This video is your introduction to Google Analytics 4. In this video we’ll cover a brief introduction to GA4, the differences between GA4 and Universal analytics, and in separate videos in the future we’ll look at the GA4 standard reports interface, as well as custom reports, and exploration reports. Then we’ll have a look at the next steps and takeaways.

Before we get started perhaps it’ll be interesting to understand how we’ve got today, and the history of Google Analytics. So Google Analytic classic came out in 2008 and Google purchased a company called Urchin rebranded it Google Analytics. In 2013 we got Universal Analytics and in 2016 we got a mobile analytics in the form of Google analytics for Firebase. In 2019 we got Google Analytics web plus app which is basically an alpha of Google analytics 4, because Google Analytics 4 makes it possible to collate web and app data in one analytics profile. GA4 officially launched in 2020. it was very alpha at the time wasn’t as fully featured as it is now.

So what’s next? Well our countdown is on to July 1, 2023, and that’s when Universal analytics properties will no longer process data effectively what that means is before then you want to make sure you have GA4 set up. As far as we know six months after this date Universal Analytics will no longer be accessible. One question many people have is, why do I need to upgrade to GA4? Put simply, if you want to continue using Google Analytics as your analytics platform then you need to upgrade to GA4, because as stated earlier on July 1, 2023, your standard Universal analytics properties will no longer collect and process data. So it’s important to back up Universal Analytics data if you want to be able to compare historic data to the new data collected in GA4.  So that’s things like year-over-year reports, or multiple year reports.

The next thing to know is that ga4 is a completely new analytics platform and it is not backwards compatible with universal Analytics. That means you cannot export your data from Universal analytics and import it into GA4, GA4 is a fresh start. So the sooner you install GA4 and start collecting data there, the more useful it will become. So GA4 requires its own installation and customisation, it’s a brand new setup. Now it’s important to know that GA4 is a more powerful and robust analytics platform, it’s been built from the ground up with new functionality in mind that improves data collection, analysis, and privacy requirements. One of the most important new features is the ability to collect web and app data in one profile. A bit of a bonus GA4 makes previously paid features available to us for free, these include a BigQuery export and exploration reports. BigQuery is Google’s cloud data warehouse platform. Exploration reports were something previously only available to Google analytics 360 customers and they allow you to create custom reports based on your own unique requirements. Upgrading to GA4 presents a fresh start and a new opportunity to review our current tracking and reporting requirements, and make sure we have everything we need going forward.

So what’s new in ga4? Well we’ve touched on some of these, but let’s have a look. It’s an entirely new analytics platform and as we saw in the historic overview Universal analytics is around 10 years old now, so it is about time for an upgrade! GA4 is an events-based measurement model whereas Universal analytics was page view, I’ll go into that a bit deeper on the next slides. GA4 unifies web and mobile analytics, it improves cross-device and cross-platform tracking. GA4 has advanced user identification, it includes multiple methods to identify and de-duplicate unique users, and it includes some degree of cookieless tracking. So there are three ways now that ga4 can identify users, and I’ll touch on those in the following slides.

GA4 enables roll-up and sub properties, so one scenario for roll-up reporting may be a company that has country level websites and you would install analytics on each of those uniquely and then have an overarching property above those where all the data could be rolled up for the head office to review. Now sub properties are kind of the opposite of that and a scenario for sub properties maybe a website that has different business units and wants to filter out data for each of the business units into their own properties. So we may have a Communications type company and they may have very separate business units where one sells mobile phones and one sells internet data, and they may want to be able to report on that in one property, but then also have sub properties that different business units can access. So there might be one property for the mobile side of the business, and there may be one property for the internet side of the business, and they can each see their respective data, but in the top level property that can all be reviewed.

There is some machine learning and predictive capabilities in ga4 and that comes about in a number of ways. One of those is in predictive audiences where Google analytics can create audiences based on how it predicts groups of people to behave. For example, it may create an audience that is predicted to be a higher converting audience, or inversely it may create audiences that may be people that are more likely to churn. Additionally, some insights it’ll provide on some reports based on machine learning insights, and we can have a look at some examples of those now.

One thing that a lot of businesses may take advantage of is enhanced measurement, which is basically automatic tracking of common events think of things like PDF downloads, and now even form completions. Historically we may have needed the assistance of a web developer to help implement those as events or at least they needed to be implemented separately through Google Tag Manager. Now these common event types are able to be tracked automatically simply by turning on enhanced measurement.

There are enhanced privacy controls, there is country level privacy controls to manage things like GDPR and any other country level requirements, and there is easier data deletion.

Being a new analytics platform there are new dimensions, there are new metrics, for example we have engagement metrics such as engage sessions, engagement rate, and engagement time, and these are basically the inverse of what we would look at in terms of bounce rate previously.

I touched on previously that we have access to previously paid features now such automatic export for data backup to BigQuery, and we have our exploration reports which can be quite powerful. They include things like funnel reports, path reports, and even path reports that can start at an endpoint.

Let’s talk about data streams. In GA4 data streams are the way we get our data into Google analytics. In Universal analytics we basically had a tag and we would put that on a website and it would send hit-based metrics to Universal Analytics. In GA4 we have data streams and they are basically similar in the way that they are set up as tags on websites but in GA4 we can send web and mobile data streams into one profile. As you can see in this screenshot I have here we have an example of Google’s flooded app and website, so we can see here the Android app the IOS app, and the website data all flowing into one analytics profile.

A big change between Universal Analytics and GA4 is that Universal analytics was pretty much a hit-based measurement model where there were multiple different types of hits that were sent into Google analytics and processed and turned into reports whereas everything in GA4 is an event. It’s kind of important to get your head around that and we’ll do a deeper dive on that soon. So for example in Universal analytics there were different hit types we had page view hit types, we had event hit types, we had social, e-commerce, user timing etc. etc. In GA4 everything somebody does on a website is an event and those events are grouped into reports for analysis.

So let’s take a deeper dive on events and the event types in ga4. This is basically a flowchart of the events and the event needs you may have for your website, and you basically start from the top and work your way down. So let’s have a look at an example of that. The first layer of events in GA4 are automatically collected events, so as soon as you have GA4 implemented on your website it’s going to start collecting event types such as first visit, session start, user engagement.

Now the next level of events that GA4 can collect are those that are turned on when you enable enhanced measurements. So enhanced measurement is that feature I talked about previously where you simply turn it on and then Google analytics will start collecting more advanced event types that previously required additional work to implement. Some examples of enhanced measurements events are page views, scroll, so when somebody Scrolls to about 90% of the page, outbound link clicks, so those are links offer your website to external websites, site search, this is for collecting the search terms that people are actively searching for on your own website if you have a search bar, video engagement, people engaging with YouTube videos embedded on your own website, file downloads such as PDF downloads or any type of file, and form completion tracking.

The next layer down is recommended events. Basically what we mean when we say recommended events is that there’s an existing schema that Google recommends around what to name each of these events when you send them to Google Analytics. So, if you have an e-commerce website and you want to track when somebody views their cart there is a recommended event name that Google will suggest you use for that event setup.

Now let’s imagine an example where you’ve gone through these three layers and you still don’t see the event that you want to track maybe something very custom to your website. Then the fourth layer of GA4 event types are custom events. These are any events that do not appear in those previous three layers that you want to track in Google analytics. The naming convention is up to you. There is a little bit of work you need to do to enable these to start showing in reports in Google analytics.

Now when we talk about event tracking in GA4 a lot of people think about event tracking and Universal analytics now most people will recall that event tracking in Universal analytics had a very rigid structure where we could use category, action, label, and optionally a value. So we would have to shoehorn any type of event that we wanted to track in Google analytics Universal in this format category. For example a PDF download the category may be PDF, the action may be download, and the label may be the document title that you want to track. In Google analytics 4 there is no notion of category action and label, everything is an event and you can have up to 25 additional parameters tracked to each event. So in the case of a PDF download the event name is file download and the parameters can be anything you want. So the parameters in this example, a file name, file extension, file author, file category, and file version.

The next change when it comes to GA4 versus universal analytics is the way conversions are handled. Now anyone familiar with universal analytics will remember this multi-step scenario that you’d have to run through every time you wanted to set up a conversion. First you’d have to select what type of conversion it was. An example of a destination conversion would be the URL where the conversion takes place, duration would be time based, you could do Pages, or screens per session, or tie it back to an event. Then you’d have to correctly and accurately match up the category, action, and label that you’d previously defined to track that as a conversion. So, it was pretty complicated, there were quite a few steps involved. GA4 has simplified conversion tracking immensely. All you simply need to do is go to the events you want to turn into a conversion in gGA4 and toggle on ‘mark as conversion’. In this example you can see a contact form is a conversion, it’s as simple as that. Any event can be a conversion so you just need to identify which of these are the most important for you and turn them on as conversions.

So let’s have a look at some of the concepts we discussed in an actual Google analytics 4 profile. When you first log in you’ll be on the Home tab. Now let’s have a look at admin and have a look at data streams. Under the property column you’ll notice in GA4 there are only two columns, there’s no view columns like there are in Universal analytics.

Let’s have a look at data streams, it’s one of the first Concepts we explored and we can see here just like in that screenshot I provided earlier. We’ve got the Android app the IOS app and the web data all coming into the one profile. Now when we click into one of these data streams we’ll be able to see more details about the data stream, its name, its URL that it’s tracking, it’s Apple website the ID, and the measurement ID. Here is where you also find the enhanced measurement options that we discussed earlier in the video, so we can see that enhanced measurement is turned on for this profile, and the enhance measurement options is taking advantage of a page views, scrolls, outbound clicks, and file downloads.

Now let’s have a look at events. So if we go to the configure tab we can see the first option is events, and here we’ll see all the events our website is collecting. Here are the options to mark a specific event as conversions. There’s a separate tab that we can look at just the events that are marked as conversions, there they are.

If we come back to the Home tab we can see some examples of insights that are driven by data learning and AI. Here are two cards on the home screen that have been created by machine learning. This one’s around an increase in users from Google Ads specific ad group, and this one is an increase in conversions. In the audience tab on this account we can see examples of audiences that we can use that have been created from machine learning and AI. Our first one here is called Asia top spenders and based on the name of that I would assume it’s a cohort of top spending users from Asian countries. We can also see an audience for predicted 28 day top spenders, and likely seven day purchases. So those audiences have been created by Google using artificial intelligence and machine learning, we can use those for reporting and remarketing campaigns in Google Ads.

Key Takeaways

• GA4 is a completely new analytics platform, it is not backwards compatible with Universal Analytics, and requires its own installation and customisation
• GA4 is a more powerful and robust analytics platform with new functionality that improves data collection, analysis, and privacy requirements
• GA4 makes previously paid features available for free, such as Big Query export, and Exploration reports
• Upgrading presents a fresh start and an opportunity to review tracking and reporting requirements
• Out-of-the-box reporting is somewhat limited but we can customise or use Explorations, or Looker Studio reports to build better reports
• Many new features / enhancements: web + mobile reporting, Enhanced Measurement, data controls, user identification etc.

So that’s everything I wanted to cover in this first video. In the next I will go over the standard reports.

I hope you found this useful if you have any questions let me know, thanks!