What is SKAdNetwork?

SKAdNetwork (SKAN) is Apple’s privacy-preserving framework for attributing app installs on iOS. It allows advertisers to measure which campaigns lead to installs without accessing user-level data like the IDFA.

Instead of tracking individual users, SKAN works by sending a confirmation (called a postback) after an install occurs, with aggregated, anonymized information.

For example, a fitness app might run ads across multiple channels. When a user clicks an ad and installs the app, SKAN ensures the ad network receives credit, but without revealing who the user is or what device they used.

How does it work?

When someone clicks on an ad and installs your app:

• Apple registers the engagement on the device.

• Once the app is opened, Apple waits a predefined period to observe user behavior.

• After that, Apple sends a postback to the winning ad network, including:
  
  

  • A campaign ID

  • A conversion value (0–63) or coarse value (low/medium/high)

  • A timestamp (delayed and sometimes coarse)

You decide which in-app actions (e.g., starting a trial, making a purchase) map to which conversion values. In SKAN 4.0, Apple allows multiple postbacks over time, giving you a broader picture of long-term user behavior.

Let’s say your app sees the most meaningful activity in the first 2 days. You can structure your events and lock the first postback window early to get timely insights, even if full LTV data isn’t available.

Why it matters

SKAN became the default on iOS after Apple introduced App Tracking Transparency (ATT), which limits access to user identifiers unless users explicitly opt in.

This means most performance marketing on iOS must now rely on SKAN. It’s critical because:

• It gives advertisers a way to measure outcomes while respecting user privacy.
  
  

• It forces a shift from user-level tracking to event-based modeling.
  
  

• It standardizes how app performance is measured in a post-IDFA environment.

Apps that adapt well to SKAN  by designing clear conversion value mappings and aligning growth/product/data teams around early user signals can continue making informed decisions, even with less data.