Overview

Measuring attribution is becoming increasingly challenging and complex. As the privacy landscape evolves, shifting guidelines and regulations become more and more difficult to keep up with. As these rules grow and change, attribution solutions are becoming more intricate and harder to maintain.

On top of all this, there is a growing number of opted-out users in marketing programs, lowering the accuracy of campaign performance measurements. This leaves you guessing as to how to optimize user engagement, especially among iOS users.

What Is PAM?

Predictive Aggregate Measurement (PAM) is an innovative approach to mobile campaign attribution, designed for performance marketers navigating today’s complex, privacy-focused mobile measurement world.

PAM is designed to navigate the complexities of today's changing privacy landscape while still expanding attribution coverage.

With PAM, you can unlock additional insights for users previously marked as either Unattributed or reported under SKAN.

By utilizing our predictive modeling tools alongside strict privacy protocols, PAM helps safeguard user privacy while also providing marketers with the most granular and effective reporting methodology available for each unique situation.

Our dynamic models adjust to the data available, employing the most precise attribution methods for each situation. We do this with user privacy in mind, so marketers can focus on analyzing performance and optimizing their strategies.

Data Precautions

PAM maximizes attribution coverage while still remaining privacy-focused.

Below are the methods PAM uses to help you meet your privacy compliance requirements.

Data Collection

PAM collects campaign data in a way that prioritizes user privacy by using aggregated identifiers and signals for users who have opted out of tracking.

These users' conversions will still be annotated with campaign data. However, this campaign data will not include individual identifiers (such as device IDs).

Data is still gathered from various linking touchpoints across multiple platforms, offering a comprehensive view.

Thresholding

PAM also applies thresholding techniques to the data collected, which helps prevent individual user actions from being traced back to them across mobile apps and the web. In this way, PAM helps maintain user anonymity.

These techniques allow marketers to access valuable insights through aggregated data, without exposing individual user-level information.

Data Aggregation

To further help protect privacy, PAM aggregates campaign data, bringing individual data points into a broader dataset.

This provides a comprehensive view of campaign performance but doesn't expose data about the individual user.

The aggregated data is presented in a way that marketers can get actionable insights while still preventing user-level identification.

Together, these three data precautions help ensure that your user's privacy is respected, while still giving you valuable insights.

Implementation

Use the steps below to enable PAM at the org-level or app-level.

Prerequisites

To enable PAM, you first need to add our Performance product to your Branch account. To learn more about our products, visit our packaging page.
You also need to have access to make changes at the org-level or app-level, depending on what level you want to enable PAM on.

Org-Level Enablement

To enable PAM at the org-level:

Go the Branch Dashboard and select the org that you want to enable PAM for.
Once you've selected the org, use the left-side navigation bar to click on Configuration, which is in the Configure section.
Next, navigate to the Attribution tab. There, you will see a switch that enables PAM.

Be careful! This enables PAM for the entire org.
Flip the switch to enable PAM for the org, then make sure to click Save at the bottom of the page.

App-Level Enablement

To enable PAM at the app-level:

Go the Branch Dashboard and choose a specific app you want to enable PAM for.
Use the left-side navigation bar to click on Configuration, which is in the Configure section.
Next, navigate to the Attribution tab.
Check to see if the switch inside the Inherit Organization Attribution Settings section is enabled. If it is, then your app is inheriting attribution settings from the org it lives under.

Decide if you still want to make PAM changes at the app-level.
Also note that the Attribution Windows section will be greyed out if your app is inheriting from org-level attribution settings.
If you've taken note of the org-level settings and you still want to enable PAM at the app-level, flip the PAM switch and then click Save at the bottom of the page.

Watch the video below for an illustrative demonstration of how to turn PAM on at the app-level:

PAM Dashboard Data

Dimensions

There are certain dimensions that are particularly relevant to PAM. These are pam enabled and aggregate measurement.

Within the Branch Dashboard, these dimensions can be found:

On the main Summary page, within the All Data or Ads tabs.
On the Sources page.
On the Analytics page, within the Activity tab.

When comparing by pam enabled or aggregate measurement, we recommend also comparing by OS or platform so that you can differentiate the PAM privacy preserving uplift (when applicable) against Android and other opted-in iOS Users. This ensures your opted-in user performance is not hidden under PAM’s privacy threshold (if/when it applies).

PAM Enabled

This dimension identifies whether the conversion event was attributed via PAM or not.

The possible values for this dimension are:

true - when PAM was used to attribute the event.
false or unpopulated - when the attribution happened via other mechanisms, the data is unattributed via PAM, or PAM was not enabled for the selected time frame.

Aggregate Measurement

This dimension is used to identify events attributed via Meta AEM or gBraid.

The possible values for this dimension are:

true - when Meta AEM or gBraid was used to attribute the events.
false or unpopulated - when the attribution happened via other mechanisms, the data is unattributed via aggregate methods, or aggregate measurement was not enabled for that ad partner at that time.

Pre-Built Views

Use the following URLs to for sample configurations of Branch Dashboard reports.

PAM Enabled View
Compare by platform and pam enabled, where feature equals paid advertising.
Aggregate Measurement View
Compare by platform and ad partner and aggregate measurement , where ad partner equals Google AdWords or Facebook.

FAQ

Does Branch offer probabilistic matching?
Yes - Branch provides customers with both Predictive Modeling (PREM) and Predictive Aggregated Measurement (PAM).
What is required to implement PAM?
Implementing PAM doesn't require any engineering resources, and is an easy lift for marketers. Simply follow these implementation steps to enable PAM at the org-level or app-level with a switch.
What does Undisclosed mean in Branch Dashboard reports?
When data attributed via PAM does not meet privacy thresholds for the selected date range, Branch will display Undisclosed in the Dashboard. This threshold may be surpassed once campaign performance improves enough to reveal the numerical PAM attribution uplift.
Is Branch reverting to modeled conversions?
Modeled conversions from Ad Networks will flow into the attribution waterfall of PAM.
How will Branch dedupe this aggregate data against the log-level owned channel data?
PAM will utilize the deduplication logic we have with opt-in conversions and SKAN. The data will live within
our Event-Level Reporting suite and be deduplicated against all other sources within our Unified View Analytics.
What do “Differential Privacy Controls” entail?
Differential Privacy Controls include various end-user privacy practices that neither rely on nor expose individual identifiers across apps and websites. Examples of Differential Privacy Controls include (but are not limited to): noise introduction, thresholding, etc.
Can you consider a conversion deterministic if you’re using an aggregate ID?
Yes, if the aggregate ID is not individually identifying. This implies a many to one relationship, which can be pulled deterministically and referenced deterministically.
Will this affect channels other than opted-out iOS users under paid ads?
No. PAM increases visibility for opted-out iOS users under paid ads without affecting SEO measurement or other channels. Users previously marked as either Unattributed or reported under SKAN will be available for additional insights under PAM.
Is fingerprinting forbidden?
Yes, fingerprinting is forbidden. Branch does not use fingerprinting - we utilize probabilistic matching techniques.