Google Analytics 4 (GA4) – What’s New + GA4 vs UA – 2022 Guide

Google Analytics 4 (GA4)

Google Analytics 4 provides the means to track, analyze, and report on the visitors to your site and app—who they are and what they do. This post walks you through the first major revision in years to the de facto standard in analytics in years that includes tons of new features and powerful machine learning.

With over 15 years on the platform and thousands of enterprise client engagements, we cover how to best use this leeading analytics tool to better understand who your digital customers are, how they found you, and how they engage with your site or app once they get there. We cover  the platform’s out-of-the-box functionality, from account creation to reporting fundamentals.

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Why are there two versions Google Analytics?

We have a brand new version called Google Analytics 4. Or GA4 for short. But we still have the older version of GA that’s been out for awhile not called GA3, but rather Universal Analytics. So, shouldn’t we just move forward with the new upgraded version? Well, actually both are going to be around for quite a while. GA4 is solidly in beta and it’s under active development, changing quite a bit.

So Universal Analytics is actually the most complete stable and technically the production version. But, as I’ll dive into a bit more in detail in the next section there are some compelling reasons why you definitely want to give GA4 a look. We’re going to dive into the differences between the two and how to decide which one to use if you’re unsure. But for now, I just wanted to clear up why both of them exist and make sure you know that this post is all about the new Google Analytics 4.

Understanding your digital customers with Google Analytics

As the world has shifted towards online shopping, the way that we manage and optimize our business has to evolve as well. Google Analytics is a free tool from Google that’s going to let you install a little bit of JavaScript on your site and it’s going to unearth a treasure trove of information about your visitors.

It monitors each visit and it sends that data back to Google where it’s processed and put into graphs and other visualizations that allow you to see exactly who’s coming to your site and what they’re doing. I’ll show you how you can query this collection of customer knowledge to find patterns and insights that we might otherwise never notice.

In fact, we can use the power of Google’s machine learning to surface the type of insights that a computer can do far more effectively than a human ever could by sifting through millions of individual interactions. Join me at LinkedIn Learning so we can take advantage of this powerful and free tool that will transform the way you look at your site or app.

This post will walk you through the basic setup, the essential reports and get you well underway in a firm foundation Analytics so that you can know your digital customers.

Digital analytics and key concepts

So, what do we mean by “Digital Analytics?” Well, analytics is is all about measuring your business goals understanding your performance and finding ways to optimize and improve that performance. And the critical part here is the continuous improvement. After all, we’re not here to just look at pretty pie charts for the sake of fancy reports. We want to actually do something about it. So digital analytics is also a process.

We first perform the measurement, for we can’t manage what we can’t measure. And then we apply the analysis. We learn from what we see, and then we take action. Now, before we jump right into the Google Analytics Tool, it’s important that we first go over some key definitions. As I firmly believe that knowing the vocabulary is half the battle of learning any new subject. Understanding these concepts is going to give you a headstart in knowing how to manipulate and interpret the data available, not just in Google Analytics, but really any analytics tool.

So the first two concepts you’re going to be hearing about a lot in this post are metrics and dimensions. These two variables make up all of the data populated in the analytics reports. Now metrics are the quantitative numbers that are measuring data and counts, ratios, percentages and so on. Dimensions are the qualitative categories that describe the data in segments or breakouts. So for example, in this particular table, the dimensions are the countries that described the data segments and the metric is the count in this case, the number of visitors.

Now, when we think about digital analytics we’re measuring the interactions engagements between us and the consumer the person who’s visiting our site or a mobile application or whatever digital experience we’re providing in the most basic unit of that measurement in Google Analytics is the event. As marketers, we may think of events, as things you attend. A webinar or a product launch.

That’s not at all we’re talking about here. Here an event just refers to any one interaction. For example, if you click on a link, you scroll a page you zoom into a product, or even if you just load a page, those are all types of events. It can also be more complex interactions like completing an e-commerce transaction or submitting a form, or even when our site has an error and something goes wrong, called an exception.

All of those interactions can be sent back to Google as the events and the type of event is described as a parameter to that event. We’ll go into this a bit more detail later but since events are the backbone of how this all works I want to point out their structure. So each event has a name. And as you see here and we have a bunch of page view events and then a scroll event at the bottom. Inside of each of these page view events, we have several parameters.

These are going to provide more details about those events such as the page location, which has just the URL or the page title or even the screen resolution. All the way at the bottom for the scroll event, we have the time in milliseconds to get to that bottom scroll point. And lastly, we have the actual value that is going to be recorded for that event parameter. So for the page location parameters that are of the page view events, we have the URL.

For the value of the resolution parameter, at the part of the Patriot event, we have 1920 by 1200. Again, we’re going to see a bit more of this later. I just want to use the idea that almost all of the data sent back to Google Analytics is going to follow this event, parameter and value model. And we can group together all of the events in a given timeframe. And we’re going to call that collection a session. Here in GA session is the collection of interactions that are taking place between when a visitor enters your site or your app and when the exit. And we used to call this a website visit.

But now that we can include apps as well a session is really the more accurate term. We define an exit by when you let your browser or app go dormant for 30 minutes or if you exit out entirely. However if a user leaves your site, but they keep browsing and then they return back to your site within 30 minutes, they’re actually still counted as part of that original session because it hasn’t been 30 minutes and they haven’t closed the process. Next, we have this idea of the user. Now these are the people who are visiting your site. In Google analytics, each event and session is going to be associated back to a user.

Google has three ways that they’re going to identify a single user. Either the cookies on the browser if they have really nothing else to go on the next best up as if you’ve logged into their service. So via Gmail, Android, Google Docs some way that Google can identify you. Lastly and probably most specific is that we can provide a specific ID to Google. So usually from a login on our own site, if we have one. Now ideally this allows us to be able to track a single user with those multiple sessions. Even if those are on different devices like a phone, a laptop, and a tablet.

Now, a group of users who all share a similar dimension or fall under a similar category can make up a segment. Segments can be divided by things like physical location the type of device that’s being used the traffic source users coming from, basically any way we can differentiate those users, we can create a segment for that. Now this is a really important concept to understand.

I mentioned before, about how analytics is all about measuring your business objectives. When a user completes an action that you wanted them to take any action you want to define as a success. We’re going to call that a conversion. We define these conversions through goals and analytics so that we can identify and record when this conversion takes place.

Now it could be completing a purchase, but also just clicking on a particular link or downloading a document or completing this very specific form I want to track or even just spending a certain amount of time on the site. You get to decide what you were calling it conversion. And we’re going to use these conversions to measure which of the users have successful visits. Then analyze how they got to the site, what content they saw and other things that can help us better understand our converting customers.

Now, speaking of tracking the source of that traffic the two most important things we track there are called the source and the medium. The source is where the user traffic originated from. And the media is the avenue that the traffic took to get from the source to your site. For example, let’s say a user was scrolling through Twitter and clicked on a post that linked to your site. The source of the traffic will be Twitter. That’s the who, and the medium they took was the social. That’s the, how. We want to see which sources and mediums are sending us the best and most converting traffic.

In the overall process of assigning that credit sources, when this conversions happen is called attribution as we are attributing those conversions to those sources. Now often that customer journey from start to conversion can involve several sources and several sessions and various content pieces on your site. And proper attribution analysis will tell us which of those touch points along the way was having the greatest influence on a successful outcome.

This is a really important topic to consider. And although we won’t be going into a ton of detail here in this post, we do have another post that is specifically about attribution and media mix model. And it covers it in full detail. And we went over these terms a bit quickly, but don’t worry. We’ll discuss them all in more detail. And you can always refer back to this video for reference.

GA 4 vs UA

What’s new in GA4?

Google Analytics 4 is the new, completely from the ground up rewrite of the world’s most popular digital analytics package. Years in the making, there is a completely new data model, a new interface, and a ton of new functionality. So one question is the name: was there a Google Analytics 3? Well, originally there was Urchin. Urchin was the company that Google bought, that was urchin.js, and believe it or not, that still works if you send that data back to Google.

They came out with Google Analytics, and this ran off of a JavaScript called ga.js, you may remember. Then we launched Universal Analytics, which was running on analytics.js, and then later this GTAG.js. And now we have Google Analytics 4, which also runs on the GTAG.js. So this is the fourth iteration, and it brings a lot of changes and upgrades. Number one, it is completely event driven. It is a total from the bottom up rewrite of the data model, and it’s more based on Firebase, but everything is event driven now, which provides a lot of advantages that we’ll go through. It also comes with automatic tracking, something called enhanced measurement, and this is really handy.

It automates a whole bunch of things that you used to have to do by hand that were quite difficult. So things like scrolling down a page is now automatically tracked, outbound clicks to other sites, a site search where people search for things on your site itself, video engagements, video starts, video progress, video complete, file downloads, when people are downloading PDFs and so on, that’s all automatically tracked.

We also have cross-device tracking. It uses Google Signals to understand when someone is using a mobile device and, say, a laptop or a computer, and that that is a single person. So it no longer breaks that and treats it as two different people. So not only is it more accurate, but you get the insights that you understand when people are using different devices. They’ve recalculated the way that sessions are done, and no longer breaks a session as it goes past midnight, the things that would make it look like a person had two different sessions when they actually only have one.

They’ve vastly improved the funnels and the pathing and we’ll go through all of that, but it is much more extensible and intuitive and better represents what’s happening on your site. The ability to do time-based analysis to understand how long it takes between events that are happening. There’s also additional more flexible conversion goals that can be set up to more accurately track what’s happening on your site or app.

And advanced users can export all the data to the cloud via BigQuery, something that was previously only available in the enterprise version. And we won’t get too far into that in this beginner’s post, but it’s worth saying that that is a pretty cool upgrade. So I’m really excited to show you all this new functionality. Let’s dive in.

What features are missing from GA4?

So hopefully by now you’ll agree there are some pretty great updates in this new version of Google Analytics. But there is a catch. We’ve said that it’s under active development and essentially still in beta. And since I’m not here to sell you on the product, but rather make you the most informed user you can be, I want to specifically address areas where the features might be missing. And we can think about feature parity compared to the previous version a couple of different ways. In many cases, you can accomplish the same outcome, just in a different way. And if that’s the case, they may never develop the feature, because you can answer the same question using the new UI or data model.

Now, in other cases, they just haven’t gotten there yet. And while I can’t share the full roadmap, trust me when I say it is pretty exhaustive and there’s some pretty great features and integrations on the way. Many of them are either things that weren’t possible to do before or only possible in the paid Enterprise version and will now be free. So while new features are added every week, at the time of this recording, here are a few things that you may find missing or incomplete. And if you have a specific question on a feature, if it’s ready or almost ready, feel free to reach out and I’ll see if I can shed any light. So the first thing is attribution. Attribution is getting a major overhaul and it’s not quite ready for release yet.

If your workflow is heavily using attribution, then you will need to rely on Universal for just a little bit longer. Same goes for multichannel funnels. It’s not quite complete. Things like referral exclusion lists are still in process. And there are certain site content reports, such as content grouping. Content grouping never really gained a whole lot of traction, and while I can’t say for sure, I would personally be surprised if it ever makes it into GA4. And there are some ways we can achieve similar analysis. So stream level user permission and filters. Views have been essentially replaced by data streams. And there are some ways that we can modify that stream, but it’s not quite the same, or in some cases, as easy as the old view filters were.

So things like the lowercase filter or the referral parameter exclusion, they aren’t there yet. And we’re holding out hope, because these were pretty great for data hygiene. The built-in tools around page load and site load times and performance analysis are also not yet included, as well as some of the integrations with other tools, so DV360, SA360, Campaign Manager, AdSense, Google Ad Manager, Optimize, Google Search Console.

The rest of the GMP suite is still being built out. We did just get the Data Studio Connector, which is great. So the rest are coming and they will be more capable and more accessible in the free version, so this should be a pretty big upgrade. The automatic rollup properties also not yet available, although you can accomplish the same thing manually just by utilizing a single tag. And lastly, the more detailed e-commerce specific features, such as product level dimensions and prebuilt funnels aren’t quite in GA4 yet.

So for this reason, we generally advise that GA4 is not 100% ready to replace a fully built-out Universal Analytics implementation, especially if you’re a heavy user of any of these features we listed above. So what should you do? Just stick with Universal?

Which version should I choose?

So which version should I choose? Well, I would argue that’s actually not necessarily the right question. Yes, there are going to pros and cons to both, and one might fit a particular situation better, as we’ve talked about. But you see, this isn’t like a standard upgrade path where you move your data over to the new version and you start using that one instead.

There is no way to port over previous data. You are starting from scratch with GA4, whether this is your first day with an account or whether you’ve been running Google Analytics for years. And of post, an account that has no data in it isn’t very useful for analysis. So you must start building up data in your account. And the best time to start was the day the beta launched a long time ago. The second best time is today. It’s also worth mentioning, there’s no forced migration here. They won’t be shutting down the legacy universal accounts any time soon.

And even if you are running the paid version of GA, you’ll never be charged twice for putting data in both, because they want you to start using the beta. So you might as well take advantage of that and run them both simultaneously. You may be collecting data in your existing account, and starting today, you add GA4, and even if you don’t touch it, you just let it run. Meanwhile, Google’s going to be adding features, and at some point, it’s going to be the more capable tool. Or maybe they just start forcing people over.

Either way, at that time, you’ll have plenty of historical data for your analysis and you can cut over. The only exception here, I would say, is if you’re starting 100% from scratch and you aren’t worried about any of the missing features we’ve talked about previously, then you probably want to just stick only with GA4. Don’t even both with the legacy universal analytics. There’s a good argument to keep it simple and clean if you’re starting from scratch.

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