Mobile App Terminology

Probabilistic attribution

What is probabilistic attribution?

Also referred to as fingerprinting, probabilistic attribution is a mobile attribution technique often used in conjunction with deterministic methods in order to recognize a mobile device, laptop, or browser device. Deterministic methods, such as cookies or advertising identifiers (IDFA, GAID), use a unique identifier associated with a device, whereas probabilistic methods rely on collecting behavioral and other user agent data and then attempting to match it with other records. The data collected to perform probabilistic attribution includes a user’s IP address, operating system, mobile hardware, web browser, and more.

Since probabilistic attribution relies on trying to match device data with a user database, this technique is typically less accurate and is used as a fallback when a deterministic identifier is unavailable. In addition, the data collected for probabilistic attribution, such as the user’s IP address, changes much more frequently than a device identifier, which means that probabilistic attribution has a shorter attribution lookback period than deterministic attribution. Specifically, the accuracy of correctly identifying a device decays exponentially after 24 hours, so this is the typical cutoff period for the probabilistic attribution lookback window.

In iOS 14, fingerprinting or probabilistic attribution shares the same requirements as using an advertising identifier, the IDFA, in terms of getting user consent for tracking.

Uses of probabilistic attribution

One of the primary use cases of probabilistic attribution is in mobile web environments since deterministic device identifiers are generally only available in mobile app environments. For example, if an advertiser is running a web-based campaign that is targeting mobile devices and the Google Install Referrer isn’t available, probabilistic attribution could be used.

In addition to web-based mobile ad campaigns, probabilistic attribution can often be used for tracking email campaigns that may be opened on a mobile device, or organic downloads from a mobile landing page.

Ultimately, the goal of probabilistic attribution is to have a fallback in place if a deterministic identifier is not available, ensuring that advertisers can still determine the performance and ROAS of their marketing campaigns. Even if probabilistic attribution isn’t 100% accurate, this additional attribution data still gives advertisers an advantage over those that solely rely on deterministic attribution.

As AlgoLift highlights, this data is critical in predicting LTV and distributing ad budgets accordingly:

Later evaluations of ROAS for particular campaigns or channels should incorporate more matured behavioural data, as opposed to operating on just an early predicted LTV bucket or conversion event. This is where probabilistic attribution becomes critical — so that advertisers can properly distribute their most up to date revenue projections amongst campaigns and channels.

In addition to mobile attribution on specific devices that lack a unique identifier, probabilistic attribution is valuable for cross-device tracking and attribution. Cross-device attribution allows advertisers to understand how their marketing efforts are impacting their customer’s journey, which is often a fragmented user experience involving multiple platforms, devices, and campaigns.

How Singular uses probabilistic attribution

Since probabilistic attribution is less reliable than deterministically matching devices, Singular uses this technique as a fallback method if there is no unique identifier available, or in the case of Android devices, if the Google Install referrer is unavailable. As discussed in our probabilistic attribution FAQ, Singular can collect publicly available data points from HTTP headers including:

  • IP address
  • Platform
  • OS name and version
  • User agent
  • Timestamp

Singular uses the tracking URL that advertisers use in their campaigns in order to collect the above information. This information is then stored in a database and referenced when attempting to match a device with a specific user action, such as an app install.

It’s important to note that starting with iOS 14.5, probabilistic attribution is only allowed in certain situations set forth by Apple’s privacy and data use policy:

Starting with iOS 14.5, iPadOS 14.5, and tvOS 14.5, you’ll need to receive the user’s permission through the AppTrackingTransparency framework to track them or access their device’s advertising identifier.

Based on Singular’s conversations with Apple, we are unable to provide probabilistic attribution for certain situations in devices using iOS 14 and above. That said, we have put in new controls that prevent any non-compliant attribution in these situations. As you can see from the attribution workflow table below, probabilistic attribution can be used if users have opted in and the media type is either paid or owned:

Similar to the iOS policies, Singular only uses probabilistic attribution for Google devices in accordance with the user-resettable Google Advertising ID (GAID).

In order to support mobile advertisers using Singular’s platform, we have implemented several requirements to ensure probabilistic attribution is performed in a privacy-preserving manner. The first step to ensure these requirements are met is to reach out to your Singular partner manager or email and they will assist you in enabling probabilistic attribution. If you are an integrated partner with web inventory, you can learn more about these requirements in our Help Center here.


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