Head of Digital Strategy
Analytics and Data Science
Google Signals launched in 2018 as a new way to collect data on individuals who are both signed into one of their Google accounts and have turned on ad personalization for their account.
Tracking users this way allows Google to collect cross-device data and tie together cross-device interactions and how they tie into the customer journey.
Data from Google Signals is aggregated and there is no personally identifiable information, which makes Google Signals a privacy regulation friendly way to:
We are slowly losing our ability to track users across the web, thanks to a few factors: the user journey being disjointed across several devices, the loss or reduction of cookies across several browsers, and privacy regulations requiring explicit consent to be given for users to have their data tracked. We have covered this in more depth in one of our previous blogs.
When combined with Google Analytics 4, Google Signals allows advertisers to track certain users across different devices and understand how different channels and touchpoints play a part in the customer journey.
This is because Google Signals can be used as a Reporting Identity in GA4 when User ID is unavailable, allowing advertisers to still stitch together cross-device journeys.
There are three Reporting Identities available in GA4:
User ID is only available when a user is authenticated, or logged in to your site or app. While it is a great way to track users, not all users are authenticated and therefore are unable to be reported on using this method.
With browsers like Safari limiting the duration of cookies, reporting by Device ID can see returning users appear in your analytics as a new user, which makes using Device ID as a reporting identity an unreliable method.
Within GA4, you can select the Reporting Identity to choose either User ID, Google Signals, or Device ID, in that order, depending on what is available. GA4 will always choose the most reliable tracking method available for a user.
Collecting as much data on your audience where it is possible to do so is crucial as, with the demise of cookies, there will be less users which advertisers and marketers are able to collect data from. Using Google Signals to collect aggregated data where other methods are not available means Google can ‘fill in’ gaps of data using machine learning models on this collected behavior, so you can still understand how different channels play their part in conversions.
Google Signals are not automatically enabled in GA4, so to use this feature you will need to activate it within the property settings, under Admin > Data Settings > Data Collection. After 24 hours, Google Signals data will be available within your GA4 demographic reports. If you have multiple properties, you will need to activate Google Signals for each one.
Google Signals data will be available within certain GA4 reports, however at this time we are unable to download this data to be used elsewhere.
While Google Signals uses aggregated data to ensure there is no identifying personal data available to those collecting it, there are still privacy considerations to be aware of when collecting data using Google Signals.
When activating Google Signals, you enter an agreement with Google that states you have any necessary privacy disclosures in place so that users are aware of and have consented to their data being collected, stored, and advertised to.
Although it has its limitations, Google Signals is one of the best tools currently available to help advertisers preserve as much data as possible in a post-cookie world. If you need help with data preservation and advertising in the absence of cookies, get in touch – we can help.
This webinar is part of our wider B2B Playbook that is designed to help B2B businesses with all aspects of their digital marketing from leveraging data, acquiring more traffic, creating assets that resonate and succeeding internationally.
In the data section we’ve compiled useful insights on everything you’ll need to know, from capturing data to building advanced machine learning solutions using your first-party data.
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