AppsFlyerMove beyond correlation: Measure the incremental impact of your acquisition campaigns
Summary
The article introduces Incrementality for UA, a solution by AppsFlyer that brings causal measurement to user acquisition campaigns. It addresses the increasing difficulty for CMOs and UA leaders to determine true growth drivers amidst fragmented landscapes, AI-driven bidding, signal loss, and organic behavior fluctuations. The core argument is that no single metric can fully explain performance; major ad networks like Google, Meta, and TikTok advocate for multi-model measurement.
Incrementality for UA combines lift experiments with automated geo-based testing and Google's Time-Based Regression method, all within the AppsFlyer dashboard, providing side-by-side views of lift and last-touch attribution. Key data points from beta testing: 18% of campaigns appearing strong in attribution actually showed no incremental lift (capturing existing demand), while 30% of experiments revealed campaigns driving up to 10x more incremental impact than attributed. Actionable takeaways: Teams can launch experiments with one click, avoid BI dependency, and make faster, evidence-based budget shifts.
The solution builds on AppsFlyer's trusted data governance and privacy safeguards, offering practical incrementality at scale. For marketing leaders, it clarifies true campaign contributions, identifies non-incremental spend, and grounds performance discussions in causation rather than correlation. This positions incrementality as a foundational tool for modern, multi-model measurement.
Analyst Note
What's notable here is how AppsFlyer is positioning incrementality not as a separate tool but as a feature native to its core attribution platform. This directly addresses a persistent pain point for UA teams: the gap between easy-to-get attribution data and the harder-to-obtain causal lift insights. By embedding geo-based lift experiments directly into the same dashboard, AppsFlyer is betting that removing friction will drive adoption of multi-model measurement—a concept that networks like Google and Meta have been advocating but many advertisers struggle to operationalize.
The beta data, showing that 18% of attribution-strong campaigns had no lift, underscores the practical risk of relying on attribution alone, especially in an era of AI-driven bidding and signal loss. The competitive angle is clear: while other MMPs offer incrementality via partnerships or separate modules, AppsFlyer is making it a core toggle, potentially shifting the basis of competition from attribution accuracy to measurement comprehensiveness. For UA and monetization teams, the key implication is a reduced need for internal data science support or third-party tools to run lift studies, enabling faster, more confident budget shifts.
However, the reliance on automated geo selection and platform-level holdouts means teams must trust the engine's design—and the methodology's validity with smaller or less cyclic markets remains to be seen.
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