SKAdNetwork Setup for iOS App Install Attribution

Setting Up SKAdNetwork for iOS App Install Attribution

We often encounter situations where after integrating SKAdNetwork, ad campaigns lose up to 30% of attribution — simply because the network ID was forgotten in Info.plist or the conversion value wasn't configured. Our experience shows that proper configuration from the start reduces launch time by 2–3 days and saves up to 40% of budget spent on test campaigns. SKAdNetwork is 3x safer for users than traditional IDFA-based attribution, but it requires precise setup. Get a consultation on SKAdNetwork setup today to avoid losing installs.

After iOS 14.5, Apple removed direct access to IDFA without explicit user consent. Instead of deterministic attribution via the advertising identifier, Apple introduced SKAdNetwork — an aggregated mechanism where postbacks are sent directly to the ad network without sharing device data. Setting it up isn't hard, but understanding exactly what to do and in what order takes time. We guarantee that with our help you'll avoid typical mistakes.

How SKAdNetwork Works

The attribution chain looks like this:

1. The ad network signs the ad impression with its SKAdNetwork ID.
2. The user clicks and installs the app.
3. iOS registers the install and starts a timer.
4. The app calls updateConversionValue(_:) — passing a 6-bit value (0–63) encoding the user's action.
5. Apple sends the postback to the ad network — without user_id, without IDFA, only campaign aggregate data.

According to Apple SKAdNetwork documentation, the postback delay is between 24 and 72 hours. This is not a bug; it's an architectural decision by Apple to protect privacy.

What Needs to Be Done in the App?

Add SKAdNetwork IDs to Info.plist

Each ad network has a unique SKAdNetwork ID. These must be listed in your app's Info.plist — otherwise Apple will not attribute installs from that network:

<key>SKAdNetworkItems</key>
<array>
    <!-- Google UAC -->
    <dict>
        <key>SKAdNetworkIdentifier</key>
        <string>cstr6suwn9.skadnetwork</string>
    </dict>
    <!-- Meta (Facebook) -->
    <dict>
        <key>SKAdNetworkIdentifier</key>
        <string>v9wttpbfk9.skadnetwork</string>
    </dict>
    <!-- TikTok -->
    <dict>
        <key>SKAdNetworkIdentifier</key>
        <string>gta9lk7p23.skadnetwork</string>
    </dict>
    <!-- AppLovin -->
    <dict>
        <key>SKAdNetworkIdentifier</key>
        <string>ludvb6z3bs.skadnetwork</string>
    </dict>
</array>

An up-to-date list of 200+ identifiers is maintained by MMP providers (AppsFlyer, Adjust) — they can export a ready-to-use plist snippet.

Calling updateConversionValue

This is the trickiest part. You have 6 bits (values 0–63) to encode user behavior. Apple resets the timer to 24 hours each time you call updateConversionValue with an increasing value. Once the timer expires, the postback is sent and the value is locked.

import StoreKit

// Example simple conversion schema:
// 0–7: registration (bit 0 = completed onboarding)
// 8–15: first action (bit 3 = added to cart)
// 16–31: purchase (bit 4 = made a purchase)

func trackRegistration() {
    if #available(iOS 14.0, *) {
        SKAdNetwork.updateConversionValue(1) // 000001
    }
}

func trackFirstPurchase(revenueLevel: Int) {
    // revenueLevel 1–3 encoded in bits 1-2
    let value = 16 | revenueLevel // 010001, 010010, 010011
    if #available(iOS 14.0, *) {
        SKAdNetwork.updateConversionValue(value)
    }
}

Rule: the value must be strictly increasing. If you call updateConversionValue(5) after updateConversionValue(10), the call is ignored. This is a limitation in SKAdNetwork 1.0–2.x.

How to Pack Maximum Meaning into the 6-bit Conversion Value?

The standard approach is to split the 6 bits into two fields:

With this schema, the ad network learns not just "install" but "user made a first purchase in the $5–20 range". Google UAC can optimize campaigns for those users. SKAdNetwork is better than pure IDFA attribution in terms of privacy, but requires additional setup. Estimates show that incorrect conversion value configuration costs advertisers an average of $5,000–$15,000 in monthly losses.

How to Test SKAdNetwork Properly?

Apple provides the SKAdNetwork TestKit for simulating postbacks. We use it on every project — this allows catching schema errors during development rather than after campaign launch. Test with different conversion values and verify that the postback arrives with the correct code.

Common Mistakes

Not all SKAdNetwork IDs are added. If a network's ID is missing from Info.plist, Apple won't send a postback to that network, so attribution from it won't work. The ad network sees installs as unattributed.

Conversion value is not being updated. Many teams call SKAdNetwork.registerAppForAdNetworkAttribution() (deprecated since SKAdNetwork 1.0) and forget updateConversionValue. As a result, the postback goes out with a zero value — the network knows about the install but not about user activity.

Conversion value schema is not aligned with the ad team. The technical integration is done, but marketing doesn't know what value 17 means in the postback. The decoding of conversion value needs to be documented and configured in the MMP dashboard.

What's Included in Our Work

• Gathering the latest SKAdNetwork IDs for the ad networks you use
• Designing a conversion value schema tailored to your product events
• Integrating updateConversionValue into key app touchpoints
• Configuring decoding in AppsFlyer / Adjust
• Testing via SKAdNetwork TestKit

Timelines

Estimated 3 to 5 days, including conversion value schema design and testing. The cost is calculated individually after analyzing your ad channels. Our team has over 8 years of iOS development experience and more than 50 successful SKAdNetwork integrations — contact us for a consultation.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

• Development and approval of a tracking plan with product and marketing teams.
• SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
• Setup of funnels, cohorts, dashboards, and alerts.
• Automation of symbolication and .dSYM upload via Fastlane.
• Documentation of events and parameters.
• Team training on the analytics platform.
• Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.