You spend money on ads but can't tell which channels bring in new players. Without proper tracking, you can't know if Facebook, search listings, or influencer links are driving installs. When you don't know which touchpoints matter, your return on ad spend stalls. You end up scrambling to cut costs or boost bids in the wrong places.
Every guess involves risk, wasted dollars, paused campaigns that could have succeeded, and no clear way to improve performance. This leads to funding ineffective campaigns while missing the ones that truly grow your user base.
Attribution modeling with SKAdNetwork (SKAN) brings you the clarity you need while respecting user privacy. By assigning credit for each install or in-app action to the correct marketing touchpoint, SKAN enables you to identify which ads and networks deliver high-value players.
In 2025, SKAN handles over 40% of all iOS attribution postbacks, making it the leading tool for privacy‑safe measurement on Apple devices. With proactive insights and conversion data linked to key game milestones, you can pause low-performing ads quickly and focus on the winners.
In this blog, you'll learn about key attribution models, how privacy changes on iOS and Android impact measurement, and practical tips for optimizing mobile UA with conversion values and multi-channel data.
Common Types of Attribution Modeling
Before setting up your measurement, it's helpful to understand the primary methods for assigning credit to your ads. Here are four common attribution modeling types you can use to see which channels drive installs:
1. Last‑Click Attribution
With last‑click attribution, you give 100% of the credit for an install to the final ad interaction before the user converts. For example, if someone taps a Google ad and then installs your game, that Google ad takes all the credit. This model is very simple and widely used in performance marketing, but it ignores every touchpoint before the last one.
2. First‑Click Attribution
First‑click attribution assigns all credit to the first ad or touchpoint that introduced a user to your app. If a player first sees a social post and then later installs the app after clicking an in‑app banner, the social post will get full credit. This model highlights which channels are most effective at driving initial discovery, although it overlooks the impact of mid and bottom-funnel influences.
3. Multi‑Touch Attribution (MTA)
Multi‑touch attribution splits credit across several interactions in a user’s journey. You might assign some credit to a TikTok video view, some to a search click, and some to an in‑app ad. This approach provides a more comprehensive view of how all touchpoints work together, but it requires additional data and setup. You can choose equal weights (linear), give more credit to later touches (time decay), or customize the distribution (U-shaped, W-shaped).
4. Media Mix Modeling (MMM)
Media mix modeling utilizes aggregate data rather than user-level signals to estimate the impact of each channel on installs and value. Modern “next‑generation” MMM blends historical data with last-click results to deliver privacy-compliant, near-real-time insights. MMM can capture broad, upper-funnel effects, such as brand awareness, that click-based models often miss.
Choose the model that fits your goals and data needs. Single-touch models work well for clear, short-term campaigns, while multi-touch and MMM offer deeper insights across longer user journeys. By understanding these four types, you can pick the right approach to measure and optimize your iOS campaigns.
Choosing the model is one part. Now, see how privacy changes affect how you can use them.
Also Read: Marketing Mix Modeling Made Simple for Game and App
Mobile Marketing Privacy Updates
As privacy rules tighten and platform policies restrict user-level tracking, measuring campaign performance has become more complex. Traditional attribution methods that rely on device identifiers are no longer reliable. Privacy-safe measurement now relies on aggregated data, probabilistic models, and server-side signals to preserve insights while protecting user anonymity.
Tracking users has become increasingly challenging, which directly impacts the application of attribution modeling. Here's what’s changing in 2025 and how you can keep your measurement strategy strong.
iOS: SKAN and AAK
Apple’s AppTrackingTransparency (ATT) framework restricts apps from accessing the Identifier for Advertisers (IDFA), a unique device code used for tracking user behavior across apps, thereby reducing visibility. To support attribution under these constraints:
SKAdNetwork (SKAN) returns aggregated install data with a delay (usually 24–48 hours) and limited granularity.
AdAttributionKit (AAK), launched with iOS 17.4, expands SKAN’s capabilities. It adds support for multiple app stores, sends more postbacks, and improves reporting speed, offering broader and more timely aggregated insights.
Android: GAID and Privacy-First Tools
Google still allows tracking with GAID, but it’s also shifting toward privacy-safe solutions:
Conversion APIs (such as Google Ads’ enhanced conversions) enable you to send server-side event data.
Google Analytics 4 (GA4) combines app and web data, utilizing modeling to fill measurement gaps, even without cookies or device IDs.
Platform Tools: Meta AEM and More
To complement SKAN and GA4:
Meta’s Aggregated Event Measurement (AEM) provides modeled conversion reports within hours. It helps you make faster bid decisions, though the data isn’t exact.
Use AEM for short-term feedback and SKAN or AAK for validating results over more extended periods.
Multi-Device Tracking: A Key Gap
One major risk is user drop-off when they switch devices, say, from mobile to desktop:
Use persistent identifiers (like login or email) to link activity across platforms.
Although toolsets differ between iOS and Android, the objective remains the same: to enable attribution without relying on personal data. Combining SKAN, AAK, GA4, AEM, and modeled reporting helps maintain measurement continuity while meeting privacy standards.
With Apple’s 2025 updates, especially AAK enhancements, understanding how attribution works under these new systems is critical.
Also Read: Meta AEM vs SKAN in 2025: Choosing the Right iOS Attribution Stack
SKAN and AAK Updates for Attribution in 2025
The original SKAdNetwork (SKAN) gave limited post‑install data (conversion values 0–63, no geo) to protect privacy. At Apple’s Worldwide Developers Conference, WWDC 2025, the company announced six major updates to AdAttributionKit (AAK) in iOS 18.4, enhancing SKAN’s flexibility for marketers.. Key features for game marketers include:
Overlapping Re-Engagement Windows: AAK now supports multiple overlapping re-engagement windows identified by conversion tags. You can run several re‑engagement campaigns simultaneously and track which specific ad or partner drove each reopen, even if they overlap.
Configurable Attribution Windows: The fixed 30-day click and 1-day view windows are no longer in effect. In iOS 18.4+, you can set click‑ and view‑through windows per network or campaign (or globally). Use shorter windows for performance ads and longer ones for branding, or disable view‑through entirely if it’s not reliable.
Configurable Attribution Cooldown: Define cooldown timers to prevent back‑to‑back events from stealing credit. For example, set a 6‑hour cooldown on installs and a 1‑hour cooldown on re‑engagements so that late clicks don’t reassign credit improperly.
Country Codes in Postbacks: Postbacks now include explicit country/storefront codes (subject to privacy thresholds), so you get regional install and re‑engagement data without sacrificing any conversion‑value bits.
Easier Testing Postbacks (Developer Test Mode): The new developer mode removes SKAN’s random delays and lets you trigger near‑instant test postbacks with custom conversion values and country codes directly from Settings, speeding up your validation cycles.
New Ad-Display Methods: AAK adds custom-click ads and view-through ads, recording clicks and impressions on any ad surface (inside or outside the App Store) beyond SKOverlay and SKStoreProductViewController. This gives you finer granularity on which creatives and placements drive installs and reopens.
Using these features actionably means:
Configure your SDK to assign conversion values (0–63) to key in-game milestones (e.g., tutorial completion, first purchase).
Use conversion tags when sending users via ads to track which creative or partner is responsible for each action.
Set window lengths and cooldowns that match your game’s engagement cycle (e.g., shorter windows for hypercasual games vs. full 30-day windows for long-tail RPGs).
With these updates to SKAN and AdAttributionKit, you can apply them to your strategies for more accurate and effective attribution modeling.
Also Read: Receiving Ad Attribution and Postbacks in SKAN 4.0
Mobile Game Attribution Modeling Strategies with SKAN
When you set up attribution modeling with SKAdNetwork (SKAN), clear processes help you measure your campaigns more accurately and safely. The tips below reflect updates for 2025 and show you how to maximize the benefits of SKAN and related tools.
1. Map conversion values to key game events
Conversion values are numerical indicators SKAN uses to track user actions post-install. Define simple conversion value tiers based on in-app events. For example, assign a value of 10 to tutorial completion and 20 to the first purchase. In SKAN 4.0, you can use up to four separate schemas, giving you both fine‑ and coarse‑grained tiers across three postback windows. Update these mappings when you add new features or offers so your postbacks always reflect the actual player value.
2. Unify data across channels with your MMP
Bring together SKAN data, ad network reports, and in‑app metrics in one dashboard. In AppsFlyer’s SKAN Conversion Studio or Adjust’s SKAN dashboard, use developer mode to preview postbacks, lock windows, and validate conversion-value mappings before going live. Ensure your MMP is tracking revenue and key events, and verify that your attribution windows align with your business objectives.
3. Leverage platform APIs and privacy‑safe modeling
Turn on Conversion APIs in Facebook, TikTok, and Snap for server‑side measurement. Meta’s AEM no longer limits you to eight events and now processes all standard and custom events automatically as of June 2025. On TikTok, switch to Advanced SAN to capture both ad links and SAN decisions for richer attribution data.
4. Combine marketing mix modeling with incrementality tests
Don’t rely on a single view of spend impact. Use a marketing mix model (MMM) to measure channel ROI over time and conduct A/B or geo-split lift tests for direct incrementality insights. This dual approach shows you where the budget moves to drive real incremental installs and revenue.
5. Enforce strict naming rules for clean analysis
Adopt a consistent campaign naming scheme that includes country, source, and creative type. For instance: US_FACEBOOK_VIDEO1. This consistency ensures that your reporting tool can slice data correctly and identify top‑performing assets without manual cleanup.
6. Validate the setup and iterate quickly
Before you launch, use your MMP’s developer or test mode to verify SKAN mapping and postback flows. AppsFlyer’s SKAN Conversion Studio lets you preview each window’s settings and lock windows by time or value to speed up postbacks. Once live, A/B tests different windows and cooldowns to see which aligns best with your in‑game engagement.
Schedule weekly lift tests (geo or user cohort) alongside MMM refreshes, and adjust SKAN windows or conversion values monthly based on performance trends.
By following these strategies, you’ll create a strong, privacy‑safe attribution system that adapts as your game grows and helps you invest where it counts.
Also Read: iOS Attribution Unlocked: Navigating SKAdNetwork, AEM & Probabilistic Models
Conclusion
As you look ahead, it's clear that attribution is heading toward privacy-first, modeled tracking. For 2025 and beyond, Apple’s AdAttributionKit (AAK) is already replacing what SKAN 5 was expected to be, bringing similar features such as multi-postbacks, rich conversion values, and even re-engagement tracking, although adoption is still uneven.
What it means for you:
Keep using SKAN or AAK to capture deterministic install data.
Add probabilistic models to estimate performance where SKAN/AAK lacks detail.
Set up Meta’s Aggregated Event Measurement or TikTok’s Advanced SAN to fill in fast data from primary channels.
Run marketing mix models (MMM) and lift tests to understand true spend impact and cross-channel effects.
Over time, as more ad networks fully support SKAN 4 and SKAN/AATK features, you'll gain richer data, like multiple postbacks, fine vs. coarse conversion values, and deeper event insights. That means you'll be able to optimize better and invest smarter, all while staying compliant and respecting user privacy.
To stay competitive in mobile user acquisition, blend deterministic and probabilistic data, leverage platform APIs, and rely on modeling strategies. As the ecosystem continues to evolve, your attribution setup will remain stronger and more reliable.
Start your 14-day free trial now to see how Segwise AI can help you make attribution actionable and drive more profitable user acquisition.
FAQs
1. What’s the difference between deterministic and probabilistic attribution?
Deterministic attribution uses exact matches, such as SKAN postbacks, to track installs, while probabilistic models fill in gaps based on aggregated patterns when exact data is unavailable.
2. How many SKAN postbacks can I get per install?
With SKAN 4 and AdAttributionKit, you can receive up to three postbacks over different time windows, providing more insight into user behavior over 35 days.
3. Why do SKAN and Meta AEM show different install numbers?
SKAN delays reporting and can drop low-volume data due to privacy thresholds, while AEM reports modeled events quickly, often showing smoother and higher counts.
4. Can you still measure ads from all networks with SKAN?
Yes. SKAN captures aggregated installs from any iOS ad network, so it shows broader campaign performance compared to tracking limited to Meta’s platforms.
5. Do I need both SKAN and media mix modeling (MMM)?
Yes. SKAN provides install-level data, and MMM analyzes aggregated trends across channels—using both helps you understand the full-funnel impact and optimize your spend.
