Mobile Attribution: The Definitive Guide for 2025

April 26, 2021. iOS 14.5 dropped. I watched our attribution data collapse in real-time.

One day we had clean, deterministic data on 85% of iOS users. The next day, ATT prompts started appearing. Opt-in rates came in at 16%. Then 12%. Stabilizing around 25%. Overnight, three-quarters of our iOS measurement went dark.

"How are we supposed to know what's working?" my CEO asked in our emergency meeting. I didn't have a good answer. None of us did.

That was almost four years ago. Since then, I've rebuilt our entire measurement approach from scratch—twice. What I learned fundamentally changed how I think about attribution.

The Question That Matters

Mobile attribution answers the most critical question in marketing: "Where did this user come from?" Without it, you're flying blind—spending money without knowing what's working.

Attribution enables marketers to:

• Measure campaign performance accurately
• Allocate budget to highest-performing channels
• Optimize creative and targeting
• Calculate true return on ad spend
• Prevent fraud and invalid traffic

The Attribution Landscape in 2025

The mobile attribution ecosystem has evolved dramatically due to privacy changes:

Mobile Measurement Partners (MMPs)

MMPs are third-party platforms that provide attribution and analytics services. They act as neutral arbiters between advertisers and ad networks.

How MMPs Work

1. SDK Integration: MMP SDK installed in your app collects install and event data
2. Click/Impression Tracking: MMP tracks ad interactions across networks
3. Attribution Matching: Install data matched to ad engagement
4. Reporting: Dashboard shows attributed performance by source

Major MMPs

• AppsFlyer: Market leader, comprehensive features, strong anti-fraud
• Adjust: Privacy-focused, strong European presence
• Branch: Deep linking specialist, cross-platform measurement
• Singular: Cost aggregation focus, ROI measurement
• Kochava: Enterprise features, Marketers Operating System

iOS Attribution: SKAdNetwork (SKAN)

SKAdNetwork is Apple's privacy-preserving attribution framework. It provides campaign-level attribution without exposing user-level data.

How SKAN Works

1. User sees/clicks ad from participating network
2. User installs app from App Store
3. App notifies SKAdNetwork of install
4. Conversion value updated during measurement window
5. Apple sends postback to ad network (with delay + noise)

SKAN 4.0 Features

• Three conversion windows: 0-2 days, 3-7 days, 8-35 days
• Hierarchical source identifiers: More granular campaign data at scale
• Crowd anonymity: More data available for larger campaigns
• Web-to-app attribution: Safari click attribution support

"SKAN isn't a replacement for IDFA—it's a different paradigm. Success requires rethinking measurement strategies from the ground up."

Android Attribution: Privacy Sandbox

Google's Privacy Sandbox for Android introduces new privacy-preserving APIs to replace GAID-based tracking:

Attribution Reporting API

Provides event-level and aggregated attribution reports with differential privacy noise:

• Source registration on ad click/view
• Trigger registration on conversion
• Reports generated after privacy-preserving delays

Topics API

Enables interest-based advertising based on browsing history topics—without tracking individuals across sites.

Attribution Models

Different models assign credit to touchpoints differently:

Last-Touch Attribution

100% credit to the last touchpoint before conversion. Simple but ignores the full journey. Standard for mobile.

Multi-Touch Attribution (MTA)

Distributes credit across multiple touchpoints. More accurate but harder to implement:

• Linear: Equal credit to all touchpoints
• Time decay: More credit to recent touchpoints
• Position-based: First/last touch get more credit
• Data-driven: ML determines credit distribution

Incrementality Testing

The gold standard for true impact measurement. Uses holdout groups to measure lift caused by advertising vs. organic behavior.

Attribution Best Practices for 2025

1. Implement SKAN properly: Configure conversion values to capture meaningful signals
2. Prepare for Privacy Sandbox: Test Attribution Reporting API now
3. Use probabilistic modeling: Supplement deterministic data with modeled conversions
4. Invest in incrementality: Run regular lift studies to validate attributed data
5. Leverage first-party data: Build robust first-party data strategies
6. Unify measurement: Connect mobile, web, and offline data where possible
7. Work with trusted partners: Ensure MMPs and networks follow privacy guidelines

The Future of Attribution

Attribution will continue evolving toward privacy-preserving measurement:

• Data clean rooms: Privacy-safe collaboration between advertisers and platforms
• Federated learning: ML models trained without centralizing user data
• Cohort-based measurement: Group-level insights replacing individual tracking
• Media mix modeling renaissance: Aggregate-level measurement gaining importance