What is Probabilistic Attribution?

Probabilistic attribution estimates which ad led to an install using statistical modeling.

Instead of matching a definitive device ID (like IDFA), it matches patterns: IP address, device model, OS version, screen resolution, timezone, and other technical characteristics.

It's less accurate than deterministic attribution, but it works when device IDs aren't available.

How Probabilistic Attribution Works

The process:

1. User clicks an ad
2. Ad network collects device data (IP, user agent, timestamp, etc.)
3. User installs the app hours or days later
4. App opens and sends device data to the MMP
5. MMP compares device signatures from click and install
6. If signatures match closely enough, the install gets attributed to that click

No unique identifier changes hands. Just statistical probability that the person who clicked is the same person who installed.

What Data Gets Used

Common data points in probabilistic matching:

Network data:

• IP address
• ISP
• Connection type (WiFi vs. cellular)

Device data:

• Device model
• OS version
• Screen resolution
• Language setting
• Timezone

Temporal data:

• Time between click and install
• Time of day patterns

The more data points that match, the higher the probability of a correct attribution.

Accuracy Limitations

Probabilistic attribution isn't perfectly accurate.

Factors that reduce accuracy:

• Multiple users on the same IP (household WiFi, corporate networks)
• Common device configurations (iPhone 14 Pro, iOS 17.2, US timezone)
• VPN usage (changes IP address)
• Longer time gaps between click and install (data becomes less unique)
• High traffic volume (more potential matches to choose from)

Industry estimates put probabilistic accuracy at 60-80% within 24 hours, dropping significantly after that window.

This is why most MMPs use a 24-hour lookback window for probabilistic attribution versus 7 days for deterministic.

Deterministic vs. Probabilistic

Deterministic attribution:

• Matches using unique device identifiers (IDFA, GAID, click ID)
• Nearly 100% accurate
• Requires user opt-in on iOS (via ATT)
• Preferred method when available

Probabilistic attribution:

• Matches using device characteristics
• 60-80% accurate at best
• Works without user permission
• Fallback when deterministic isn't possible

MMPs prioritize deterministic attribution. Probabilistic only gets used when deterministic data isn't available.

When Probabilistic Attribution Gets Used

In 2025, probabilistic attribution serves as a fallback:

iOS scenarios:

• User didn't opt into ATT tracking
• Install falls outside SKAN measurement window
• Web-to-app installs pre-SKAN 4.0

Android scenarios:

• Google Play Store enforced policy changes limiting GAID access
• Users who opted out of ad personalization
• Non-Google Play installs (APK sideloading)

Cross-device scenarios:

• User clicks on mobile web, installs from desktop (or vice versa)
• Click happens on one device, install on another

The volume of probabilistic attribution has decreased significantly since iOS 14.5 (2021) as SKAN and deterministic alternatives became standard.

Privacy Concerns

Probabilistic attribution is essentially device fingerprinting.

Apple explicitly prohibits it in SKAN environments. Google has increasingly restricted it on Android.

Privacy frameworks view probabilistic matching as circumventing user consent mechanisms.

As of 2025:

• iOS: Largely blocked, violates App Store guidelines in most implementations
• Android: Still functional but increasingly restricted
• Web: Third-party cookies being phased out limits effectiveness

The long-term trend is away from probabilistic methods toward privacy-preserving frameworks like SKAN.

Technical Implementation

MMPs handle probabilistic attribution automatically. The process:

1. Collect device signature on click (via ad network SDK)
2. Collect device signature on install (via MMP SDK)
3. Run matching algorithm comparing signatures
4. Score potential matches based on similarity
5. Attribute to highest-probability match if it exceeds threshold

The matching threshold determines accuracy vs. volume tradeoff. Higher threshold = more accurate but fewer matches.

Fraud Considerations

Probabilistic attribution is more vulnerable to fraud than deterministic.

Common fraud tactics:

• Click flooding: Sending fake clicks with varied device signatures, hoping to match legitimate installs
• Install hijacking: Intercepting installs and claiming attribution
• Emulator farms: Generating installs with spoofed device data

Because there's no definitive identifier, fraudsters can manufacture matches that appear probabilistically valid.

MMPs use fraud detection algorithms to filter suspicious patterns, but probabilistic attribution inherently has lower fraud resistance than deterministic.

Reporting Distinctions

Most MMPs separate deterministic and probabilistic installs in reporting:

Attribution type breakdown:

• Deterministic (IDFA/GAID match)
• SKAN (iOS privacy-preserving)
• Probabilistic (fingerprint match)
• Unattributed (no match found)

Knowing what percentage of your installs are probabilistic helps assess data quality and fraud risk.

Lookback Windows

Standard lookback windows differ by attribution type:

Deterministic: 7 days for clicks, 1 day for impressions

Probabilistic: 24 hours for clicks, not typically used for impressions

SKAN: Built into Apple's framework (0-2 days, 3-7 days, 8-35 days for SKAN 4.0)

The shorter probabilistic window accounts for its declining accuracy over time.

Current State (2025)

Probabilistic attribution has decreased significantly:

• iOS: Effectively deprecated in favor of SKAN
• Android: Still used but declining as privacy frameworks evolve
• Volume: Typically 5-15% of total attributed installs (down from 40-50% pre-2021)

The industry consensus is that probabilistic attribution is a transitional technology, gradually being replaced by privacy-first frameworks that don't rely on device fingerprinting.

FAQs

What is probabilistic attribution?

Probabilistic attribution is a statistical method that estimates which ad led to an install by matching device characteristics (IP address, device model, OS version, etc.) when unique identifiers like IDFA aren't available.

How accurate is probabilistic attribution?

Probabilistic attribution is typically 60-80% accurate within 24 hours of the click. Accuracy drops significantly after 24 hours as device data becomes less unique. It's less accurate than deterministic attribution but serves as a useful fallback.

Is probabilistic attribution still used in 2025?

Yes, as a fallback when deterministic identifiers aren't available. It's most common for Android non-Google Play installs and iOS users who didn't opt into ATT tracking but installed outside of SKAN measurement windows.

Is probabilistic attribution the same as fingerprinting?

Yes. Probabilistic attribution is a form of device fingerprinting. It identifies users based on their device characteristics rather than explicit identifiers. This is why it faces increasing privacy restrictions.

Why is the probabilistic lookback window shorter?

Probabilistic attribution accuracy declines rapidly after 24 hours because device signatures become less unique over time. People change networks, update software, and patterns shift, making matches less reliable.

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Probabilistic attribution served an important role in mobile measurement before privacy frameworks matured. In 2025, it's a diminishing fallback method, useful in limited scenarios where deterministic data and SKAN don't provide coverage.