Google Signals: new Google Analytics feature released

August 02, 2018 - Written by

As anticipated recently during the Google Marketing Live event the rollup of the long-awaited Google Signals feature has been released.

What’s Google Signals?

The key condition for the new feature to collect and return data is that users have turned on the Ads Personalization settings, allowing thus Google to use their info to personalize ads,

Google Signals is a new Google Analytics Cross Device Report, similar to the User-ID report.

In this case however Google Analytics tracks sessions and device type for all the users that signed into their Google accounts and converted. Once the feature is activated, Google Analytics is able to recognize the device from which the first click was made, other devices from which different clicks in the path of the customer journey are performed and at the end the device that lead to the conversion.

Let’s see an example: the user clicks a first ad on the smartphone, then clicks a second ad on a tablet and finally completes the conversion on a desktop (mobile > tablet> desktop).

You can now develop a holistic (aggregated) view of the cross-device interactions of the users and how they got to the final conversion. The feature is of huge help to enhance remarketing and reporting.

How to activate Google Signals?

In order to activate Google Signals you should notice a blue banner above your account name as you can see in the image below.

Once you click on “Get Started” you will land in a new page inside Google Analytics where you are asked if you want to activate Google Signals for all properties of the account, for that property or for specific properties.

By clicking on “Activate” you will be redirected to the “Data Collection” section of Google Analytics, where you can verify if the option “Data Collection for Google Signals” is checked.

What reports Google Signals provides?

At your left, under the label Audience, you willl find a new report named Cross Device. Let’s see what kind of report are included.

Please notice that, as it has been activated few days ago the graphs are from Google demo account.

Device Overlap

Similar to cross device report via User-ID view, through this new report you can get a better understanding of the overlap of the devices that users employed to engage with the site and convert.

Device Path

The report indicates the sequence of devices that users employed on the path to conversion.

For example, [Desktop] > [Mobile] indicates that users engaged with your content first from a Desktop device and then from a Mobile device.

A filter for the pathing is available. Through the filter you can see the whole path to conversion (all steps in path), before (steps before) and/or after (after conversion).

The screenshot below show real data.

The pathing helps to understand the user journey before, during and after a goal, event or page view are triggered.

The feature allows to evaluate the user behaviour along all the interactions with the site, for example the pathing devices before the Exit Intent event is fired.


The data in the report is based on the Last Non-Direct Click attribution model. The report shows the channels that lead to conversions following the usual Acquisition, Behavior, and Conversion metrics.

Acquisition Device

The report shows the key devices employed by users to engage with the site or convert. It’s of great interest as you can see the Revenue Volume broke down by acquisition device vs other devices, a kind of an attribution model.


At present as Google Signals is in Beta version:

  • You can’t apply segments to cross-device reports
  • Limit of 250k sessions (for the selected data range) before sampling occurs
  • Google Data Studio, Big Query, i Custom Report/Tables and User-ID views are not supported and therefore cannot use data collected by Google Signals

What such data are useful for?

  • Optimizing the customer journey depending on the device employed by users
  • Enhancing remarketing by targeting specific users/device clusters
  • Enhancing the relevance of the cross device ads

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