Skip to content
Glossary

SKAdNetwork (SKAN)

SKAdNetwork, usually shortened to SKAN, is Apple's way of telling an advertiser that an iPhone ad led to an app install, without naming the person. Apple sends a delayed, bundled message after someone installs, not a live report of that one user. The report can wait a day or more, and Apple sometimes withholds it. SKAN is a limited credit system, not a full picture of who installed.

Theodor Lindfors, Founding Marketer ·

How SKAdNetwork works

Let's take a software company as an example. They sell a job-site phone app to plumbing businesses. Plumbers pay a monthly subscription. The phone app is what plumbers use on job sites to look up customers, log visits, and send invoices. The software company advertises the app on Meta and Apple Search Ads so more plumbers will download it. SKAdNetwork (SKAN) is Apple's way of telling those ad platforms an install happened without naming the plumber.

Apple sits between the ad network and the app. Apple records that someone saw or tapped an ad, notices the install, and later sends a signed postback (a delayed confirmation message) to the network. The app can update a conversion value during a measurement window. That conversion value is a small number, 0 to 63, that the software company maps to early behavior, such as opening the app or logging a first job. The conversion value is not a dollar amount.

Let's take a software company as an example. They sell a job-site phone app to plumbing businesses. A plumber sees a Meta ad for the app on an iPhone, taps it, and installs. Apple does not tell Meta which plumber installed. Hours or days later, Apple sends Meta a postback (a delayed confirmation message) that says, in effect, an install happened that belongs to this campaign, plus a small conversion value if the software company configured one (for example, the plumber opened the app and logged a first job). If too few plumbers installed that campaign, Apple may hide even that detail so no individual can be spotted.

Later SKAdNetwork (SKAN) versions added multiple postbacks (delayed confirmation messages) over a longer period and coarse conversion values for lower-volume campaigns, but the shape of the tradeoff is unchanged: less detail, more privacy.

Why SKAN matters

After App Tracking Transparency (ATT: Apple's prompt that asks whether an app may track you across other companies' apps and sites), most iOS users do not grant tracking permission, so device-level attribution is not available for them. SKAdNetwork (SKAN) is the sanctioned way to still know which campaigns drive installs of a job-site phone app.

How to read SKAN data

Three properties change how you work. SKAdNetwork (SKAN) postbacks (delayed confirmation messages) are delayed by design, so today's spend does not have today's answer. Data is crowded and aggregated, so small campaigns can report nothing at all. And the conversion value schema you chose (what the 0 to 63 number is allowed to mean) determines what you can ever learn.

Practically that means fewer, larger campaigns and slower decisions on iOS. Judge CPI (cost per install: ad spend divided by attributed installs) on a longer horizon and stop reading a half-reported day as a trend. If a software company spent $2,000 on Tuesday advertising a job-site app and SKAdNetwork (SKAN) has only posted 40 of the 120 installs by Wednesday morning, Tuesday's cost per install is not done yet.

Common SKAN mistakes

  • Comparing a SKAdNetwork (SKAN) install count to a mobile measurement partner (MMP) or platform count and calling one of them wrong.
  • Splitting a job-site app's budget across many small campaigns until every one falls below the privacy threshold.
  • Designing a conversion value schema once (what the 0 to 63 number is allowed to mean) and never revisiting what it can answer.
  • Making same-day decisions on SKAdNetwork (SKAN) data that has not finished arriving.

Lemonado

Your AI co-worker for marketing

Lemonado is your marketing team's AI co-worker. It connects to your whole stack and does the work end-to-end: reporting, campaign checks, and analysis.

In-house teams and agencies use it so people spend attention on decisions, not busywork.

Stop fighting with data. Start feeding your AI.

Connect your data to AI and free your team from reporting and busywork.