In a world flooded with data dashboards and vanity metrics, knowing your app advertising "performed well" isn't enough. Was it that catchy headline? A seasonal spike? The stars aligning on the App Store homepage? Or was it truly the effect of Apple Ads that moved the needle? That’s exactly what incrementality analysis is built to uncover.

Let's break down why measuring incrementality is the difference between guessing and growing. You’ll learn how incrementality analysis uncovers the real impact of your Apple Ads (Previously Apple Search Ads/ ASA) efforts and how app marketers can ditch the assumptions, justify budgets, and scale smarter. Whether you're optimizing creatives or launching new keywords, mastering incrementality is your new growth superpower.

What Is Incrementality Analysis?

Incrementality analysis is a statistical method used to determine the true impact of a specific marketing activity, like a paid campaign, by isolating it from organic growth, market trends, or seasonality. In simple terms, it helps you answer: “Would these results have happened without this specific initiative?”

In the context of Apple Ads, it evaluates the actual uplift caused by your paid campaigns, not just the attributed installs. This allows app marketers to optimize spend, allocate resources more effectively, and scale campaigns with confidence.

Why It Matters: Getting Past Attribution Myths

Most ad platforms provide attribution data showing which campaigns led to installs. But attribution doesn’t equal impact. Users may have installed your app regardless of seeing an ad; they might have searched for your brand or discovered the app organically. That’s why relying solely on attribution can be misleading.

Incrementality analysis solves this by comparing a baseline scenario (what would have happened without the ad) to actual performance. This is how you get to the real effect on Apple Ads,  separating signal from noise.

How Incrementality Analysis Helps Elevate iOS App Marketing 

For mobile app marketing teams focused on scalable growth, incrementality analysis is an essential tool to measure what’s truly working. In a digital environment where installs can be influenced by anything from app store features to broader market shifts, attribution alone doesn’t cut it. You need clarity, not just on what happened, but why it happened. That’s where measuring the effect of Apple Ads becomes indispensable.

Rather than relying on surface-level performance data, incrementality isolates the real impact of your campaigns. It ensures that teams don’t mistake correlation for causation and helps pinpoint the actual value driven by paid user acquisition. This leads to smarter, more strategic marketing decisions.

Here’s what incrementality analysis enables for app marketing teams:

• Proven ROI
  Demonstrates which campaigns truly drive incremental installs and value, not just attributed conversions.
  
  
• Smarter Budgeting
  Prevents overspending on campaigns that don’t generate net-new users, ensuring budgets go toward high-performing efforts.
  
  
• Deeper Insights
  Helps understand the dynamic between organic and paid growth, clarifying how much of your success comes from actual Apple Ad performance versus external factors.
  
  
• Better Testing
  Allows marketers to evaluate the real impact of changes in creatives, keyword strategy, or product pages by comparing them against projected baselines.
  
  
• Cross-functional Alignment
  Supports collaboration across ASO, creative, and UA teams by offering a shared, validated source of truth based on measurable outcomes.

By tying these insights directly to statistically validated data, incrementality analysis allows every function involved in mobile app marketing to move in sync, eliminating guesswork and enabling teams to optimize their app advertising efforts with confidence.

Methods to Measure Incrementality in Apple Ads

There are four main methods to measure the effect of Apple Ads using incrementality models:

1. Extrapolation Model (Forecast-Based Analysis)

This approach uses only pre-event data to forecast what would have happened if the campaign hadn’t run. It’s ideal for long-term events such as:

• Major keyword updates
  
  
• Store listing changes
  
  
• Market-specific campaigns

By comparing forecasted data to actuals, you can see whether your app advertising had a statistically significant impact.

2. Interpolation Model (Before-After Comparison)

This model uses data from both before and after the campaign to analyze short-term effects, like turning ads off and on to observe the change. It’s perfect for A/B testing and identifying incremental lift in:

• New Custom Product Pages
  
  
• Temporary ad bursts
  
  
• Promotional app events

3. App Store Connect Analytics

App Store Connect Analytics offers a native overview of app performance before, during, and after Apple Ads campaigns to estimate incremental impact:

• Log in to App Store Connect and access App Analytics.
  
  
• Define campaign start and end dates.
  
  
• Establish baseline metrics using data from before the campaign.
  
  
• Analyze Apple Ads traffic and compare it to the baseline.
  
  
• Evaluate first-time downloads, impressions, and product page views.
  
  
• Estimate organic lift by subtracting paid installs from total growth.
  
  
• Review user engagement and retention metrics.

This method suits long-term campaigns and major app changes, providing a high-level incrementality view using native Apple tools.

4. Holdout Group Testing (Controlled Experiments)

Holdout group testing involves dividing your audience into a test group that receives Apple Ads and a holdout group that does not receive them. By monitoring key performance metrics like installs and in-app engagement across both groups, you can accurately isolate the incremental impact of your campaigns. This controlled approach helps eliminate the influence of external factors such as seasonality or organic growth, providing a reliable measurement of your ads’ true effectiveness.

Key benefits of holdout testing include:

• Direct comparison between exposed and unexposed groups
  
  
• Accurate measurement of incremental installs and conversions
  
  
• Controls for market fluctuations and organic behavior
  
  
• Provides statistically significant results for confident decision-making

Real Use Cases of Incrementality in App Advertising

Understanding the effect of Apple Ads isn’t just about looking at performance metrics,  it’s about tying real actions to real outcomes. Incrementality analysis helps app marketers validate what’s working and eliminate guesswork in these critical scenarios:

• Testing new keywords: Did the new keyword strategy actually drive incremental installs, or just intercept organic traffic already on its way?
  
  
• Creative asset changes: Are new screenshots, icons, or videos genuinely influencing conversion rates, or are they just cosmetic upgrades?
  
  
• Custom Product Pages (CPPs): Do personalized landing pages lead to higher conversion, or do they simply segment traffic that would convert anyway?
  
  
• Seasonal promotions: Was the spike in installs due to your campaign, or was it just the holiday surge?
  
  

In mobile app marketing, these kinds of insights are game-changers. Without incrementality analysis, you’re left wondering whether your app advertising efforts are moving the needle—or just keeping up with the tide.

Why Traditional Reporting Isn’t Enough?

Attribution reports often offer a binary view: “This install came from this ad.” But in a privacy-first world shaped by SKAdNetwork and fragmented user journeys, that level of insight just doesn’t cut it anymore. For modern app marketing teams, the real question isn’t where the install came from, but whether it would have happened without the ad.

That’s exactly what incrementality analysis uncovers. By adding a statistical layer of truth to your reporting, it helps marketers confidently answer:

• Are these installs truly incremental, or just intercepted organics?
  
  
• Is my return on ad spend (ROAS) actually improving, or just shifting channels?
  
  
• Should I scale this campaign, or cut my losses?

For app advertising teams, these aren’t nice-to-have insights—they’re critical to growing smarter, not just louder.

Take the Guesswork Out of Growth

Running ads without incrementality analysis is like flying blind and hoping for a smooth landing. You might get lucky, but you won’t know why. Measuring the effect of Apple Ads brings visibility, accountability, and, most importantly, truth to your results. No more attributing growth to chance when you can prove exactly what moved the needle.

For app marketers, clarity is the real currency. Whether you're tweaking keywords, launching fresh creatives, or scaling your mobile app marketing engine, knowing what actually works turns guesswork into strategy. Smarter app advertising starts with asking the right questions—and incrementality delivers the answers.

If you're looking to scale your iOS app and drive sustainable growth with smarter Apple Ads strategies, connect with us. Leverage Newton's expertise in incrementality analysis to optimize spend, validate impact, and make data-backed decisions. Supercharge your iOS app marketing with Newton’s insights and take your growth to the next level