Why Incrementality Testing Matters for Modern Marketing Measurement

Essentially, incrementality testing is a randomized, controlled test that divides consumers into two groups. The first is the treatment group that sees your marketing campaign, and the second is the control group that doesn't.

What you'll aim to do with incrementality is understand if the money you're spending on advertising is actually being well spent and is positively impacting consumer behavior and outcomes. In our opinion, the only measurement that can define the impact of advertising is incrementality testing.

How does it work? The comparison of consumer behavior between the two groups isolates the impact of the campaign, deciphering if the ad translated into conversions or if the same conversions would have happened organically.

You're already getting the picture of why it matters so much for modern marketing measurement, so read on to learn why so many marketing managers are turning to incrementality to measure true causal lift through automated experiments that complement attribution.

Measuring the true causal impact

The issue is that it isn't always easy to decipher the difference between organic and paid conversions. You don't know if the traffic that converts to customers would have converted anyway. Well, you don't know, unless you're using tech such Appsflyer incrementality testing.

With incrementality, you get to measure the true causal impact of ads by identifying the additional life in conversions or revenue that a campaign directly generates. You separate organic and baseline sales, and unlike attribution, which will only show correlation, incrementality establishes causation. That said, incrementality is definitely an excellent supplement for attribution marketing.

And remember, the core of incrementality is the test vs. control methodology. By segmenting the audience exposed to the marketing campaign from the control group, sometimes called the holdout, which is a statistically matched segment and purposely not exposed to the campaign, it's so easy to compare outcomes from the two groups, with the difference defined as incremental lift.

You eliminate the "would have happened anyway" notion, and from there, you can also gain a deeper understanding of your ROAS by understanding how profitable the campaign was, also known as incrementality ROAS, or iROAS.

Waste reduction and efficient budget allocation

If the point of incrementality is understanding whether you would have generated the conversions anyway, naturally, incrementality matters for waste reduction and efficient budget allocation.

You're not just getting the typical vanity metrics some marketers obsess over, which, in a lot of cases, don't actually correlate to the true impact. It gets to cut through surface-level metrics like high impressions and clicks to see if you're wasting your money or if your budget allocation is on point.

What you can then do is understand if you're over-crediting channels and potentially overspending on the high-visibility channels, such as branded search or retargeting, when you don't necessarily need to. 

And with Proxima research estimating that up to 60% of marketing budgets are wasted due to blind spending, we'd say understanding waste reduction and budget allocation is essential.

A privacy-first focus

You're definitely making more privacy-conscious marketing decisions. As the testing is controlled and experimental, you're not interacting with individual-level tracking, user IDs, etc. You're taking aggregate data and turning it into actionable insights by measuring the true impact of advertising.

You're not violating any consumer privacy regulations, such as the GDPR or CCPA, and you can still scale experimentation without compromising accuracy. 

And now that privacy regulations are becoming so tight, incrementality has never worked so well. For example, Apple's ATT framework, part of iOS14, banned the ability to measure using device matching. The SKAdNetwork now captures around 68% of installs driven by non-organic activity, and incrementality fills the gap.

The benefits of incrementality testing

The benefits are everything we've been mentioning so far, but to sum it up a bit better, here's why we think everyone should be doing regular incrementality to analyze the effectiveness of their campaigns, including the ROAS and iROAS:

• Accurate Attribution

• Budget Optimization

• Proves Marketing Value

• Strategic Insights

• Real-Time Optimization

• Privacy-Compliant

• Causal Understanding

And some platforms do it better. In our opinion, AppsFlyer is one of the most sophisticated attribution marketing platforms with a focus on incrementality. With the Appsflyer incrementality measurement feature, you can test user acquisitions and remarketing campaigns simultaneously for a consistent view of incremental lift across every channel.

But what's better is the dashboard that you get to see all the data on. It works side-by-side with their attribution feature, so you can easily compare last-touch and casual lift without having to move between platforms.

AppsFlyer's incrementality feature

AppsFlyer's incrementality feature is the market leader. Having helped thousands of big-name brands understand which campaigns drive incremental growth. With the AppsFlyer sophisticated incrementality feature, you can expect an average of:

• A 114% increase in CR

• 50% increase in ROAS

• 60% reduction in CPI

• 66% reduction in CPA

They've specifically designed this feature for teams measuring real impact, creating a safe space for them to run automated experiments, measure causal lift, and invest confidently. Focusing on last-touch attribution as Appsflyer does, it's so easy to automate incrementality to get the entire picture of performance.

What we also love is that testing launch takes minutes—you don't have to manage complicated BI bottlenecks, as the platform automates test design and delivers fast results by launching experiments across all major networks almost instantly.

And using the Appsflyer smart algorithms, it creates exposed vs. holdout groups from your first-party data. From that, you can see statistically significant results with built-in validation.

Incrementality testing is essential for modern marketing. Not to be confused with A/B testing, it's an excellent tool that helps you measure the true and sometimes hidden return on investment (ROI) that you don't always see with advertising spend.