Facebook recently announced it was making changes to its reporting APIs that ultimately will restrict access to raw user-level view-through data after April 22. From this date, Facebook view-through attributed installs will be labeled as “restricted” vs being attributed to Facebook ad campaigns.

This restriction applies to both MMPs and advertisers directly. As an alternative, Facebook is providing view-through attribution data at an aggregated cohort level. If view-through attribution is used by advertisers acquiring users from Facebook campaigns they will be unable to understand the effectiveness of their user acquisition campaigns at the user-level for users who’ve viewed but not clicked on their ads.

Quick definitions:

View-through attribution (VTA) allows advertisers to attribute installs or actions from a user viewing a mobile ad.

Click-through attribution (CTA) allows advertisers to attribute installs from after a user has clicked on a mobile ad.

User acquisition managers modulate the effectiveness of these two attribution methodologies by varying the window of time that an install can be attributed after the view or click happened. An example might be a VTA window of 1 day and a CTA window of 2 weeks.

VTA can contribute a significant proportion of the installs delivered on mobile ad campaigns, and Facebook limiting access to this metric on the user-level means that growth teams can’t accurately understand the effectiveness of their Facebook campaigns against other channels down to the deepest granularity.

Solutions going forward:

Ignore VTA on Facebook:

This is the easiest and most conservative route to go, although potentially the most damaging. If Facebook VTA is a significant proportion of your attributed installs on Facebook, your ROAS will be underestimated. Although there will be purity in the data and you will continue to attribute using deterministic, not probabilistic methodologies.

Modulate the CTA attribution window:

It will be possible to understand the effectiveness of Facebook campaigns at the cohort (campaign) level through an MMP API and therefore you’ll be able to understand your ratio of CTA:VTA installs on each campaign. With this information, you can modulate the click-through window based on the incremental installs you’re receiving through VTA on each campaign.

With this approach, you are rewarding each campaign for each VTA install it drives, but in a roundabout way — view-through and click-through attribution should be independent. The goal should be to properly account for view-through installs that received no click and the associated return on ad spend (ROAS), but simply turning another “knob” to get the same attribution ratio may not be the best option.

Apply a multiplier across the campaign cohort:

For a solution to this problem, perhaps the best place to look is how AlgoLift currently accounts for LAT (limited ad tracking) users on Apple Search campaigns. When a user has LAT turned-on on their device the device ID is sent to the MMP as a string of 0’s and the user is typically attributed as organic.

To account for ROAS that came from LAT campaigns (which can be 30%+) AlgoLift offers their clients the ability to apply an estimated multiplier using the below formula at the account or campaign level:

A similar methodology can be used to account for VT installs from Facebook:

Then when assessing the user-level LTV of a campaign, the revenue from VT installs from the cohort API should be attributed evenly across users acquired from the campaign.

https://docs.algolift.com/docs/faq/facebook-and-google-faq#how-is-facebook-view-through-attribution-vta-handled-by-algolifts-ia-system