SKAdNetwork attribution
SKAdNetwork (SKAN) is Apple's framework for attributing app installs and post-install conversions to ad campaigns without identifying the user or device. Instead of a deterministic identifier, it sends the ad network an aggregated, delayed 'postback' confirming a conversion happened, with deliberately limited campaign granularity and a conversion value of restricted resolution. It is the privacy-preserving backbone of iOS install attribution after ATT.
What this means
SKAdNetwork attributes an app install (and limited post-install activity) to the campaign that drove it without exposing user- or device-level identifiers. When a user installs and opens an app after seeing or clicking an ad, the device sends a cryptographically signed postback to the ad network confirming the conversion.
Crucially, the postback is aggregated and delayed by privacy timers, and it carries only a limited campaign identifier and a low-resolution 'conversion value' that the app sets to encode a small amount of post-install behavior. There is no user identifier in the loop.
What the constraints mean for reporting
The deliberate limits — coarse campaign granularity, a small conversion-value space, randomized postback timing, and crowd-anonymity thresholds that can suppress low-volume conversions — mean SKAN reporting is fundamentally aggregate and lossy compared to the deterministic IDFA era.
For practitioners this means accepting delayed, less granular install attribution and designing the conversion-value schema carefully, since it is the only post-install signal available. SKAN cannot answer user-level questions by design, and newer versions adjusted the granularity and timing rules, so the platform documentation is the authoritative reference for any given version.
- Aggregated, signed postbacks instead of user identifiers
- Delayed by privacy timers; coarse campaign granularity
- Low-resolution conversion value encodes limited post-install behavior
How it appears in analytics and logs
Install attribution that arrives delayed, aggregated, and without per-user paths is SKAdNetwork; its coarse conversion values and timer-based postbacks are deliberate privacy constraints, not data loss.
Diagnostic use case
Understand SKAdNetwork when interpreting iOS install attribution that lacks user-level data, has delayed postbacks, and exposes only coarse campaign and conversion-value granularity.
What WebmasterID can help detect
WebmasterID's first-party web measurement is independent of SKAdNetwork's app-install framework; understanding SKAN helps you interpret mobile-app reports alongside your own web data.
Common mistakes
- Expecting user-level paths from a framework built to hide them.
- Under-designing the limited conversion-value schema.
- Ignoring that anonymity thresholds can suppress low-volume conversions.
Privacy and accuracy notes
SKAdNetwork is engineered so the ad network learns that a conversion occurred without learning who converted — privacy is the design goal, not a side effect.
Related pages
- iOS ATT and attribution
App Tracking Transparency (ATT) is Apple's framework requiring an app to request user permission before tracking it across apps and websites owned by other companies, or accessing the device's advertising identifier (IDFA). When permission is denied, the IDFA is unavailable, which removed the deterministic identifier mobile attribution long relied on and pushed the ecosystem toward aggregated, privacy-preserving measurement.
- Modeled conversions
Modeled conversions are conversions a platform estimates statistically rather than observes directly. When direct measurement is blocked — by missing consent, cross-device journeys, or privacy protections — ad and analytics platforms model the likely conversions from observable trends and aggregated data, and report them alongside observed ones. Understanding which conversions are modeled is essential to reading attribution honestly.
- View-through conversions: credit for impressions
A view-through conversion credits an impression a user was served but did not click, when they later convert within an impression window. It tries to value awareness that does not get clicked, but it is among the easiest credits to over-count, because seeing is not the same as being influenced.
- Privacy-first analytics
First-party web measurement alongside app-install signals.
Sources and verification notes
- Apple Developer — SKAdNetworkOfficial documentation of the privacy-preserving install-attribution framework.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.