Is Adobe Cookieless?

Is Adobe cookieless?

As marketers look to prepare for the impending death of third-party cookies we have been exploring some of the key issues around including looking at how existing and new proposed cookieless solutions are positioned for the change.

Here, we look specifically at Adobe Analytics and consider:

What is Adobe Analytics?

Adobe Analytics is a web analytics and reporting tool that relies on cookies to track and analyze website traffic and customer behaviour.

Originally launched back in 2005 under the name ‘Omniture SiteCatalyst’, Adobe bought Omniture in 2009 and the product was formally renamed Adobe Analytics in 2012.

The tool can track metrics such as:

  • page views
  • unique visitors
  • bounce rates
  • conversion rates
  • revenue

The tool also provides a range of data visualisation and reporting options, including customisable dashboards and reports.

Adobe Analytics is also part of a wider Marketing Cloud portfolio of products.

What attribution model does Adobe Analytics use?

Effective attribution and unravelling the complexity around the impact of each and every touchpoint on the customer journey has become more important than ever for marketers – particularly as the prospect of a cookieless future looms.

Especially in a world where a buying journey can start with an offline media trigger, which moves the prospective buyer online to research their options.

Often on a range of devices from mobile to tablet and laptop – and with a range of media from paid social to ad re-targeting contributing to the final sale or conversion.

So, what attribution model does Adobe Analytics offer?

Here is quick run-down on the choice of attribution models available including where they might work well and possible limitations of each:


Last-Touch is a widely used attribution model which attributes 100% conversion credit to the touchpoint right before conversion

Where it works wellPossible limitations
Could be appropriate where you have a heavy focus on optimising conversion at the bottom end of the funnelIgnores the impact of important touchpoints before Last-Click on complex journeys
The more touchpoints that come before Last-Click the less accurate it gets


First-touch flips Last Click on its head with 100% credit going to the first interaction on the user journey

Where it works wellPossible limitations
Works well with a simple marketing journey specifically focusing on top of funnelAn oversimplification of the truth like Last-Click
Businesses scaling from a slow base might benefit from itMuch less appropriate where you have a lot going on in your marketing funnel and a more complex and extended buyer journey


Linear gives equal credit to every touchpoint seen leading to conversion

Where it works wellPossible limitations
May be useful for conversions with longer consideration cyclesNot as useful where you already understand the customer journey and have a more detailed need to dig into the data on individual channels
Or when looking to measure a campaign holistically and get a feel for the customer journeyRewards channels that appear in shorter conversion paths more than longer ones


With U-Shaped, the first and last interaction get 40% credit each and 20% shared evenly amongst touchpoints in-between

Where it works wellPossible limitations
Where you are looking to identify what drives channel acquisition or have heavy a focus on nurtureIf you have an extended buying cycle you can potentially lose the data view you need


Custom models allow you to apply custom weighting to first and last touchpoints and all points in-between

Where it works wellPossible limitations
Can give an understanding of the contribution of channels that are typically poorly served by the models aboveCan suffer from data quality challenges
Weighting can be subjective in nature which skews the attribution outcomes


Time-decay uses a half-life statistical calculation tied to the amount of time that passed between the initial touch point and the eventual conversion

Where it works wellPossible limitations
With a short sales cycle less than 90 days and if you are looking to identify what drives conversion in the lower funnelNot so appropriate for longer B2B customer journeys where it ignores the fact that customers can make important decisions earlier in the process

There are additional models available too like:

  • Same-Touch
  • J-shaped
  • Inverse-J
  • Participation
  • Algorithmic models

Is Adobe Analytics cookieless?

The simple answer to this is no, Adobe Analytics is not cookieless.

Like other commonly used analytics solutions it relies on cookies for attribution purposes.

And with the impending death of third-party cookies this raises a number of issues as follows:

Degradation in tracking and targeting

At the most basic level, the removal of cookies is going to have some fairly significant impact on the ability to track and target advertising activity using Adobe Analytics (and the broader MarTech stack).

How this actually affects individual users and brands is going to differ on your own specific set up (more detail on all of this on the Adobe site here) but here are some of the headlines:

  • Visitors on iOS or Safari that have not visited the site within 7 days will have journeys split and attributed against separate user IDs which has implications for effective user journey tracking
  • First-party cookie life span will shrink to 7 days
  • It won’t be possible to track conversions beyond 7 days on iOS browsers or on Safari for MacOS users if the user doesn’t revisit within the window
  • Decreased lookback windows for targeting and personalisation

Issues with underlying data quality

This second issue isn’t directly tied to the death of third-party cookies but more of a legacy issue. And is due to the fact that solutions like Adobe rely on a cookie/pixel-based approach to stitching together user journeys.

And this type of approach has actually done a pretty poor job of identifying and effectively attributing complex cross-device journeys.

This isn’t something that is limited to Adobe though, it is inherent in any solution that uses the Cookie-pixel approach.

Take the example on the right, which is for a real customer implementation for a leading UK retailer. In this case, we identified that up to 80% of the data being generated by Google Analytics (another solution which relies on cookies for attribution) was actually being incorrectly attributed.

And we were able to use the sophisticated AI and Machine Learning techniques in Corvidae to rebuild the data – and to provide a much more accurate view of the impact of advertising spend.

So, the impending death of third-party cookies has implications for Adobe Analytics users but there are also legacy issues that are impacting here too.

How is Adobe preparing for the cookieless future?

This is an in interesting one and the honest answer is that is difficult to fully assess this.

Much of the external communication from Adobe around the change talks in generic terms about broad issues like

  • the need to focus in on first-party data,
  • leveraging cookieless advertising opportunities like contextual advertising
  • and the need for more authentication in a world

What is a little harder to determine, is what they are doing with their own solutions to mitigate the impact of third-party cookies going away.

And while we have been focused on applying AI and Machine Learning techniques to improve the quality of the attribution process itself – and not only provide historical attribution analysis but highly proactive suggestions for how changes in media spend will impact revenue, like the example below – it appears that Adobe have been focused on using the same technology to drive alert and anomaly type reporting rather than hook it in to the attribution process itself.

Adobe Analytics Alternative

Introducing: Corvidae

In many ways we were ahead of the third-party cookie change – albeit in a very round-about way.

About 7 or 8 years ago, while we were working with clients on effective marketing attribution, two things became clear to us.

Firstly, the quality of underlying data from attribution tools like Adobe and GA was pretty poor due to limitations in the cookie/pixel approach outlined above.

And secondly, we had to find a way to rebuild that broken data to give our clients a more accurate view of the impact of spend on revenue.

Which is why we developed Corvidae, our cookieless attribution tool, which uses a patented approach that allows digital marketers to:

  • Break down siloed data right across your marketing channels
  • Gain an understanding of the complete customer journey
  • Refocus marketing spend in the most profitable areas
  • Achieve greater ROI from your marketing efforts

Interested to learn more? Download a copy of our eBook: Is Cookie-Free Attribution a Myth?

Is Cookie-Free Attribution a Myth?