Insights

The Hidden Costs of Probabilistic Attribution (and Why SKAN Wins Long-Term)

November 3, 2025

Understanding the shift: AAK, SKAN, and iOS attribution

Probabilistic attribution, often referred to in the industry as Apple Attribution Kit (AAK), uses device signals, modeling, and time-based matching to infer where app installs come from. SKAdNetwork (SKAN), on the other hand, is Apple’s native framework that attributes installs without device IDs, in a privacy-compliant and aggregated way.

Featured snippet-friendly difference:

Probabilistic user acquisition estimates which ad led to an app install using modeling and device data, while SKAdNetwork validates installs via Apple’s privacy-focused framework without using device identifiers. The core trade-off: immediacy and granularity versus accuracy and compliance.

As privacy regulation tightens and Apple’s ecosystem evolves, mobile marketers must understand how attribution methods impact growth, measurement, and long-term strategy.

What is probabilistic attribution and why did it gain traction?

Probabilistic attribution maps ad clicks to app installs using signals such as IP address, device type, OS version, and click timing¹. It gained popularity due to:

Near real-time reporting
High data granularity
Fast optimization loops

Example: A mobile game publisher launches a UA campaign and sees conversions attributed within minutes. The MMP provides granular insights like device type and source app, helping the team rapidly test creatives.

But this speed comes at a cost, especially in today’s privacy-first world.

The hidden costs of probabilistic attribution

What happens when attribution data is inflated or inaccurate?

Probabilistic methods are modeled, not deterministic. They can double-count or over-attribute installs that may actually be organic or misattributed².

Example: A marketer thinks their ad campaign drove 10,000 installs. Later, they realize 30% were organic users incorrectly assigned to paid campaigns. This distorts performance metrics and leads to inefficient budget allocation.

Over time, this results in poor decision-making, unreliable ROAS forecasts, and misaligned KPIs.

How privacy changes have weakened probabilistic attribution

With ATT adoption across iOS, most users opt out of tracking. As of mid-2024, the global ATT opt-in rate is only about 13.85%³. That means most device-level data used by probabilistic models is no longer accessible.

Example: An entertainment app runs UA on iOS using probabilistic attribution. However, due to signal loss from opt-outs and shared IP addresses, install attribution becomes inconsistent, especially in privacy-sensitive regions.

In the end, performance metrics look volatile, and the UA team loses trust in their data.

Short-term gains can lead to long-term inefficiencies

Probabilistic data is fast, but it’s often directional, not definitive. Over-reliance can lead teams to scale campaigns prematurely based on inflated metrics.

Example: A team uses early install signals to aggressively scale a video ad that initially shows low CPI. But by the time actual purchase data arrives, LTV is below threshold and the campaign has already burned through budget.

Probabilistic models may appear granular, but they rely on inferred relationships that may not reflect actual user behavior.

Example: Attribution based on IP and user agent works in some regions but fails in countries where carriers use shared IPs. Attribution looks good on the dashboard, but post-install engagement doesn’t align with real value⁴.

These blind spots waste time debugging, lead to false confidence in campaign results, and drain creative and data science resources.

Why SKAdNetwork (SKAN) wins for long-term measurement

SKAN is Apple’s privacy-first attribution framework. It eliminates reliance on device IDs and delivers validated install data through delayed postbacks and aggregated conversion values⁵.

Despite some limitations, SKAN is fast becoming the foundation of iOS attribution because it aligns with Apple’s privacy roadmap and ensures measurement durability.

The benefits of SKAN in a privacy-first ecosystem

Zero ad fraud: Installs are validated by Apple’s systems
Future-proof: With SKAN 5 and successor frameworks on Apple’s roadmap, SKAN is not going away⁶
Reliable LTV modeling: With properly designed conversion-value schemas, marketers can map post-install actions that indicate long-term value

Example: Aarki powers a SKAN-first campaign using a 7-day conversion schema. While data is delayed, the insights are deterministic and used to optimize both creative and audience targeting effectively.

What about the trade-offs?

Yes, SKAN comes with challenges:

Postbacks are delayed
Data is aggregated and lacks user-level insights
Creative-level insights require schema planning and rigorous testing

But smart marketers can manage these issues. For instance, using Aarki’s unified creative framework and AI-powered variant testing allows performance optimization even within SKAN’s constraints⁷.

Comparison table: Probabilistic attribution vs SKAdNetwork

Metric	Probabilistic Attribution	SKAdNetwork (SKAN)
Data granularity	High (device-level signals)	Lower (aggregated data)
Privacy model	Inferred, uses sensitive signals	Privacy-first, no device IDs
Conversion windows	Real-time, immediate	Delayed (1-3 days)
Reporting cadence	Fast (minutes to hours)	Slower (postbacks, days)
Compatibility	Broad, with most MMPs	Native to iOS, expanding with SKAN 5
Fraud resistance	Moderate, prone to spoofing	High, Apple-validated installs

What this means for you as a mobile marketer

Use SKAN as your measurement foundation: Probabilistic can support early testing, but SKAN should be the truth layer for performance and scaling decisions.
Invest in schema design and creative testing: Build SKAN-friendly schemas that map early actions (like onboarding, purchases) to conversion values.
Use Aarki’s privacy-first DSP and AI framework: Integrate creative and media to optimize UA and retargeting campaigns in a SKAN-compliant way.

Frequently asked questions

Can probabilistic attribution still be helpful?

Yes, in limited scenarios such as creative testing, retargeting dormant users, or regions with high ATT opt-in. For example, if you’re running re-engagement campaigns on existing users with known identifiers, probabilistic models can provide directional insights. However, it should not be the primary measurement source in a privacy-constrained ecosystem, especially for user acquisition at scale.

When should I switch fully to SKAN-based measurement?

Most advertisers are already prioritizing SKAN in 2024. If your opt-in rate is below 20%, or if you want measurement that’s scalable and policy-aligned, now is the time to go SKAN-first⁸.

How do I reconcile SKAN and probabilistic signals?

Aarki and other leaders recommend a unified measurement approach, also called SSOT (Single Source of Truth), where SKAN data serves as the baseline and probabilistic insights act as directional complements⁹.

Does SKAN support retargeting and in-app optimization?

Yes, newer versions of SKAN and Apple’s attribution updates are evolving to support re-engagement and richer post-install tracking all while remaining privacy-safe¹⁰.

Conclusion

Probabilistic attribution once provided speed and granularity. But in today’s iOS world, it carries hidden costs: inflated performance data, compliance risks, and misaligned strategy. SKAdNetwork, while less granular, delivers deterministic, privacy-compliant measurement.

For marketers ready to lead in 2026 and beyond, SKAN is the framework to trust. At Aarki, SKAN-first thinking is already integrated across creative, media, and measurement. The future of mobile attribution isn’t modeled, it’s verified.