And as such, marketers are understandably considering their options for accurate, and compliant, marketing measurement.<\/p>\n\n\n\n
So, what level of attribution is really available within Google Analytics \u2013 and what attribution models does it use to provide this?<\/p>\n\n\n\n
Read on, as we dive into this topic:<\/p>\n\n\n\n
- \n
- What attribution models are available in Google Analytics?<\/a><\/strong><\/li>\n\n\n\n
- How does Data Driven Attribution work?<\/a><\/strong><\/li>\n\n\n\n
- What attribution models are not available in GA4?<\/a><\/strong><\/li>\n\n\n\n
- Why is Google removing these models?<\/a><\/strong><\/li>\n\n\n\n
- The problem with using Google Analytics for attribution<\/a><\/strong><\/li>\n<\/ul>\n\n\n\n
<\/div>\n\n\n\n\n<\/div>\n\n\n\nFree download: A Quick Guide to Compliant Marketing Analytics<\/a><\/strong><\/p>\n\n\n\n
<\/div>\n<\/div><\/div>\n\n\n\n<\/div>\n\n\n\nWhat attribution models are available in Google Analytics?<\/h2>\n\n\n\n
<\/div>\n\n\n\nHistorically, Google has received a bad rap for it\u2019s over reliance on Last Click<\/a> to provide its users with attribution reports.<\/p>\n\n\n\n
However, from September 2021, they moved away from this and instead introduced a new Data Driven Attribution (DDA)<\/strong> model as their default.<\/p>\n\n\n\n
Marketers do have the option to opt-out from using this, as Google has kept five rules-based attribution models<\/a> in the mix:<\/p>\n\n\n\n
- \n
- Last click<\/li>\n\n\n\n
- First click<\/li>\n\n\n\n
- Linear<\/li>\n\n\n\n
- Time decay<\/li>\n\n\n\n
- Position-based<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n
However, some of these are only available for a little while longer \u2013 as we\u2019ll cover later in this blog.<\/p>\n\n\n\n
<\/div>\n\n\n\nHow does Data Driven Attribution work?<\/h2>\n\n\n\n
<\/div>\n\n\n\nGoogle\u2019s DDA uses Machine Learning to understand how each marketing touchpoint contributed to a conversion, while remaining privacy compliant.<\/p>\n\n\n\n
The advertised benefits of this new model include:<\/p>\n\n\n\n
- \n
- Learn which keywords, ads, ad groups, and campaigns play the biggest role in helping you reach your business goals<\/li>\n\n\n\n
- Optimise your bidding based on your specific account\u2019s performance data<\/li>\n\n\n\n
- Choose the right attribution model for your business, without guesswork<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n
What attribution models are not available in GA4?<\/h2>\n\n\n\n
<\/div>\n\n\n\nIn April 2023, it was announced that Google would be removing some core attribution models from its platform<\/a>, as well as Google Ads:<\/p>\n\n\n
\n![\"\"](\"https:\/\/corvidae.ai\/app\/uploads\/2023\/05\/image-1.png\")
<\/figure><\/div>\n\n\n- \n
- First click<\/li>\n\n\n\n
- Linear<\/li>\n\n\n\n
- Time decay<\/li>\n\n\n\n
- Position-based<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n
In May 2023<\/strong>, these models will be unavailable for any new conversion actions.<\/p>\n\n\n\n
From June 2023<\/strong>, Google will begin removing the ability to select these models for ads that don\u2019t already use them for reporting within Google Ads.<\/p>\n\n\n\n
They will then be officially sunset in Google Analytics and Google Ads beginning in September 2023<\/strong>.<\/p>\n\n\n\n
<\/div>\n\n\n\nWhy is Google removing these models?<\/h2>\n\n\n\n
<\/div>\n\n\n\nAccording to Google<\/a>, these attribution models are being sunset due to \u201cincreasingly low adoption rates, with fewer than 3% of conversions in Google Ads using these models\u201d.<\/p>\n\n\n\n
Following the announcement from Google, Corvidae\u2019s Senior Software Sales Executive, Pete Allcock, shared some thoughts on the news over on LinkedIn<\/a>:<\/p>\n\n\n\n
\n
\u201cDespite the vast majority of businesses still using Last Click as the preferred model; on the face of it, it seems a weird decision to remove features from a new platform which is still being developed.<\/em><\/p>\n\n\n\n
“But, when you consider what we are actually dealing with; it all begins to make a little more sense\u2026<\/em><\/p>\n\n\n\n
\u201cFirst party cookies really struggle to connect a customer journey cross-device\/browser\/user-agent etc. As a result, upper and middle funnel are disconnected.<\/em><\/p>\n\n\n\n
\u201cSo, regardless which model you pick (this includes Data Driven Attribution – DDA) you will be skewed towards Last Click anyway. As a result, we are really looking at Last Click vs Almost-Last Click.<\/em><\/p>\n\n\n\n
\u201cWith everyone using the same measurement protocols, the inevitable happens, and we all end up fishing in the same pond – largely at the bottom of the funnel; which further impacts CPA.<\/em><\/p>\n\n\n\n
\u201cSo, who benefits from increased ambiguity and increased cost?! I heard you ask.<\/em><\/p>\n\n\n\n
\u201cFar be it from me to suggest, but surely, it’s the very platform you’re spending your budget with; the same one that provides the measurement you’re basing your decisions on.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n
<\/div>\n\n\n\nThe problem with using Google Analytics for attribution<\/strong><\/h2>\n\n\n\n
<\/div>\n\n\n\nAs Pete covered in his LinkedIn post, the real problem with marketing attribution<\/a> doesn\u2019t sit with the attribution model.<\/p>\n\n\n\n
It comes down to the way that data is gathered and collated in the first place.<\/p>\n\n\n\n
As such, cookies aren\u2019t fit for purpose. They provide marketers with disjointed user paths that are impossible to optimise.<\/p>\n\n\n\n
And while Google are soon to deprecate third-party cookies from Chrome and have moved to a Machine Learning-focused model, this doesn\u2019t solve the underlying issue.<\/p>\n\n\n\n
CEO and Founder, Chris Liversidge, summarised this in a recent webinar<\/a>:<\/p>\n\n\n\n
\n
\u201cGA4 & Adobe are heavily reliant on the cookie to stitch paths together.<\/em><\/p>\n\n\n\n
\u201cUse of Google Signals and ID 2.0 3rd party data sharing have little to no impact on stitching success.<\/em><\/p>\n\n\n\n
\u201cCross-device user journeys are impossible to effectively track and optimise using GA or Adobe, and Data Driven Attribution (DDA) is siloed to those broken paths, creating misleading results.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n
<\/div>\n\n\n\n![\"\"](\"https:\/\/corvidae.ai\/app\/uploads\/2023\/05\/image-2.png\")
<\/figure><\/div>\n\n\n<\/div>\n\n\n\nAs we can see in the graphic above, by relying on cookies to track journeys, once a user switches to a different device, it splits a full journey into two separate paths.<\/p>\n\n\n\n
If we were to apply Google\u2019s DDA model to the converting path (Path B)<\/strong>, we\u2019re applying more revenue than we should to the three touchpoints that are visible because we\u2019ve excluded the the touchpoints that were in Path A<\/strong>.<\/p>\n\n\n\n
So, we\u2019re left with overinflated reports and a skewed view of how our marketing activity is really performing.<\/p>\n\n\n\n
<\/div>\n\n\n\nIs there an alternative to Google Analytics?<\/strong><\/h2>\n\n\n\n
<\/div>\n\n\n\nAs we mentioned at the start of this blog, GA is a core part of the marketing toolkit for most. So, considering a move away from what is familiar can seem daunting to say the least.<\/p>\n\n\n\n
But there is a better way to do attribution.<\/p>\n\n\n\n
We\u2019ve spent almost a decade researching attribution methods and have developed a patented approach using AI to remove the need for cookies to stitch together complete user journeys.<\/p>\n\n\n\n
Corvidae<\/a> creates user paths that are on average twice as long as Google Analytics<\/strong> and operates at over 95% accuracy compared to just 20% for cookie-based methods<\/strong> (used by attribution tools<\/a> like GA and Adobe).<\/p>\n\n\n\n
- How does Data Driven Attribution work?<\/a><\/strong><\/li>\n\n\n\n