What is GA4?
GA4 is the new generation of Google Analytics, an analytics service that enables businesses to measure traffic and engagement in a variety of ways across their websites and apps.
Originally released in October 2020 alongside UA, it is now the default experience for users and is Google’s answer to a more modern and flexible platform better suited to the needs of businesses in the modern world.
Where in the past, any new releases were an “upgrade” from the one before, that doesn’t apply in this case.
GA4 is effectively a brand-new product and is a fundamental rebuild in terms of what Google Analytics has been in the past bringing a brand new experience to us, the user.
How does it differ from UA?
Universal Analytics was first released in 2012 and was built on session-based analytics and designed only with desktop engagement in mind. Although this worked at the time, with the rise of mobile and apps, it quickly became unfit for purpose.
More recently, with Google’s move away from cookies (their key tracking method on UA), they had to rethink how they tracked engagement.
Because of the above, GA4 is the next generation of marketing, user-centric driven analytics tool and is built to be futureproof and keep up with the modern world.
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Want to know more about the key differences? Here they are.
One of the biggest differences between GA4 and UA is the way they track data.
Universal Analytics previously tracked interactions as “hits”. A hit was counted as a single interaction between a user and your website or app.
There were four hit types included in this; page views, events, transactions, and social interactions.
However, GA4 classes each interaction as an event. As part of the increased flexibility of the platform, events can be customised to track any type of user interaction on your website or app.
This means you can track events such as; viewing a video, making a purchase, clicking on a link, and submitting a form.
We’ve found that event-based tracking is better than “hits” as it allows you to get a complete picture of how users are interacting with the website or app. Because of this, it means you can make improvements to your website or app and create a more effective marketing strategy.
Sessions are another area that has changed in Google Analytics 4.
A session in UA used to expire if no new activity is tracked within 30 minutes of the last “hit”. However, in GA4, the session duration is calculated as the time elapsed between the first and last event.
Sessions in GA4 can start without a page view: Unlike UA, Google Analytics 4 can start a session without a pageview. Instead, it starts a session with a user engagement as GA4 is based on events and not hits.
You’ll remember that Universal Analytics stored your data forever. Google Analytics 4’s data expires after 14 months.
Unlike UA where you were able to choose your data retention timeframe, with GA4 you can only choose 2 months or 14 months.
With the launch of GA4, year-on-year comparison will still be available, accessing historical data is no longer possible.
One of the challenges marketing teams and businesses may face is comparing a current GA4 period of data with historical data to measure growth success.
No bounce rate metric
With Google Analytics 4 comes new metrics and it won’t take you long to notice that the historic bounce rate is no longer included in reports.
Bounce rate in UA used to tell you the number of users that left your website without interacting such as clicking a link, signing up for a newsletter, or making a purchase.
Instead, GA4 now shows a metric called “Engaged sessions per user” and shows how many users actually engaged with your website or app. Things that count as engagement include a user who stays on a page longer than 10 seconds, triggers 1 or more events, or views 2 pages or more.
Comparing report data from UA and GA4
If you didn’t have GA4 set up and pulling data through by 1st July 2023, then you won’t have a true like-for-like report.
If you have no choice but to use data from Universal Analytics from the previous year, include an explanation of why the data may not be directly correlatable.
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