What Is SKAdNetwork (SKAN)?
Apple’s privacy-preserving ad measurement framework — the way to measure campaign performance on iOS without user identifiers.
Book a free callSKAdNetwork (SKAN for short) is Apple’s privacy-preserving mobile ad measurement framework. Without sharing user identifiers, it reports which campaigns drove installs and post-install value in an aggregated, anonymous way. For iOS users who don’t grant ATT permission, it’s your only source of measurement.
How does SKAN work?
- A user sees or taps an iOS ad; the ad network sends a signed signal to Apple.
- The user installs and opens the app.
- Based on user behavior, the app records a conversion value.
- After a measurement window and a random delay, Apple sends an anonymous postback to the ad network.
- The MMP collects these postbacks and produces campaign-level reporting.
The key point: postbacks are campaign-level, not user-level, and they’re delayed. That’s why real-time, user-level optimization isn’t possible on iOS.
Conversion value schema
This is the most critical — and most neglected — part of SKAN. You only have a limited number of bits, and what you encode in that narrow space determines whether you run your iOS campaigns blind or with real visibility.
There are three common schema approaches:
- Revenue-based: The conversion value encodes the revenue range a user generated within their first day. Best suited to ROAS-focused campaigns.
- Engagement-based: Encodes completed onboarding steps, levels or the number of key actions. Provides an early signal for products where revenue comes later.
- Hybrid: Some bits are allocated to revenue and some to behavior. This is the most common and usually the most balanced approach.
Changing the schema breaks comparability with historical data. That’s why the schema should be designed carefully before campaigns launch and not changed often.
What’s new in SKAN 4.0
| Feature | What changed |
|---|---|
| Multiple postbacks | Postbacks are sent across three separate time windows instead of just one |
| Coarse conversion value | A low / medium / high signal for low-volume campaigns |
| Hierarchical source identifier | Campaign granularity unlocks in tiers depending on the privacy threshold |
| Web-to-app support | Journeys that start on the web can be measured |
The most important practical gain is multiple postbacks: you now get signals not just from the first day but from later windows too. This has noticeably improved iOS measurement for subscription products and apps that monetize later.
What to watch out for when working with SKAN
- Build the delay into your decision-making. Because postbacks arrive with a random delay, data for the last 24-48 hours is incomplete; don’t shut down campaigns based on that window.
- Stay above the privacy threshold. In structures split across too many campaigns and ad groups, volume falls below the threshold and no data comes through at all. Simplify your campaign structure on iOS.
- Design the schema around your product. Off-the-shelf template schemas often don’t match your revenue curve.
- Don’t compare it one-to-one with Android. The two platforms measure differently; putting iOS and Android ROAS side by side in the same table is misleading.
Frequently asked questions
Is using SKAN mandatory?
For iOS users who don’t grant ATT permission, effectively yes — there’s no other measurement source. Classic deterministic attribution still works for users who opt in, but because opt-in rates remain limited in most categories, SKAN is the main source of signal.
Why does SKAN data look different from my MMP dashboard?
SKAN sends aggregated, delayed data, and breakdowns below the privacy threshold aren’t reported at all. The MMP fills these gaps with modeling. So a difference between the two sources is expected; what matters is using a single source consistently.
What campaign structure should you use on iOS?
As simple as possible. Because of the privacy threshold, over-segmented structures don’t return data. A small number of campaigns with enough volume and a well-designed conversion value schema will perform far better than a granular structure that produces no data.
Let’s stop flying blind on iOS
We design your SKAN conversion value schema around your product and make your iOS campaigns manageable with data.
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