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So What? Using Google Data Studio with Google Analytics 4 data

So What? Marketing Analytics and Insights Live

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In this week’s episode of So What? we focus on Google Data Studio with Google Analytics 4 data. We walk through how the metrics and dimensions differ and how to plan to transition your dashboards. Catch the replay here:

So What? Using Google Data Studio with Google Analytics 4 data

In this episode you’ll learn: 

  • how UA and GA4 metrics and dimensions differ
  • how to plan to transition your dashboards
  • best practices for your reporting

Upcoming Episodes:

  • TBD

Have a question or topic you’d like to see us cover? Reach out here:

AI-Generated Transcript:

Katie Robbert 0:25
Well, hey, Howdy everyone. Happy Thursday. Welcome to so at the marketing analytics and insights live show I am Katie, joined by Chris and John, who were the top two spots today. On today’s show, we’re talking about using Google Data Studio with Google Analytics 4. So the past couple of weeks, we’ve been talking about some of the tips and tricks that you need to know about using Google Analytics 4. And today, we wanted to focus on the reporting side of it. You know, a lot of people will be finding themselves in a position where they have existing reports that they need to transition over to Google Analytics, 4, and a lot of people will be creating that new report. And so we wanted to walk through some of that with you today as pretty much a demo of how to knowing that the dimensions and metrics in Google and Google Analytics 4 and Universal Analytics are not exactly one to one. So Chris, where do you want to start today?

Christopher Penn 1:26
Well, let’s see. Let’s take a look at a dashboard maybe and think about like, how do we how do we do this process? So here is one of the Trust Insights dashboards that we’ve gotten Google Data Studio, as folks may remember, Data Studio is the preferred reporting method in the Google Marketing Platform. So Google has made it pretty clear with their design intent, you’re supposed to do configuration in Tag Manager, you’re supposed to do actual analysis and Google Analytics, you’re supposed to do reporting in Data Studio. And so what we have here is some of our reporting. So Katie, should we just get started?

Katie Robbert 2:07
Yeah, let’s just get started. All

Christopher Penn 2:09
right, let’s, no, you’re not supposed to say that.

Katie Robbert 2:14
No, so I thought we were gonna So with everything, when you do an exercise like this, if you’re moving reports from system to system, it’s an opportunity to step back and redefine the user story and the business requirements. And so when I look at this, knowing personally, that this was built a couple of years ago, and the business has evolved in the dashboard hasn’t the first thing we want to do is restate the user story, the, as a person, I want to do a thing so that here’s the outcome. And then you can check it against your existing dashboards to say, Are these dashboards, meeting the needs that I have to get me to the outcome that I’m looking for? It doesn’t answer the question being asked. So that’s, that’s where you start?

Christopher Penn 3:03
Exactly. So there’s, there’s two things, the user stories, and whether your dashboards fulfill that intent, and then the access and the administrative stuff, who still needs access to this thing, it’s just like transitioning to a Google Analytics account. By the way, this mind map, we’re going to put this make this a PDF, I’m going to put it in the analytics for market a Slack group. So if you’d like to get this with helpful links that we’re gonna be talking about, it’ll be over in analytics for Marco. So, Katie, this is your dashboard of our KPIs as company now, does this for you still fulfill kind of what we’re we’re trying to figure out?

Katie Robbert 3:38
It doesn’t. Because last December, one of the things that we did internally was that we rearranged our goals in Google Analytics to follow more of our funnel, so the awareness, engagement, and conversion, and this report does not reflect that change. So that’s problem number one. You know, And problem number two is, you know, when I look at this, and I see that bucket of any Thank you, that’s too vague, because there’s too many things that fall into that. So that would be the second thing that I would change is I would break that out, more discreetly.

Christopher Penn 4:19
Alright, so the first step in the process for doing a migration is this. Make a copy of it, please do not edit your live dashboard, you will be sorry, you’ll be very resize. I’m gonna go ahead and make a copy of this.

Katie Robbert 4:35
I feel like that’s really good advice for a lot of things. Never added the live thing, make a copy of it, and then you can always replace it.

Christopher Penn 4:44
Exactly. And one of the other things that is super useful with Google Data Studio relatively new is under the file menu, go to publishing settings and turn on manual publish. What this does, is this effectively allows you to make changes to data to a Data Studio report now. But if the reports already in production, the users won’t see it, the changes you’re making until you go ahead and publish it. So make sure that you turn that on that way. Even if you don’t do this whole, like staging versus production environment, this is sort of a ad hoc, were creating a staging environment.

Katie Robbert 5:22
That’s so smart.

John Wall 5:23
Right? It’s great. They didn’t name that sandbox, that would make way too much sense.

Christopher Penn 5:28
You know, draft version, you know, the draft version makes sense? Because that’s, that’s the terminology used in Google Docs. Yeah. Okay, so the next step in the process, after you’ve cloned it, and you made a copy, one of the big things that Google has a tendency to do is to make changes to the data sources themselves. So the first place for you we go, is we go into the resource menu and look at our data sources. Right. So these are all of the data sources that are connected to this dashboard. This might be a bit much. So one of the things we would want to do, and we’re not going to do it live here, because nobody wants to watch us click around this is go through and figure out are these all needed? Or can we, you know, retire and remove some of these, because clearly, some of these might be a bit on the hefty side.

Katie Robbert 6:21
Yeah, and then it’s also, you know, in addition, it’s, it’s an opportunity to double check your governance. So one of the things that we covered on a different episode with all of these data sources is that we ourselves failed a bit in the proper naming conventions, as we were bringing these data sources in. So we’ve tried to clean some of it up, but we’re not clear about who these data sources belong to.

Christopher Penn 6:47
Exactly. So this one here, for example, all website data, this is actually somewhat problematic. So I’m going to change this to Trust Insights. Ga three account, that way. It’s clear when we’re working with GA three and GA four, which one is which. So let’s go hit done on that. The next thing to do, especially with Google Analytics, 4, anytime you’re adding a dashboard, it’s a good practice is to go into the your GA for property, hit, reconnect, and look for any changes in the number of fields, right, you can even hit the refresh Fields button. Every now and again, Google adds new stuff. And they don’t tell you, and it doesn’t update. So you have to go do that yourself. In order to make sure that you’ve got essentially the latest version.

Katie Robbert 7:45
So I’m assuming the, you know, updating this is not as easy as just switching the data source from GA three to GA four, like, I’m guessing that would probably break some things

Christopher Penn 7:59
that would break everything. Oh, good. And this is where things get really challenging is you’re gonna we’re gonna go through essentially piece by piece and figure out what needs to be changed. So here’s I guess what the challenging part. All the metrics that we see here from Google Analytics 3, because this is fundamentally Google Analytics 3 dashboard, do not necessarily have equivalents in GA for so part of our homework before we begin, is essentially to inventory. What data do we have here. So we have all goal completions, users, new users, returning users, bounce rate, session duration. Our next stop is to figure out, do these GA three fields have an equivalent? Now here’s the interesting part. If you search for Google’s documentation, comparing metrics, right to how you translate Universal Analytics to GA four, you get a moderately helpful guide to how Google looks at some of these data points, how they convert one interesting, very interesting thing, by the way, in the documentation here, this one here is going to throw people for a huge loop. Universal Analytics counts one conversion per session for each goal. Right. So if a user submits the form twice, only one conversion set, so it’s kind of as ethical, you need conversions. Che for says, and we count every conversion event, if a user submits the same form twice, you got two conversions out of one, which is a big difference, especially if you have a complex form where people can keep hitting that button to show what’s going on. So there are some pretty substantial changes. Here’s another fun one bounce rate is gone. Right? So if you think back to our dashboard, your bounce rate well, that metrics not going to make it instead we’re going to replay it have to replace it with engagement rate. Here’s the bone I have to pick with Google about this. This is a good introduction, especially for non technical folks. But if you are building a Data Studio dashboard, by definition, you’re pretty much doing software development, right? You may not be writing code, but you’re making a piece of software that end user is going to use when you go to search for what’s changed. This is not helpful from a depth perspective. And the documentation for what’s actually changed unhelpfully lives in Google’s developer portal. Right. So this is now line by line. This is what the field was named, and the old date API. This is what’s named in the new API. So there’s a field for example, called active users in GA for it is different than any of the old Universal Analytics fields. So there’s, it’s a new metric. But then there’s also as total users field. And so if you say you’re going which I use active users as total users, it’s not much one in the old in the old data, student, and reminder, Data Studio uses the Google Analytics API for all of its data. So the we have source medium in the old one. Now, it’s called Session source. Because there’s now also a first user source, which is essentially first hash. And there’s also a field that’s named source. But it’s actually conversion source, which is documented somewhere in here. Either way. This is not particularly intuitive. So if you as you’re building your you’re translating your your source, your reports and data studio from one to the other, you’ve got to know the translations that Google is set up, I’m behind the scenes,

Katie Robbert 11:51
will drop links to these pages in our analytics remarketing slack group. And if you’re not a member of that free slack group, you can join for free at trust For markers, I know for sure, I will be referencing those links those resources quite a bit because I’m already confused.

Christopher Penn 12:13
Believe me, much, it’s one of those things. This one is really important for E commerce because you’ll notice that not only are they’re totally different field names is actually built in different hierarchies. So instead of old category hierarchy, you now have a flattened items set of item categories for up to five categories. So if you’re doing an E commerce change, for trying to report on your E commerce site and Data Studio and GA for you have to have done the configuration correctly in Tag Manager, and g4 in order for E commerce to work properly. And not everything has the most intuitive names. So there’s that.

Katie Robbert 12:54
I think I’ve asked you this question before, Chris. But has Google made any kind of statement as to why they’ve made such a radical change the system and why they’ve made it essentially more difficult to use.

Christopher Penn 13:07
They have not made a statement, but the reason for it is because this is all Firebase. This is actual Google Firebase under the hood, Google Analytics 4 is nothing more than Google Firebase. So that when they migrated the Firebase database and basically called GA for, they kept all the Firebase stuff under the hood. And so all the field names, things are actually a reflection of Firebase is architecture, not Google Analytics.

Katie Robbert 13:34
And for those who don’t know, what is Google Firebase,

Christopher Penn 13:37
Google, Firebase is Google’s mobile app analytics software. So if you’re a mobile app developer, you would use Firebase in your app to tell you you know, what parts of the screen people are tapping on how fast your pages are loading and what people are swiping on in your app. And now with GA four, you see that heritage showing up so that when you have a data stream for your website, and you have a data stream for your mobile app, you can now sort of see one view of the customer in one place, but it really is just a reskin of Firebase. Got it? Okay.

John Wall 14:09
Yeah. And so this is like a 20 year upgrade, really, like mobile first. And, you know, finally jettisoning some text back from the days of gopher,

Christopher Penn 14:20
exactly. And to a blog post you put up recently, John on the Trust Insights website. It’s not an upgrade in the traditional sense. It’s not like when you uninstall Microsoft Word 2021. And you install Microsoft Word 2022. And you just you just go to work. It’s a new app. It’s it’s a new app, your data doesn’t move over. So the day you install GTA four is day one of your ability to look back and stuff. So certainly one of the things we would caution people to make sure you you do the transition sooner rather than later. So the next step, what we want to do here is we want to, we’re going to take this set of metrics Yeah, I’m gonna copy and paste them. And then one by one, we’re We’re going to change the data source. And I’m like you said, Katie, from our GA three to GA four. And so now goal completions, we’re going to look for conversions. So this is, so we have now changed. And you can notice here, there’s kind of a big difference. Yeah.

Katie Robbert 15:21
If you hadn’t just explained to me what the difference was, I would be like, something broke.

Christopher Penn 15:30
Exactly. And so if you see this, and you’re like, huh, that doesn’t look right. One of the first things to do is go into Tag Manager, and GA four, and see how did we define our conversions? Right? Do you have more conversions with different conversion setup in GA four than you do in GA three? In this case, I know we do. Because there’s we’ve set we set up some very different conversions that GA for, I want to say that more closely mirror those goals that we talked about back in December. So this is probably actually closer to correct. Next, let’s do users.

And as we just read about, we have active users, new users and total users, because we’re trying to do the GA three equivalent, we’re going to use totally users. If you have a mobile app, you’re going to you probably would use active users instead, because active users only triggers when somebody is has actually tapped open your mobile app and is actively using it as opposed to it just being installed and present and sending data back. Whereas we only have a website. So you can’t really load you can’t really have the Trust Insights app or website on your phone and not be looking at it. It’s kind of hard to do.

Katie Robbert 16:54
And so remind me now so we’re looking at about 4000, no 6000 users difference. And that’s because between universal nga for Google has defined these two metrics as different even though they’re meant to be equivalent.

Christopher Penn 17:13
Exactly. So you have things like total number of users, and they do kind of somewhat explain how those things differ. For example, Universal Analytics, his client ID, while GA uses user ID. And there’s a lot of other stuff that is questionable in here. So here’s one of the gotchas in GTA three, it, there’s a setting you can turn it on, that says exclude bots and spiders. It’s not in GA for so that 6000 user difference may very well be that.

Katie Robbert 17:56
So basically, this is going to create chaos. Okay, just just checking.

Christopher Penn 18:03
Sure is, let’s go ahead and do our new users.

Katie Robbert 18:09
Just a process question. So you copied all of those individual scorecards over and now you’re changing the data source, could you basically capture all of them at once and change the data source once even though? You have to go through and update the metrics like Okay, so you could that’s

Christopher Penn 18:31
okay, I probably get one by one just so we can talk them through

Katie Robbert 18:36
right I was just more so curious.

Christopher Penn 18:39
Yeah. So returning users this is going to be a tricky one to do because it doesn’t exist in GA for

Katie Robbert 18:51
well you know, I if I recall correctly, I create a filter for returning users in GTA three in the GTA three version of this dashboard it was

so it’s minus Yeah, that’s what I did.

Christopher Penn 19:20
I think I forgot to fielding properly

Katie Robbert 19:28
there so I so technically it doesn’t exist in Data Studio for GA three either. It was a calculation that I created.

Christopher Penn 19:37
Ah, okay. Now we should have our returning users metric

Katie Robbert 19:48
huh? Well, these numbers are just all over the place.

Christopher Penn 19:55
They really are. Bounce rate as we know doesn’t exist. Right? That’s engagement. Now, does it translate to engagement rate? Right? Which is interesting because it’s, it’s the inverse. If you think about if 90% of your traffic bounces, then 10% of your folks have stayed engaged with it. Whereas if 90% of your folks are engaged, only 10% have said, we’re out of here. And so this number is actually a reversal of the previous,

Katie Robbert 20:26
I was going to ask about that, because I was looking at before you started making the copy. It was like, it’s the red, it’s down. But isn’t that a good thing for this particular metric?

Christopher Penn 20:38
So read is is okay, for bounce rate, it’s bad for engagement rate. Okay. Got it, because you want more engagement, but fewer bounces? Google? Let’s do duration here. So we don’t have a session duration. At all, this is a question of, okay, how do we want to? How do we want to deal with the fact that this simply just does not exist?

Katie Robbert 21:09
Well, and this goes back to the user story, and we writing the business requirements. Is that a metric that we’re going to make a decision on now moving forward, versus when we put this dashboard together? Three or four years ago? You know, and so in this particular instance, the answer is, yes. Because one of the things that we internally are working on is the stickiness of our website. So right now, for those who don’t know, like, people come to our website, get what they need, and then leave and don’t come back a whole lot. And so that’s one of the things internally that we’re working on as one of our tactics. And so over time, this is a number that I would want to see and see it go up. But how we calculated in GA four, I have no idea.

Christopher Penn 21:59
Right? So one of the things that we would want to do is go to the actual documentation for not Data Studio, not GA four, but go to the documentation for the Data API, because remember, Data Studio uses the data API as its source of data and find is there anything in the metrics table here, that would be close to an equivalent, something that we could use? It would fit, you know, tell us, you know, can we tell us how much time is somebody spending on page, and just going through a quick browse through here, screen page procession, they’re not really user engagement duration, looks promising total amount of time seconds, you have to add rows to the foreground of users. So let’s see if that is available yet. That has not made it into datasheet view yet. So it’s in the Data API, but has not been included yet into in Data Studio. So that was gonna be one we’re gonna have to park that. You might even want to.

Katie Robbert 23:06
Yeah, that’s, and that’s going to be part of the frustration, as you know, users are trying to recreate these reports, that they may be having their full set of requirements in their user stories, the data just may not exist. And for us, you know, because it’s the three of us, that’s okay, we can get by without it. But that’s not true. For everybody, especially, you know, a marketing manager, who CMO is saying, but I need to know.

Christopher Penn 23:38
Exactly. And so one of the things that you may want to do in this in the process as you’re migrating a dashboard, is actually do what we’re doing right now, which is a live side by side so somebody can look at it go, what the heck is going on? Right? How do you have 21,000 returning users in GA three and 481? In GA for like, how does that even work? How do you have that many more new users and total users just from the exact same tags on the exact same website, having almost double the number of users? How does that work? And that’s where, again, a lot of these resources provided by Google it to not to marketers, but to developers will be helpful.

Katie Robbert 24:29
Yeah, how can you have more returning users than total users?

Christopher Penn 24:38
Well, that’s a GA three, one. So we go back to B one, that’s a GA three one. So we’d have to go back and look and see how was that constructed? It looks like it’s actually based on page views. Okay, so there’s that there’s also scoping issue there. So that was Try to doing some of these conversions. Now, here’s where things are probably going to get a little bit ugly. Let’s go ahead and

John Wall 25:08
because that hasn’t yet,

Christopher Penn 25:12
it’s not been bad yet. I’ve used it as basic top level metrics. So let’s go ahead and clone our, our goal completions by source here. And let’s go oh, now, one of the nice thing is, is we’re starting to see, like all the different conversions that we’ve got set up, like no, that’s, that’s GMP, so I need to switch this to J four.

So let’s put this to conversions instead of us. So these are conversions, but these are all conversions. So we have to, if you want to be able to specify a conversion event, like say, I only want to see certain conversions, you will now have to instead of having the different goals available, as as you saw in GA three, we have to filter it, let’s call this any Thank you. And then identify the event by its name equal to leave, that’s what we named it.

Katie Robbert 26:31
Well, that goes back to the governance of everything and making sure that you’re naming everything correctly, and in such a way that you can easily find it.

Christopher Penn 26:42
Right? I appear to have not done that.

Katie Robbert 26:48
Let’s take a look at newsletter instead.

Christopher Penn 26:52
I’m actually gonna pull open my GA for property.

Katie Robbert 26:57
Also not a bad thing to have open when you’re building these new reports.

Christopher Penn 27:01
So any Thank you has underscores in it? Thank you

that’s still not right. Because I filtered on the oh, you know, it’s, it’s it’s the event? I’ll bet you it’s the event.

Katie Robbert 27:30
John, do you have any data studio dashboards built for the marketing over coffee site?

John Wall 27:37
I think we have one just, you know, just kind of generic users. But no, it’s not like that’s at the top of our monthly reporting list. We’re definitely a lot more get other than that.

Katie Robbert 27:49
I would be you know what, I think it would be interesting if we were to go through this exercise with your report, even if it is a simple report so that, you know, we can then you know, speak to those audience members, those prospects and clients who are going to run into the same like, well, I just have a simple report here, you still have to follow the steps. And so that way, we can have even more practice doing that, because what we want to do is transfer all of the Trust Insights reports, which will take some time, but if we can demonstrate on the set of marketing over coffee reports, which sounds like it’s a smaller book, then we’ll have that complete. Here’s what it was, here’s what it is. And here’s what the process looks like.

John Wall 28:35
Yeah, and it’s great just to get that feel for as you know, there’s no substitute for this process right here of flipping the switches and saying, oh my god, this is totally broken. What? What went wrong?

Christopher Penn 28:46
Yeah, and there’s, there’s a lot of things appear to have gone wrong here. I’m going to actually figure it solved, forgot, no, I’m serious. The thing that we’re looking at here is I’m gonna just take these out first for space is I can see within the data itself, I’ve got my sources and mediums and I’ve got my different events, right. But for some reason, I’m not able to execute a filter on that thank you event, even though it’s that’s what’s properly named. So this is one where it’s like, Okay, I’m going to spend some time beating on this, to figure out why that particular filtration mechanism doesn’t work. But that this is a, like I said, a big change from GA three where each goal was broken out separately. And you could easily say I want to see goal three completion, I will see goal five completions and stuff now, we’re going to have to do it by the conversion by filtering for each of the individual conversion types that are in g4. So it’s one of the things like it’s an it’s more flexible, and it is more I guess, customizable, but it also has the capacity as we’re seeing right now to go totally off the rails because the If you don’t have proper governance, you’re gonna be sitting there. Like, oh, what did I do wrong here?

John Wall 30:05
Yeah, it does look like they are case sensitive. That’s one thing. I see two of them up there. Well, and

Katie Robbert 30:10
our reports are not that complicated. And so if you think about, you know, businesses where each goal is tied to a different business line and has a different set of stakeholders, and has very specific definitions, you know, it’s going to get that much more complicated, where ours is not that complicated. And we have 100% control over every single data input. And John, it looks like you cracked the code.

John Wall 30:37
I get the bug stamper of the day. How about that?

Christopher Penn 30:43
So now we’re looking at the source, the sources for the this is the Annie, thank you conversion on the website, where are we getting those conversions from? Now, the nice thing is, we could then, you know, split this out for other conversion events, like is this the first visit, you know, which is the equivalent of a new user? And so on and so forth?

Katie Robbert 31:16
Interesting, we do have a question, Chris. Have you had any issues clustering date charts by year and month? I have, I have had very odd totals that do not match scorecard. Chart counts, I’m assuming between the two different systems?

Christopher Penn 31:35
No, this is that’s actually a Data Studio thing in itself. Data Studio has some funky date handling. And so you’ll notice like there’s Day date, day weekend, a variety of other date fields in there. And I’ve run into issues where the totals don’t match up. Now, there’s two reasons this? Well, there’s two big reasons for that. One is the using the wrong variable for the for data aggregation, which can get you into trouble because we’ll have essentially the data glued together incorrectly. The other problem depends on your, your GA three or GA four account and how much traffic you have, you have one of the challenges. Let me see if I will trigger on this one didn’t trigger on this dashboard. And one of the challenges with Google Analytics, both the free and the paid version is the more data you request, the more likely you’re going to hit a sampling limit and the sampling limit, essentially, as Google statistically sampling your data, and you will then be at a point where you may have two shards measuring the same thing, but in slightly different ways. And you’ll get different numbers for what seems like it should be the same thing. We just ran into this with one of our clients. They had a we had a month over month chart of their users. And they had a chart looking at one month of their users. And the two had different numbers. And it was off by about 2%. The turns out the month of a month chart was sampling data at a rate of 3.5%. The individual month was sampling at 46%. So there’s a huge gap between the two in terms of what how much data was was being returned, which is kind of a pain in the butt.

John Wall 33:26
If you had it hosted over on data BigQuery would that make that go away? Because you’ve got all your own data?

Christopher Penn 33:33
Yeah. So if you were to put all that into BigQuery, and use the BigQuery integration with GA four, and then you’re building your reports in GA into the industry with your BigQuery data, then yes, you’ll get you’ll avoid the sampling issue. Or the other option is if if you really care about it, you can pay the quarter million dollars or whatever year for Google Analytics 3 60.

Katie Robbert 33:56
Is Google Analytics 3 60? Does it include Google Analytics 4.

Christopher Penn 34:02
So this is separate version? So there’s essentially Google Analytics 4 360?

Katie Robbert 34:09
Yeah. Oh, my goodness. Well, you know, I, I feel like, you know, we can’t solve all the problems of Google not having everything, you know, available, everything being a one to one. However, you know, one of the things that we can be doing now is auditing all of our reports, going back through the user stories, making sure that we have all the requirements because, again, sort of as we stated at the top of this live stream, our goals and KPIs have changed in the three or four years ago when we built these reports initially, so it’s an opportunity for us to go back through and so some reports may stay unchanged because we want to keep the same metrics, but you know, some of these reports, we’re gonna say, You know what, that metric isn’t important anymore, or it’s missing the following three things in order for us to have the complete story. So this is the time as Google is adding more things continually to be doing that documentation. So that you know, come, q3, and q4, that’s when you’re rebuilding the report. So use this time now, to do all of your user story and documentation requirements.

Christopher Penn 35:25
Yep. And remember, so like, I just realized, I use the wrong field for source, I use the conversion source instead of session source. So you’re going to want to have really, really good QA. As we said, the bottom part of the process is, you’re going to want to have QA with the user stories, and a technical QA. It’s again, like real software development, Hey, did you use the right fields? On this report? You know, and someone else checking your work? Say, Yes, this is the correct field was not the correct field. That step? I guess it was never really optional. But it was, there was so many people who are so well versed in GA three that you could spot things pretty easily. With a not a lot of folks skilled up yet, on GA four, you really are going to want to have more than one set of eyes on this stuff.

Katie Robbert 36:20
And it goes back to having those detailed business requirements. Because let’s say Chris, you built a report and you asked me to QA it, the first thing I’m going to ask you is what the heck am I looking for? How do I know if something isn’t correct, because as you just stated, not a lot of us are skilled up on GA four yet. So we are going to be dependent on those business requirements in order to do proper QA.

Christopher Penn 36:46
Exactly. And the last thing I would advise is, if you go into File report settings, there’s an option for your to put a Google Analytics account in here. I would strongly advise for anybody who’s doing work with Data Studio, that you have a Google Analytics accounts just for Data Studio reports, so that you can see which reports are getting viewed. So this workbook here has 19 pages in it. And when you pull up a measurement account, you can see which of these 19 reports gets viewed the most. And over time, I would actually do this sooner rather than later. So that when it comes time to do your migration, if you’ve got a report that’s gotten zero views for the last three months, you probably don’t need to migrate that one. If I say, you know, we’re just gonna let this one just fade away.

Katie Robbert 37:34
Well, and that comes with part of the requirements is how often do people need to look at this report. So there are some reports that I look at maybe about twice a year. And so that three month timeline for it in that context wouldn’t work. But if we had stated upfront, this report gets looked at twice a year, this report gets looked at annually, this one gets, you know, build that into your requirements. So then when you’re comparing that against that Google Analytics view report, then you can see, okay, this, that checks out, that makes sense.

Christopher Penn 38:05
Yep. So what have you learned today, kids?

John Wall 38:10
Oh, go break all the things.

Katie Robbert 38:13
Not a lot. You know, what we learned is, you know, there is a way to approach transferring your reports from Universal Analytics to Google Analytics 4, we’re going to post some of those resources in our free slack group. We also learned that Google Analytics 4 is going to be the system of record. However, Google hasn’t released everything that you need yet. So spend this time doing your documentation on what you will need, so that when things are released, you can add it in. And if you want help with that, then you know, that’s what we do.

Christopher Penn 38:52
Exactly. We have one last question asking if we have a preference for user for secession totals? And the answer is it depends. It depends on what you’re trying to do. So sessions are a good measurement for assessing marketing effectiveness. How did you generate that session? User totals are good for measuring the number of people that data thing. So if you, for example, are looking to try and compare what’s coming through Google Analytics with what shows up in your marketing automation system, your marketing automation system is based probably on contacts, which are people so use users as the comparable metric and that situation. On the other hand, if you’re trying to figure out doing multi touch attribution, you’ll say, Okay, what marketing channels are working best. You’re and you care about getting eyeballs on your content or form films or whatever. Sessions is the better choice in that case, but it’s always a question of, what is the goal, right? What is the user story? What is the thing you’re trying to make a decision on? And then what metric maps most closely to bad?

Katie Robbert 39:53
John, parting words.

John Wall 39:56
Do this sooner than later, man, you don’t want to be doing this on May 2023. That would be very painful.

Christopher Penn 40:05
Alright folks, we’ll see you all next week. Take care. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at trust AI podcast, and a weekly email newsletter at trust Got questions about what you saw in today’s episode? Join our free analytics for markers slack group at trust for marketers See you next time.

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