimes those surprisingly large gaps are red flags, signaling a configuration error. If we stay with the email marketing example, you may find that your email deliverability rates were significantly different between your test and control group — which could certainly impact conversion rates. When in doubt, double-check the settings and run the experiment again to verify.

5. Apply Insights to a Larger Campaign

Finally, take everything you learned and use that info to get your test campaign as close to perfect as possible. Dial in your messaging, decide whether you want to reallocate funding and concentrate on some channels more than others, and scrutinize your chosen audience. Once you’re satisfied, it’s time to turn your experiment into a real campaign.


Incrementality is a hugely useful tool that can help marketers identify what’s working and what isn’t, creating stronger, more focused campaigns that get the job done without sacrificing the bottom line.

For more information on performance marketing initiatives that drive engagement and brand success, check out Tinuiti’s TV & Audio services.

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