So What? Basic Attribution Modeling in Google Analytics 4

Well, hey, happy Thursday. I can't believe it's already Thursday already. So welcome to So What, the Marketing Analytics and Live Show. Today we are covering attribution modeling in Google Analytics 4. And so just to sort of set the stage, an attribution model, if you're not aware, is basically it's a calculation to help you understand what gets credit for all of the different things that you've done.

And we've covered the different kinds of models in other episodes, but at the end of the day, it's really, you know, what's working, what's not working, and this is in context of your digital marketing. Um what we do know about Google Analytics 4 is that it's very different from Google Analytics 3. And the big difference, and that's what we're going to cover today, is that that attribution model um project that you can set up in Google Analytics 3 does not exist in Google Analytics 4. And so what Chris has been working on is figuring out how to rebuild an attribution model using the data from Google Analytics 4. And he's built it in Data Studio.

And so we're actually going to cover that uh today. So Chris, John, any other comments before we sort of dive in and see what it looks like. No, let's go. So we're we'll look at attribution models in Google Analytics 4 in four different places. So um yeah, we can show what's different.

But to your point, Katie, I'm gonna go ahead here and switch us to uh this the screen. This is what you're used to seeing, right? You get good old Google Analytics 3, you've got things like your assisted conversions and stuff in here, your model comparison tool, and of course, that attribution uh beta model, which is there, it's nice, it's easy to use uh relatively. And when you move over to Google Analytics 4, it's not there, uh, which is a little bit painful. So the question is, where did it go?

Like Google could not have possibly built a piece of software that didn't have attribution in it, right? That would be weird. Well, and what we do know is that they're Google Analytics 4 is still technically sort of a beta project. And so what we are advising uh our clients and sort of anyone else who will listen to anything we have to say is to not abandon Google Analytics 3, but to set up Google Analytics 4 in parallel, because Google is slowly rolling out features. So for a while, we actually thought the channel groupings uh would not exist in Google Analytics 4.

And we actually just saw it was either yesterday or the day before, that they do exist now. They look a little different, and you can't edit them the same way that you could in three, but they're back. And so they might bring back attribution modeling, but we just don't know if and when that will happen. And we don't want to have that gap of not having that information, especially if you're making decisions with it around how to budget and how to resource your teams. So we want to make sure that as best we can, we can recreate some version of attribution modeling using Google Analytics for data.

That's right. So there is attribution modeling built in, but instead of it being its own discrete section, it's kind of hidden away. It's tucked away uh inside Google Analytics 4. So let's take a look at this. The first thing you need to have prerequisite is you need to have gone through and set up events.

So you would have done this in Google Tag Manager and you would have declared them here in Google Analytics. You create your events, and then you obviously mark those events that you think are important as conversions. Once those conversions exist, then you can start to put together at least very, very basic attributions. So I'm going to go into a conversions here. And these conversions, these are the ones we said, yep, that's a conversion.

I'm going to go with any thank you page because that's an easy one for us to do, right? So here we have uh conversions by event source. Uh what is your what happened? And I'm going to go to the little widged up here. And here is where attribution kind of got tucked away.

So this gives you four different methods for computing attribution in Google Analytics for for any of your goals. Now, this is kind of cool, right? It's kind of handy. It means that you can look at models on a per convert conversion event basis. The challenge is these conversions are very different in definition than what we're used to.

So you have uh the first one there, cross-channel last click, second is cross-channel last engagement, third is Google Ads preferred last click, and fourth is Google Ads preferred last engagement. And Katie, your facial expression says, where what's going on here, right? Well, that to me says that, you know, and you know, I don't know the deeper definitions, but they all look like basically a version of a last click attribution model, which is the version that we try to advise people against, unless they're using a very simplistic e-commerce website. Not only that, these are fire-based conversions. Because remember, Google Analytics for really is just Google Firebase.

So what are those definitions? Cross-channel last click says that GA4, aka Firebase, um, looks at all the clicks from all sources and attributes the conversion to the last click that was is not direct, right? So it is a non-direct last click. Cross-channel last engagement is Firebase under the hood, looks at the last click or add impression. Uh if there is no last click, excluding direct.

So it's either add impression or last click is that second one. The last two, Google ads preferred last click simply means we're gonna give credit to Google Ads for the last click, no matter what the last click happens. Yeah, exactly. Um, and again, for mobile ads, uh Google Ad Ads preferred last engagement says we're still going to give credit to Google Ads for the last interaction. So those are the four models built in here.

Um that you can see now you could be forgiven for saying this is kind of sort of not really helpful, right? It's not because essentially what you've been given is a last click model and a position-based model. A position-based model is where you determine what gets the most credit. And in this instance, Google has said, we do, we get the credit. So give it to us.

Exactly. So the account managers basically built this. Uh the setup. Exactly. Um, okay, so what can we do to get to get at this?

Maybe in a way it's a little bit more sensible. I'm going to go and actually bump this into the analysis hub, uh, which is the exploratory BI tool. And now you can see here we've got uh you know event source uh cross channel last click and then the number of conversions. I can add more columns here than just event source cross-channel last click. So I'm gonna do let's do a user's uh let's look at the different sources we have.

I have a session source uh and a user source. So these are the the different kinds of um attribution I could potentially plug in here. Remembering that there user session and event are different scopes. Um so the user source is that person was the last cli uh source that we know of from them for the last click. The session is just that particular session, and the event is literally that conversion action alone.

So you could add in user session or event to try and do some comparisons. Let's go ahead and add in user source here. I'm gonna drag user source in. And now I'm starting to be able to see, I can start comparing the user source, uh, the source by which the user was first acquired, and then the last click, right? The source which conversions attributed based on the very last click.

So I can see 41 users almost timely was both the the origin and the last thing they did. Some of these, however, like trust Insights. So our website was the first one, and the last was the was Google itself. Uh some of these almost timely as a first touch, and then Gmail, their their regular Gmail account was the last. Some like it was organic and then the email is the last.

So shouldn't those two columns be reversed? Sure, I can just drag that out. Oh okay. Because I'm like, wait. And it's one of those things like as you're putting it together, you want to make sure if you're giving this to someone else to look at that it's intuitive.

So Chris, you were reading it right to left, but we want it to be left to right. Yes, unless you're Japanese, Chinese or another language that we reads right to left. Well I I can't speak those languages unfortunately so I would be left to right. Exactly. So now we're starting to to get at least in the analysis hub this broad analysis.

But the the challenge is this is still kind of hard to read right um it's still not the most intuitive thing. But those definitions are really important. The user source in Google Analytics for is essentially the first touch. The event source of the session source is effectively the last touch. And so you could put the two together to get a sense of okay I can at least see where people are coming in first and then what was the last thing that pushed them over the edge but analysis hub you what you saw here this is not the place we want to send people this is not what you want to show the CEO right this is uh this this will not go well for you.

Um so what do we do? Data Studio that's that's the name of the show today. Exactly. So I've gone ahead and connected Google Analytics 4 here to uh our our uh Data Studio workbook and we're going to put in a new chart. Now let's just go a colored heat map.

And I want uh in this case, let's do our first touch, right? Our user source, and uh maybe our user medium. In fact, it might even be better to have medium, which is because remember, medium is effectively the default channel grouping in Google Analytics 4. And I want to see conversions, maybe views too, but conversions are kind of more important. So let's go ahead and make conversions first in the table.

I'm gonna make this a little bit wider here. And resize our columns, fit the data evenly, make it a little bit bigger. And so now I have a nice first touch table, right? This is the first touch for the conversions. And let's sort it by conversions, because again, we like conversions, they they make us happy.

If people are wondering why John and I are watching this kind of slack job, it's the first time we are also seeing this come together in this version. So we're both like, wow. It's like watching Bob Ross. He's like happy conversions. These are happy.

Chris is the Bob Ross of Data Science. Oh, so is it so that means we need to start changing colors here? Like, oh, it's a happy color. I mean, I think blue and teal are pretty happy. Yeah, they're they're pretty happy.

Okay, so let's label this too because uh as with any good uh thing, we want to have some kind of labeling. So first touch attribution model, that bold, crank up the size a little bit. So there's our first touch attribution model. That's that's good. Uh what we want to do now is I'm gonna clone that table.

I'm gonna rename. I'll do a last touch table. And now instead of user medium, remember what we were saying earlier. You have uh session medium is sort of the last touch. Let's go ahead and get rid of the user mediums, put in session medium, session source.

So I don't know if I'm getting ahead of you, Chris, but it looks are you restricted to being only able to sort of recreate the first and last touch models? Like, are you unable to recreate things like the linear and the time decay models? As of right now, that's correct. Um we can talk about that in a little bit as to what you can do as a workaround for that, but it's not pretty. Um so now we've built ourselves a last touch attribution model, the session.

Uh, this is the last known thing, and you can see there are a few differences uh along the way. For one thing, the numbers are slightly different. Let's kick it out of design mode into into a view mode here. Uh the numbers a little bit different in the first touch model. The this email newsletter gets 100 conversions, and the last touch model it's a hundred and seven, right?

So the there is a difference there. You see, there's differences on view counts as well. Uh and you see, like for number five, in the first touch model, uh, Trust Insights, the website itself gets uh credit for 15 conversions that same slot for a last touch model, a different website gets credit for it. All right, so there are differences between these two. Um, and so the question that you want to ask yourself as a marketer, the so what is which model do you have more control over?

Which model do you have um numbers that are you know maybe more concerning to look at? So, for example, when you look at this last touch model and compare the views to the conversions, we can see that there are some places where the numbers are pretty substantially off, right? So uh we see you know nine conversions here and 760 uh views of of this here, and then 717 and six conversions here. So the last touch model for that email channel, last touch seems to be the model that is more I would say a uh favored for an email newsletter. Makes sense.

I mean, it's the last touch you we tell people in our newsletter, hey, you should buy our data science one-on-one course for 299 from trustinsights.ai. Um, but Chris, but that's based on our current attribution model, which we do outside of Google Analytics. That's right. Um, and this is where this is where the challenges are. You have these two models, and today, which is April 22nd, 2021, this is all that there is in GA4, unless you have the ability to build your own attribution models from your raw data, uh, which is a slightly heavier lift.

So, Chris, one of the questions that we have is this what you're doing here doable in the free version of Data Studio? My understanding is that Data Studio is free to everybody, and so is Google Analytics. So Google Analytics 360 is the paid version. Umalytics three, universal, classic, however you consider it, is free to everyone. And I believe so is Google Analytics 4.

It's sort of it's now the add-on to Google Analytics 3. That's correct. And as far as I know, as of right now, there is no um premium version of GA4. And so everything that we've just done is all with all the free products. Makes sense.

And that's super helpful too, because as we're gonna see, you know, at least I would imagine for the foreseeable future, budgets are still gonna be pretty restrictive. And this kind of thing is not where people are going to be spending their money. If this is something that they have to pay for, they probably skip over it for now. Exactly right. So, John, you know, as you're talking with prospects and as you're, you know, just sort of chatting with the community, I'm guessing that this isn't even a question that's being brought up yet.

Like, can I do attribution modeling in Google Analytics 4? Are you still seeing that people are sort of still wrapping their head around attribution modeling in Google Analytics 3? Yeah, it's most people realize that if they want to do some attribution, that getting it all in Google Analytics is not going to be enough. You know, they're either gonna have to figure out some way to set up some additional goals to get Salesforce or Marketo or Eliqua or whatever to get that tied in there to get things working. And then I think a lot of people are just not even aware that GA4 is coming down the pipe and that it's going to be considerably different.

You know, so yeah, it very few people are talking about, you know, I don't hear it, have not had anybody ask me about attribution and GA4. Like that's completely beyond where they're going. And where it's at now too is again, people are usually coming to us because they want their attribution model to be uh a little more advanced and take more into account. And so going to them and saying, hey, you know, you're gonna get a first and last touch option, that's just uh that's a no-go right from the start. Makes sense.

All right. So Chris, so you said that there's other ways that we can be looking at our data in an attribution model. What does that look like? Uh you're not gonna like this answer. Um I mean, it's a day ending and why.

The way that we're going to get good multi-touch attribution models or any of the uh models is to use the raw data itself. So one of the features that is available in Google Analytics for, uh, and it does cost money, uh, not much, but it does cost money, uh, is the ability to send your Google Analytics data to a Google BigQuery database. Uh, BigQuery is their huge cloud SQL database where you know it lives in the sky uh and all your data goes there to die. Um not really. Well, it can go there to die if you send it there and never pull it back out again.

Exactly. And what you get inside of BigQuery is you get the ability to see what's happening uh at the individual hit level inside Google Analytics. So you get, for example, the event names, uh, the event timestamps and what page somebody was on, and then all of the attributes that go with each individual event. So I'm scrolling over here, you can see the operating system, uh, the language, the referrer, what country the person is from, uh, the region. There's our traffic source, our traffic medium, our stream ID.

If you want to know what if you're gathering from both mobile and non-mobile devices, um, and obviously, if you've configured any of the other fields that are available, you also get uh critically timestamps and the user uh the the anonymized user ID. Now to build an attribution model, what you can do is query the Google Big Query database and pull out this group by these individual IDs that exist in here and the actions that somebody took, like what page were they on, or what source or what medium did they come from. And with a sufficiently robust enough piece of code, reverse engineer an attribution model. And at that point, you would use you could use you know the traditional ones like uh linear or time decay if you want to write code to do that, or the more sophisticated ones like Shopway values or uh Markov chains. But the key is because Google Analytics 4 gives you the raw data, you have the ability to build these models yourself.

And I suspect, I don't know for sure because I don't know. Um I don't I don't get inside information from Google, none of us do. Um, I suspect that Google has made this available so that people can build an app ecosystem around Google Analytics 4, right? Uh around the around the Firebase ecosystem. There are, for example, plenty of apps in the app store that allow you to do more with your Firebase analytics, and all they're doing really is tapping into these databases.

Here's the catch. You've got to be able to code uh to do that. That's um there is no currently there is no way to build those models without dipping into the coding. So it's not something that you could say, okay. I found my data in BigQuery, I'm gonna bring it into an Excel spreadsheet or even Tableau and you know group things together.

No, because you can absolutely look at the raw data itself in a in a spreadsheet. I mean, this is effectively just what it is, it's a spreadsheet. But the method by which you build attribution models requires computations that you can't easily do in Excel. You can do you could do some of them, you know. For example, you could do first touch pretty easily.

You sort by your group ID and you see what was the first source, you know, by uh uh by timestamp. That's pretty straightforward. Same for last touch, sort by group ID, and then you know what's the last event source that you see there that okay, that's your last touch. But for things like uh linear credit models, time decay models, and uh multi-touch attribution, you've got to be using something a little bit more horsepower, as much as I love Excel. I think that's totally fair.

I think that one of the other uh things, so you mentioned multi-touch, so people are always wondering about assisted conversions as well. And so it looks like that's not available at this time through GA4 either. Is there a way to add that in? Like, could you, I guess, either in here or in Data Studio, add another column for another piece of data that would tell you an assisted conversion, for example. Hmm.

Maybe. And I'm not sure what that would be because I feel like you have you have user source and event source, which are really the two things that you would want to look at. I don't know if there's anything that falls in between that that says I don't know, they left and came back. You know, maybe it's some other kind of session information. Hmm.

Let's try this. Uh this is time to experiment. Let's go back to page 12 in our workbook here. And let's see about creating a new table. And as you, the viewer, is doing these kinds of things, it definitely makes sense for you to be labeling.

We've covered um Google Data Studio reports before, and one of the things that John and I had talked about was making sure that you're labeling the pages. And so as you can see, Chris labeled the page in the book, our book of reports, as the GA4 attribution. So you're not looking at it and going, well, was this three, was this four? I don't really remember. So making sure that you're doing that kind of work up front so that people can very easily tell what it is that they're looking at.

I think there is a way to do. Let's take a look at some event name. Okay. Yes, I think there is a way to do like you were saying, the um at least a linear journey. But it's not pretty.

You know, it might also just be worth noting that one of the questions that we as consultants get all the time is can I look at the individual user level data in Google Analytics? And in Google Analytics 3, not really. And it looks like that carried over into Google Analytics 4. You still cannot tell who the individual person is, and that's by design. Um, Google Analytics is not the place to be housing and looking for personally identifying information.

Now, if you have other systems like the CRM, um, you can certainly hook up Google Analytics into that and start to do some of that matching, but just straight Google Analytics alone, you will never know who exactly the person is. And again, that's by design. So what we had to do here is we had to connect directly to the big query database itself. We couldn't, we were not able to connect. We can't get the data that we want out of Google Analytics for its integration into Data Studio.

So what I did was I connected to the BigQuery table itself, right? This lovely thing. And the attributes that are available in there, even though it's functionally the same thing as the Google Analytics4 table, the attributes that are available from digging into BigQuery directly are different. What you can see is I can get at that user ID. I can get at the time sequencing that things happen, and I can get at the source and or the medium and then be able to process that.

Certainly linear attribution because you'd be able to see all the sources that occur on the way to a conversion. And this would be the starting point for building the more sophisticated uh attribution models. You could theoretically, I guess, make a calculated field for half-lifes for a time decay model. You can't do multi-touch this way. Um it will not be correct.

But that speaks to the, I think just how still under development Google Analytics 4 is, and the fact that the packaged software, namely Google Data Studio, has been formatted to take one data source and look at it two different ways. So you get two different sets of answers. And I can guarantee almost no one using Data Studio knows to look at it both from a Firebase perspective, which is what this is, versus looking at it from a Google Analytics perspective, which was our our previous charts here. So there's two different ways to look at this, the same data and potentially get different answers. My advice now would be if you if you need the other attribution models, just uh get in your DeLorean and go back in time.

Stick with Google Analytics 3 uh until the attribution features in four are more mature, because as we saw, even though we're able to assemble first and last touch based on the data, these are not the ideal models. No, and not for the type of business that we operate. And so, you know, again, the reason for that is because we use more than one channel. We use email, we use organic social. We've now included paid social and paid ads and and other methods and direct.

Um so what this is not telling us is where all of those things play together. This is only just telling us the very first thing somebody saw or the very last thing somebody saw. And as you have a complex mix of channels, these are not the models that you would want in that you'd want to be using to determine what's working. Um, they're the models you want to use if you don't care what the other teams are doing and you just want your stuff to look really good, which quite honestly that works for a lot of companies because they are overinvesting in one specific channel, even if people are still coming to them through you know, SEO or organic social. They don't really care about the things.

They just want to know, you know, where does email fall? So it really depends, but for us, these models don't make sense. Exactly. Yeah. Anything which is more than just a single touch, single transaction, these models are a starting point, but they are by no means the finished product.

And as we're talking about, you can see there's differences, even in just, you know, uh looking side by side, uh, the sources and the mediums and the conversions and the views show up differently for each of these. So that's sort of the state of Google Analytics for attribution right now. It's not great. Um, I fully expect there to be a cottage industry popping up as more and more companies adopt GA4 of companies that can offer software that can do some of the attribution modeling. I would suggest that uh every marketer take a hard look also at the attribution models that are available in your marketing automation software as long as the data is connected properly.

Um and it has more than last touch. I mean, if it's last touch, then it's no better than this. But if they they do have true multi-touch attribution, you may want to be looking at that as a source of truth as well. So, Chris, as a marketer, what should we be thinking about in terms of Google Analytics for and reporting? Because you're obviously you're talking about attribution modeling, which is a more advanced application of the reporting, but even basic reporting, um, basically Google Analytics 4 doesn't allow for any kind of reporting within the system itself.

So, should marketers be worried? Uh there's some. There's some. I mean, so you have the basics, right? That you have um like where did my users come from, which again, this is user source source medium, right?

You have traffic acquisition, which is session source medium. So again, a lot of these things are kind of baked in a little bit. And these are okay starting points again for inquiries. The thing to remember is that Google Analytics 4 is not a reporting tool, right? It is a BI tool.

It is a true analytics piece of software. And that's it's you know, that's what it's designed to do. And that's why it's so different, because it really is all about analyzing your data. That's why the analysis hub really is you know where Google wants you spending your time doing analysis in here, uh, and then you know, picking apart your data. And it's pretty clear by the design, if you want reporting, you should be reporting in Google Data Studio, right?

Google Data Studios where Google says, hey, this is the reporting mechanism. Just like they say, you know, when you look in GA4's uh configuration, where's goal setup? It's not there. There's you can mark things as conversions, but they really want you doing the implementation of your goals in Google Tag Manager. That's you know, that's that is very clearly how they want all of us working.

So, from a marketing perspective, right now, if you've got stuff that's working in GA3, keep working with it. And it's not going to go away soon. And start the planning process for migrating to Google Analytics 4. The first things that you should be doing, no more creating goals that are destination goals uh or anything else in GA3. Every time you put together a new goal, make it an event based goal because it is much easier to when you're setting it up in Tag Manager, um, to then add the corresponding GA4 goal.

It's so much easier to do that, do it once, but properly with in in tag manager than to set up destination goals at GA3 and then go, I gotta go and set up a tag and a trigger and stuff for the same goal in GA4. Do it all in Tag Manager. Um so no more destination goals. Um and start moving your reporting into Data Studio. Whether you're on GA3 or four, doesn't matter.

Move as much of your reporting into Data Studio as you can. Get it away from get people out of the habit of using Google Analytics for reporting. It should just be an analysis tool. That completely makes sense. Uh John, any final thoughts?

Yeah, I just love that idea of getting you know users over in Data Studio because you know everybody's had their horror stories of when they've let the whole world into analytics and they've broken stuff and thrown filters on you you weren't expecting and all that stuff. So yeah, I'm all all for data studio. I agree with that. And if you want help with any of this, uh we're around, feel free to reach out to us. Um, all of our contact information will be available in the parting video, which we love to play at the end.

It's got our fun techno music. Um and I think unless people have questions, feel free to drop them into the chats. That wraps it up for this week's So What. All right. We'll so we'll talk to you all next time.

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 TrustInsights.ai slash T I podcast and a weekly email newsletter at TrustInsights.ai slash newsletter. Got questions about what you saw in today's episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai slash analytics for marketers.

See you next time.