Move beyond correlation: Measure the incremental impact of your acquisition campaigns

TL;DR

Know what’s truly working: Measure the real causal impact of your user acquisition campaigns across all major networks.

Remove the guesswork: Understand which conversions would have happened anyway and which were driven by your ads.

Scale with confidence: Run lift experiments powered by automated geo-based testing, and trusted causal modeling.

Unify attribution + incrementality: View lift and last-touch attribution metrics side-by-side inside AppsFlyer.

Build for the multi-model future: Align with the measurement frameworks recommended by Google, Meta, and TikTok, and leading marketing science teams worldwide.

For CMOs and user acquisition leaders, knowing which ad dollars drive true growth has become much harder.

Today’s marketing landscape is increasingly fragmented and complex, shaped by AI-driven bidding systems, constant creative iteration, rising signal loss, and unpredictable organic behavior.

These pressures create familiar challenges:

• Performance fluctuates for reasons unrelated to campaigns
• External forces mask true contribution
• Decisions are made with partial visibility during a time of increasing efficiency demands

This is why the major networks have aligned around the same idea:

• Google promotes a modern measurement toolbox.
• Meta describes a suite of truth.
• TikTok encourages triangulation.

While the language differs, the conclusion is consistent: no single metric or model can fully explain performance on its own.

Major ad networks are calling for multiple measurement approaches layered alongside real-time attribution. When teams rely on more than one lens, they gain a clearer and more reliable view of performance. 

That’s why we developed Incrementality for UA. Incrementality for UA brings this multi-model approach directly into AppsFlyer by adding causal lift insights that strengthen and complement the attribution data teams rely on every day.

The challenge: You cannot optimize what you cannot isolate

Campaigns do not operate in controlled conditions. They run in an environment shaped by seasonality and holidays, algorithmic volatility, app store visibility, competitor activity, CRM and lifecycle pushes, surges in organic demand, and broader macro market shifts. On top of that, overlapping targeting across campaigns and channels makes it increasingly difficult to understand which efforts are truly driving incremental growth.

Incrementality reveals whether marketing activity caused incremental change beyond what would have happened anyway.

Without that causal view, teams risk overvaluing campaigns that capture existing demand, undervaluing campaigns whose impact is hidden by noise, shifting budgets based on correlation rather than causation, and debating performance across competing truth sources with no clear way to resolve those disagreements.

Incrementality has always been valuable, it just hasn’t been accessible. Until now.

The solution: Causal measurement, built for real customer acquisition

Incrementality for UA gives growth teams a practical way to understand true campaign impact, directly inside the measurement environment they already trust. Building on years of experience supporting the world’s leading brands with marketing measurement and attribution, including incrementality measurement for remarketing, it brings causal testing to user acquisition at scale.

Smart setup, seamless execution

With a single click, teams can launch experiments that previously required weeks of planning, analysis, and coordination.

That simplicity is powered by a fully automated causal measurement engine designed specifically for how marketing teams operate today. The system intelligently selects test and control geographies based on real behavioral data, enforces platform-level holdouts across major ad networks, and applies statistically rigorous causal methodologies trusted by leading marketing science teams. Lift and attribution metrics are visualized side by side, making results easy to interpret and act on.

Causal modeling uses Google’s Time-Based Regression method, the gold standard in incrementality testing.

No code. No custom workflows. No BI dependency.

Just causal clarity at the speed marketing teams operate.

Built on the Single Source of Truth you already trust

Incrementality for UA runs on the same data, governance, and privacy safeguards that power AppsFlyer attribution for more than 14,000 brands.

The methodology was shaped and validated in collaboration with leading advertisers and major ad networks, meeting the standards expected by top acquisition and Marketing Science teams. 

Trust, transparency, and repeatability are not add-ons — they are core to the solution.

Insights where decisions actually happen

Incremental results appear directly inside the AppsFlyer dashboard alongside key attribution metrics. This enables faster, more confident optimizations and immediate budget shifts based on causal evidence. 

User acquisition teams gain direct access to insights without relying on separate workflows or teams, reducing conflicting truth sources and enabling continuous learning in real time — all without context switching.

With that, attribution and incrementality finally work as complementary, unified lenses.

Learnings from our beta

Early testing with dozens of advertisers showed how powerful accessible causal measurement alongside attribution can be:

• 18% of campaigns that looked strong in attribution showed no incremental lift, indicating they were capturing existing demand.
• 30% of experiments revealed campaigns driving up to 10 times more incremental impact than originally attributed.

(Closed beta insights; individual results vary.)

Seeing both lenses, attribution and incrementality, gave teams a far clearer read on their true growth drivers. It helps them power smarter, more effective budgeting decisions.

Why this matters for marketing leaders

For marketing leaders, incrementality provides clearer answers to critical questions:

• What is the true contribution of each campaign?
• Where is budget being spent without incremental impact?
• How can performance discussions be grounded in causal evidence rather than assumptions?

Incrementality is no longer a theory. It is a practical way to allocate budgets with greater confidence.

A foundation for what comes next

Incrementality for UA represents the next evolution of modern measurement:
continuous, automated, causal insights at scale.

It helps teams combine rigor with speed, align with platform best practices, and confidently embrace the multi-model future the industry is steering toward.

With a clearer understanding of what’s truly driving growth, teams don’t just measure better, they grow better.