An accurate view of an individual’s probability to convert at every touchpoint.

How Corvidae uses clicks and impressions

Technical overview.

Before ingestion into our Neural Network, our proprietary Machine Learning model ingests data from each marketing channel and stitches sessions across devices and data silos.

By using probabilistic techniques, we can generate a view of individual sessions and unify a user’s activity across all touchpoints to give a clear view of where conversions are taking place – and the touchpoints that influence these conversions.  

But Corvidae allows you to take this one step further. 

By understanding where conversions are taking place in the customer journey, we can identify which channels and campaigns have opportunities for growth and begin to automate spend allocation.

Allowing you to easily shift marketing spend from areas that are underachieving and move it elsewhere – resulting in lower CPAs and increased ROI. 

Probabilistic vs Deterministic data

How Corvidae boosts your attribution data.

Complex customer journeys involving interactions with a range of devices and media – often over an extended period – present some very real challenges when it comes to collecting and effectively attributing data around these journeys.  

Our attribution model provides marketers with an accurate view of an individual’s probability to convert at every touchpoint.

It does this by effectively rebuilding your marketing data and laying online data alongside offline data to identify overlaps in time, location, and other identifiers. 

For example, if a customer shows a heightened level of engagement with one touchpoint than is commonly seen by most customers, this is then given a greater weight when attributing the eventual revenue to that individual event.

Corvidae gives a significant boost in attributable data, often by 300% or more compared to Google or Adobe analytics packages.

Enhanced campaign-level attribution

One of the key benefits of Corvidae is the more granular campaign level view of attribution it provides.
Channel attribution still exists but is aggregated. 

This means that you can split up and view the data in different ways: 

Paid Search

  • By source (Google/Bing/etc.)
  • By campaign, including brand vs. non-brand 
  • By competitor key phrases

Display/Native Ads

  • By source (e.g. Google DV360, Amobee, Beeswax)
  • By campaign
  • By referrer


  • By campaign
  • By source
  • By referrer (Note: referrers don’t usually allow impressions to be tracked via them.)

Ready to try attribution the Corvidae way?

Getting started on the road to accurate, cookieless attribution doesn’t have to be complicated.
Our team are on-hand to make the move to a new attribution tool as easy as possible.

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