iOS Signal Loss Estimator
This estimator calculates the proportion of your iOS user base that has opted out of tracking under Apple's App Tracking Transparency framework and models the resulting measurement gap in your Meta and other platform reporting. It is designed for performance marketers and data analysts who need to quantify how much of their conversion volume is being under-reported and what modeled or server-side event data might be filling the gap. The tool applies observed industry opt-in rates and shows the measurement impact under multiple opt-out rate assumptions.
Inputs
Results
How this is calculated
Enter your iOS app or mobile web traffic share, your overall conversion volume, and your platform attribution window, and the estimator applies category-specific iOS opt-in rate benchmarks to project the fraction of conversions now invisible to platform measurement.
Every model runs locally in your browser. Nothing you type is sent anywhere.
Frequently asked questions
What is iOS ATT and why does it affect ad measurement?
iOS App Tracking Transparency, introduced in iOS 14.5, requires apps to ask users for permission before tracking their activity across other apps and websites. When users decline — and the majority do — the advertising identifier used to connect ad exposure to downstream conversion is unavailable. This breaks the attribution chain for click-based conversion tracking and reduces the visibility of ad platform measurement tools like the Meta Pixel and the Google Ads tag.
What percentage of iOS users opt in to tracking?
Opt-in rates vary by app category, region, and how the permission prompt is designed. Across all categories and regions, typical opt-in rates have been observed in the 30 to 45% range in the years following ATT's introduction. Gaming apps tend to see lower opt-in rates. Apps that clearly explain the value exchange before showing the system prompt — a practice called pre-prompt — tend to see higher opt-in rates.
How can I recover measurement accuracy after iOS signal loss?
The main approaches are implementing the Conversions API or server-side event tracking to send conversion data directly to platforms without relying on browser-based cookies or device identifiers; adopting privacy-preserving measurement approaches like aggregated event measurement; using modeled attribution provided by platforms; and investing in incrementality testing and media mix modeling to validate true campaign performance independently of platform-reported attribution.