Is there a way to measure the real impact my campaigns have on the revenue?
If you’ve ever asked yourself this question, you probably stumble upon incrementality.
Incrementality represents the increase in sales caused by your ads. It's essential for measuring the true effectiveness of your advertising efforts, providing a clear understanding of how your campaigns, messaging, or specific channels influence revenue. Incrementality testing can boost marketing ROI by 30% says Forrester's report.
In this article, we'll guide you through the process of incrementality testing on Meta. Let’s dive in.
What is incrementality testing and what results it shows?
Incrementality represents the increase in sales caused by your advertising, and incrementality testing is the way to measure these results.
It’s important to understand the crucial difference between what incremental results are, and what they aren’t.
Picture it like this: you’re running a campaign on Facebook, and you measure the campaign’s success through last-touch attribution, meaning that Facebook counts all the conversions that would have happened without being exposed to your ads. These results aren’t incremental.
To put it quite simply:
When your ads help someone to buy a product earlier that’s not incrementality, because that purchase would’ve happened anyway.
The difference between attribution and incrementality measurement
Attribution shows which touchpoints lead to conversions. Since this process is not always that simple, multi-touch attribution has been developed to distribute credit among multiple touchpoints.
On the other hand, incrementality measurement begins by asking whether the conversion would have occurred without that channel. If the answer is yes, it may indicate that you’re wasting your ad spend.
Incrementality testing vs A/B testing: is there a difference?
A/B tests and incrementality tests use similar methodologies but have different objectives.
A/B testing is more versatile, used for measuring and optimizing the performance of a specific campaign, while incrementality testing is used for measuring the effectiveness of the campaign itself and the impact that the campaign has on the desired outcome.
Why should you consider incrementality measurement on Meta?
Incrementality in marketing or advertising can be pivotal in making important business decisions. It provides a clear view of how effectively you are spending your ad budget and shows the impact your ads have on customer behavior.
Incrementality testing reveals the true and often hidden potential of your ads. When making decisions about budget allocation or optimizing your ad spend, incremental results can offer a more precise picture of what's working and what's not.
Relying on the last-click attribution model in such scenarios could be a leap of faith, as it does not precisely measure the impact of your advertising but considers all factors leading to conversion, including organic traffic, Google ads, and other traffic sources.
Meta introduced Incrementality Optimization Sales Campaigns
The recent update from Meta is one more reason to consider incremental testing. They introduced the model that focuses on driving true incremental sales using data from holdout tests conducted over the past decade. Initial tests show a 24% reduction in Cost Per Acquisition. This approach offers a competitive edge by providing transparency and accurate optimization, improving ROI.
How does incremental measurement on Meta work?
For incrementality testing, a scientific approach is essential. This involves dividing the audience into two distinct groups, with advertisements shown only to one. On Meta, this stands for Lift measurements.
How does it operate? Initially, Meta randomly splits your target audience into two groups:
- The test group, which is exposed to your ads.
- The control group, which does not see the ads.
The key focus is measuring performance in each group. The difference in sales between these groups is referred to as incremental lift.
The incremental lift can manifest as positive, neutral, or negative:
- Positive incremental lift indicates superior results in the test group compared to the control group, signifying that the campaign under test is generating revenue and achieving the intended outcome.
- Neutral incremental lift, which is quite self-explanatory, represents identical results for both groups. Such outcomes suggest the need for further testing, possibly involving changes to CTAs, templates, or the overall campaign setup.
- Negative incremental lift, where the control group outperforms the test group, is a rare occurrence. However, it may signal a negative reception of your brand among potential customers. This can happen due to ad fatigue for example.
How to do incrementality testing on Meta?
You can test incrementality on Meta in a few ways, and all of them work in the same way: splitting the audience into 2 different groups.
The most recommended one is the Conversion Lift Study.
Conversion Lift Study
Conversion Lift Study works as explained above: Meta randomly splits your target audience into two groups, where the test group sees your ads, and the control group doesn’t. Most tests take 3 to 4 weeks to complete.
While you’ll get pretty accurate results from Meta’s metrics, note that the following will be taken into account:
- Every data outside Meta metrics
- Brand effect
- Lifetime value
- Overall network effect
Conversion Lift Study is pretty much self-service, meaning that you can do most of the work by yourself on Meta. But could you?
Such testing takes time and budget, meaning that all boxes need to be checked and that you have to make sure you did everything right. Because no one wants to be responsible for gambling the team’s budget on the testing that didn’t turn out as expected. Finding the right partner can make this process easier.
The real-life example of Conversion Lift Study with Hunch
Meta's marketing science team conducted a 4-week Conversion Lift Study to measure the lift in ROAS for one of our clients. Using Hunch AI-powered tools, they removed backgrounds from 350,000 product images to create enriched DPAs.
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The results of testing showed that Hunch outperformed both competitor overlay and no-overlay templates across all frontiers, driving an incremental ROAS lift of 2.3x. Therefore, partnering with Hunch is estimated to save you 64% in annual costs.
We hope you now have the knowledge to begin experimenting with incremental testing, and we wish you the best of luck. If you have any questions or need our help, reach out!
Frequently asked questions
What is incrementality testing?
Incrementality testing measures the sales your advertising actually caused, not the ones that would have happened anyway. It answers a simple question: would this conversion have occurred without your ad? That is why it matters so much. Forrester found incrementality testing can boost marketing ROI by 30%, giving you a far clearer read than last-touch attribution.
What is the difference between attribution and incrementality?
Attribution shows which touchpoints led to a conversion and splits credit across them. Incrementality starts from a tougher question: would that conversion have happened without this channel at all? If the answer is yes, that is a sign you may be wasting ad spend on sales you were already going to get.
How does incrementality testing work on Meta?
Meta randomly splits your audience into two groups: a test group that sees your ads and a control group that does not. The difference in sales between them is your incremental lift. On Meta this runs as a Lift measurement, it needs a scientific setup, and most tests take 3 to 4 weeks to complete.
What is incremental lift, and what do the results mean?
Incremental lift is the sales gap between your test group and control group. Positive lift means the test group won, so your campaign is genuinely driving revenue. Neutral lift means both groups matched, a sign to test changes to CTAs, creatives, or setup. Negative lift, where the control wins, is rare but can point to creative fatigue.
Why measure incrementality instead of trusting last-click attribution?
Last-click counts every conversion on the path to a sale, including organic traffic, Google ads, and other sources, so it can flatter your ads and hide what is really working. That is why relying on it for budget decisions is a leap of faith. Incrementality reveals the true, often hidden, impact of your spend.
Can Hunch help with incrementality testing on Meta?
Yes. A Conversion Lift Study is mostly self-service on Meta, but the real question is whether you should run it alone, because the time, budget, and setup leave no room for error. That is why a partner helps. Meta's marketing science team ran a 4-week study for one of our clients, where enriched DPAs built with Hunch drove a 2.3x incremental ROAS lift.
