You Ask, I Answer: Google Analytics 4 Long-Term Benefits?

In today's episode, Tanner asks, can you explain why Google Analytics 4 is supposedly going to be better in the long run? That's a really good question because obviously, with a lot of the transition issues and the fact that it's still a little rough around the edges, uh, it may not be able to easy to see what those benefits are. And certainly if you are uh not already on Google Analytics 4, uh there is no immediate need to make the switch. Um, there is, in my opinion, an immediate need to uh get it set up and get it collecting data, but once you've done the basic setup, you can just set it and forget it until you're ready and until the platform has matured. So there's three big things that Google Analytics 4 offers that are different and better than what you get in Google Analytics 3 or Universal Analytics.

The first, um, and an easy one is the improved cross-device tracking, especially if you have a mobile app. So if your company has a mobile app, um, having Google Analytics 4 allows you to unify your mobile app users with your uh web users, and that gives you a much bigger, better, cleaner picture of who your audience is. So that one's kind of a no-brainer if you have a mobile app. If you don't have a mobile app, you still do get better cross-device tracking and better raw data on the back end because Google Analytics 4, as we've talked about, fundamentally under the hood is actually Firebase Analytics. It's actually the Firebase database underneath there with the Google Analytics interface on top of it, the the GA4 interface on top.

So it's built for mobile first, which is a phrase you've heard a lot from Google in the last 10 years, right? Uh mobile first, mobile first web, mobile first indexing, mobile friendly, mobile usability. Clearly, and it's not a surprise, right? These things are everywhere. Um, it is a mobile first world, and so it makes sense for our analytics to reflect this particular strategy.

That brings us to point number two. Um the event model that Google Analytics 4 uses is the Firebase model, where every interaction somebody has swipe, tap, do this, do that is tracked as its own separate event. That's one of the reasons why out of the box it says, you know, do you want to turn on enhanced measurement? And it tracks all these extra things. Those are standard Firebase events.

And so the benefit of this is that it makes our data more granular. If you look in the Google Analytics BigQuery table that it will set up for you automatically, um, it is much easier to see every individual interaction that a user has. Now, this is a lot more of a uh tenuous benefit for right now to the average marketer. For the average database person, it's a huge benefit because in the previous um version of Google Analytics, you had four scopes, right? You had the hit, you had the session, you had the user, and you had the product.

And not all the data was compatible with each one, right? It was very, very challenging in some cases to get unified data out. If you wanted to know about users who had converted within a session, that was a real pain in the butt to get that. What the Firebase database looks like on the back end is uh the the technical term is denormalized, right? Instead of a unique user, you know, have a unique event and a whole bunch of uh uh in some ways duplication of the user data.

It makes for a very big flat spreadsheet, essentially, instead of having you know four. Actually, that's a really good way of explaining it. Imagine uh those four scopes in Google Analytics 3 are different four different tabs in a spreadsheet. It's kind of a pain in the butt to get data from one tab to the next. Google Analytics 4 denormalizes that, which is a fancy way of saying it just puts it all in one big sheet.

So you don't have to reference cells and other tabs and things like that. You can do it all in one table. This obviously has a major benefit for Google itself because a denormalized table is easier to process, it's faster to process. Um but it has benefit for us as marketers if we have the skills to work with that kind of data, because now all the fields, all the dimensions and metrics that we're used to that used to have these limitations, don't have those limitations anymore. We can query the database through either Google Analytics 4 or the back end database and pull out that the data that we want and aggregate it at the level that we want to view things at.

So you can uh roll everything up into a user, or you can break it down to a session or even intra-session data. Um that in turn gives us the ability to have much better path analysis. There was a substantial limitation in Google Analytics 3 uh for path tracking to conversions. It is is it is still not great. Uh it's a pain in the S to get to get that data out.

Because in the dimensions and metrics in in GA3, uh you had to reference a whole bunch of you know the the three steps before conversion and trying to aggregate this model together, which you can do. But now in GA4, this event model gives us the ability to track every single action somebody took on the way to a per a path to a purchase. So if you're using advanced attribution models suddenly as long as you can retrofit your code um your model is so much better because you don't you're not limited to a look back window of the last three or four interactions that somebody had you now can see if they've been on your website for an hour and a half clicking around you can see all 50, 60, 70, 80, 100 different events that happened before that conversion and build a much more robust conversion model. So that event model really gives us the granularity we need to do very substantial analysis. Is it easy no you gotta be really good at working with that data.

But can you work with it and turn it into valuable insights yes the third major thing in the long run is that big query integration up until now only uh Google Analytics premium users were able to get the back end raw data from Google Analytics. Now everybody has it. And again this is not something that a non-technical marketer is going to find a whole lot of benefit with because it requires a lot of expertise but for the technical marketer this is a a a huge benefit this is a massive benefit because you can now go in and get the raw data. You don't have to do things Google's way if you have a better way of doing it right if you are a skilled programmer in R or Python or Java or any of the languages that can talk to a BigQuery database you can write your own code to access the data to process the data and maybe even visualize the data in some other way. For a lot of the third party visualization tools like Alteryx and Tableau and stuff, they have big query connectors that are native.

And pulling data out of a big query database is way easier than pulling it out of the Google Analytics API. It's faster, it's more accurate, you run into fewer connection issues. So for the data-driven marketer, that big query integration is a massive benefit, and it will be better in the long run. What this means for most marketers, uh, at least those who have the budget to either build the technical capability themselves or hire it out, is that you'll have much more custom attribution models, you'll have much more uh custom audience models, and you'll have your special sauce, your unique way of of analyzing your data that other companies don't have, right? That technical proficiency will be part of your secret sauce that makes your company more successful.

If you have a better model, a better template, a better um algorithm for analyzing that data, you can use that to create competitive advantage. Whereas other companies that are stuck with just the stock tools in the interface, they'll do okay, right? But they won't be able to reap the full power and benefits of that data. Google is essentially giving you all the raw ingredients and saying, hey, some people are only going to be able to make pizza, right? And that's okay because pizza will feed you.

But if you can take this flour and yeast and all this stuff, you can make breads and muffins and pies and all these things that other people might not be able to. So that's where you're gonna see in the years to come, a big competitive difference is those companies that can leverage the data and those companies that can't. So those are the three major benefits. They're gonna take time to see the value. There's one more benefit, and that is for agencies specifically.

Again, if you develop a proficiency, if you develop a capability, if you develop the uh algorithms and the models and the software to leverage the data, that will be part of your secret sauce that you can bring to your clients. Um, that can be a major, major benefit. So, if you've got follow-up questions, it's a good topic. Got follow-up questions, leave them in the comments below. Subscribe to the YouTube channel and the newsletter.

I'll talk to you soon. Take care. Want help solving your company's data analytics and digital marketing problems? Visit TrustInsights.ai today and let us know how we can help you.