Dara: On today's episode, Dan and I talk about the very recent news that
both Google Analytics and Google Ads will be getting rid of some of
the rules-based attribution models.
Leaving only data driven and last non-direct click.
Daniel: So rest in peace, linear, time decay, position based and everything else.
But if you want to learn a bit more around Google Analytics, what it has to
offer we are currently underway with our first Google Analytics 4 training cohort.
This kicked off a couple of weeks ago, it's going amazingly six week program and
we've got our next cohort runs in June.
So if you want to join the last opportunity to jump on boards and learn
Google Analytics 4 with a bunch of peers and professionals and experts, then
that's your opportunity before the big day that Google Analytics gets switched off.
So check out measurelab.co.uk/training for more information.
And we're currently running a early bird promotion that gives
you 25% off while stocks last.
Dara: Enjoy the show.
Hello and welcome back to The Measure Pod, a podcast for
analytics and data enthusiasts.
I'm Dara, I'm CEO at Measurelab.
Daniel: And I'm Dan, I'm an analytics consultant and trainer also at Meaurelab.
Dara: So it's just you and I again today, Dan.
It feels like it's been a little while, so we have to come up with
something interesting to talk about.
So I'm going to turn to you, put the pressure on you to come up
with a interesting and engaging and challenging topic for us to cover today.
Daniel: Well, luckily Google seems to change something every five minutes, so
I don't have to come up with anything when Google have announced a bunch of
changes that I think are going to be the topic of conversation for today.
The main one and the biggest one and possibly the biggest change they've made
since ,dare I say, biggest change they've made since they've decided to deprecate
Universal Analytics is that they're removing a number of the attribution
models that are available in GA4.
And now these are attribution models that have been around
since the beginning, in a sense.
They've been around since Universal Analytics.
Even in Universal Analytics, we can even create custom attribution models.
But finally, they've come to the end of their life cycle in Google's eyes,
and they're going to be switching off a few of the rules based attribution
models specifically, they're going to be removing first click, linear, time
decay, and position based attribution, leaving only last click, last ads
preferred click and data-driven attribution to be used by us all.
Dara: Good old trusty last click.
I could just imagine all of the people who would be truly devastated
if they announced the news that they were getting rid of last click.
But at the risk of jumping ahead slightly, we'll park this but one of the
questions, one of the first questions that comes to my mind is, who cares?
Daniel: This is going to be another classic Dan versus
Dara conversation I think.
I think I care, I care not just because I cut my teeth in this industry in
an attribution platform, or I've been using attribution or the variety
of attribution models, should I say since I've ever been using Google
Analytics and even beforehand.
The way I've always explained attribution when I'm talking to clients
or colleagues or people I'm training around Google Analytics, especially
when it comes to marketing analytics is around having access to data.
And I think an attribution model is just a lens you can apply on your data to get a
different perspective of what's going on.
For me, what I'm feeling like at least right now initially is that they're
removing these lenses, which means I'm removing the ability to build a
better narrative around what's going on with my customers around my marketing.
Dara: To be clear, and let's maybe, this is probably a good point to maybe
zoom out a little bit and go through the detail of what's been announced.
I have to admit, I'm a little hazy on whether I know you think this is
being removed even from the model comparison tool, and I was a bit
less sure about that because I know it's being removed from the, the
kind of active way you can use it.
So from the default selection in GA4, so you're not going to
be able to change the default.
It's going to be data driven and if you don't meet the thresholds
for data driven it will default back to last non-direct click.
But it was always previously quite useful and this is leading me back
to my kind of who cares question.
And I know obviously some people really do care, but I think by and large, the
majority of businesses using GA are probably still relying on last click,
and at most if they've even done this, they might have gone into the model
comparison tool just to have a look and compare the different models,
which wasn't always that useful anyway because of the fact that you'd always
get that weird thing where direct would look great in every other model.
So it often shows you some slightly weird numbers anyway, but beyond that
I'd love to know, and I mean, it'd be amazing to know this, how many GA
accounts actually have a default model that isn't data driven or last click.
If anyone listening knows that, please let us know.
Daniel: Yeah, I'm inclined to agree and maybe we'll put a LinkedIn
poll up or something like that.
But I think the thing for me is that you're quite right that there's
two ways of thinking about this.
First of all, most people don't change the defaults, whatever they are, and
Google Analytics has defaulted the default attribution model to data-driven
attribution, which in itself, if you don't hit a certain threshold of data volumes
defaults to last click attribution.
So in a sense, we're using last click and data driven out of the box already.
And this doesn't, from what the announcement reads, this is not going
to affect the acquisition reports in the reports workspace in GA4.
So your traffic acquisition and user acquisition reports, which are in a
sense first click attribution and last click attribution, they're going to stay.
What they are removing is the ability to use them as a default model for GA.
So you can only use data driven, I assume they're just going to
make it data driven and not even give you the choice of last click.
And they're removing the opportunity to use it in the advertising workspace or
the attribution reports they call it.
So that is the model comparison reports and the other ones from what it says here.
So it does read from the announcement, which we'll
share a link in the show notes.
It does read that they're removing the ability to use anything but
last click or data driven in all reporting spaces in Google Analytics 4.
The only way I can conceive of being able to get like a linear or a first
click back or a time decay model is to be building it ourselves in a sense.
Maybe there's some open source libraries or packages we can use,
but using the BigQuery export and doing it ourselves in SQL.
Dara: I guess I always think whenever I hear that, like that is obviously useful,
but not being able to change it within GA is going to mean that you're going to
have that issue, which exists, I guess already anyway, where you maybe have
certain people within a business who are using modelled data in BigQuery and then
maybe are feeding that into a dashboard.
But you're probably still going to have some people who are going into
the GA interface, and then you've got that age old question comes up of
why did these numbers not match up?
So just maybe this is, well, it is going to limit the amount you can
do if you do create your own models outside of GA, unless you just
bypass the interface completely, which I guess some people will.
Daniel: Well yeah, but then I mean this poses the question of then,
well, why use GA in the first place?
I think this is another sort of like nail in the coffin in a sense of Google
Analytics being a data product and it's more of an advertising product now.
So Universal Analytics, I've always explained Universal Analytics
was a data tool with a bolt on advertising marketing module, right?
GA4 is a marketing product with a bolt on data module and I think the
way that this is going is kind of reinforcing that idea for me at least.
You know, that this is going to just focus on data-driven attribution.
The only reason I can perceive of that is to better credit digital
marketing channels, such as, for example, Google Ads, Display Video
360 and Search Ads 360, right?
So the Google marketing suite.
There's no other reason, other than computation, it's not
costing Google anything by having these models available, right?
It's just a, you know, as a dropdown in a model comparison
report, it's not doing any harm.
So the removal of them is to, in a sense, forcibly encourage people not to use them
to look at the data from a perspective of Google and the data-driven attribution
model they don't share the source code, so we don't know exactly how it works.
But we do know that it upweights Google advertising slightly more
than other advertising, right?
So in a sense, they're forcing us to use a model that upweights the Google
Stack marketing or the Google marketing platform products, which I understand,
I'm not bitter about it, I get it.
If I was Google, I'd be doing the same thing with a lack of visibility
across data with things like conversion modeling, data-driven attribution, machine
learning modeling, behavioral modeling, you know, where consent's not provided
through things like consent mode, it's all going into this big black box.
And then machine learning happens, and a variety of different
levels then out spits a result.
And I think that's the nature and the reality of things nowadays.
And I think this is just another one of those inevitable changes where,
you know, acquisition reporting and attribution modeling is going to
be all machine learning modeling.
Again, you know, it's another layer of machine learning modeling that's
obscuring the underlying data.
And yes, we get access to the underlying data.
Yes, we can use BigQuery.
It's almost like a lazy answer to everything nowadays.
Just use BigQuery, do it yourself, most people won't, most people don't know how
to, but yeah, that's still not going to help because you know, Google does so much
to embellish the data that we are never going to be able to replicate this stuff
in BigQuery no matter how hard we try.
There's lots of people trying really hard just to replicate standard reports
in GA, and yet they're still struggling because the output, the data that Google
outputs, the raw data to BigQuery is fundamentally different and misses a lot
of the sort of nuance that Google does on top of the data, which they don't share.
Dara: Yeah, and again, I would go out on a limb and suggest that the vast
majority of users aren't going to be too effective by this, by this change.
You do have your very advanced users who like to use all of the features
and will have gone in and changed their default attribution model, created
their own rule-based models, et cetera.
But I think most people, that's not going to be the case.
If I'm to be uncharacteristically less cynical, non cynical, you
know, Google do have that data.
So maybe, maybe they have looked and seen that, you know, not too many people
are using, are changing the defaults.
So they figure, well, let's get rid of that option.
And the thing with the data driven is, I always think, it's the
unknown that gets people, isn't it?
Because in theory, at least, using the data-driven attribution modeling is
going to be better than any abitary kind of rules based or semi data
based kind of rules that you come up with based on your own understanding.
It's always going to be limited to some extent.
You're not going to be able to make it as customised, as kind of optimised and
fine tuned as you could get with machine learning based attribution modeling.
The problem is you don't know how it's working and you don't know if there is
some intentional bias in that, where it is upweighting Google's channels.
So it's probably the unknown that's going to get a lot of people, but something
you said there as well, you know, it's like when something changes, people go
up in arms and it's usually the, probably the minority people like us who will
complain, but before long, to most people this will be the only way it's ever been.
They'll take for granted that the numbers that Google Analytics says are their
conversions will be correct, or to some, you know, correct in inverted comas,
and they'll then maybe have a vague understanding that there's some machine
learning going on behind the scenes.
But you're right, this isn't something that really, if you want to continue
to use the Google stack, then you don't really have a choice, you've
just got to accept this, you either stick with good old trusty last click
attribution, or you put your faith in Google's hands and think, okay, we'll
use the data driven and hope that it's being done in a reasonably good way.
Daniel: Well, for sure, I completely agree and the reason I think I feel strongly
about this is because I've used them and I'm one of maybe the minority that
have used these specifically mourning the parsing of linear attribution,
which was my favorite because it's the my go-to attribution model.
It's easy to, or the easiest one to explain outside of last click, and
it's something that I used often to kind of demonstrate the inherent
biases or the, the limits of last click attribution or first click attribution.
So for me, I'm going to miss linear, I'm going to miss those other things.
It doesn't mean I'm not still going to be able to do some
of the stuff I can already do.
And yes, of course we can go down the path of building ourselves.
I think for me it's more like, and I think this is where people might
feel a bit of an over inflated sense of outrage around this is like, even
though I can't go to your party, I still want to be invited, right?
I want to have the option of this thing there, knowing it's there, and
then me deciding not to use it is different for it to be removed entirely.
So I think that's the justification they've given for removing this, if I
just read it out verbatim because it just doesn't sound satisfying enough basically.
It says, these models don't provide the flexibility needed to adapt
to evolving customer journeys.
Data-driven attribution uses advanced AI to understand the impact each
touchpoint has on our conversion.
That's why we made data-driven attribution, the default attribution
model in GA4 and Google Ads.
For these reasons, first click, linear, time decay, and position
based attribution across Google Analytics 4 will be going away.
So in a sense, it's just saying times are changing data, driven's better,
we're going to remove the old stuff.
It doesn't feel like it's explained it, and I don't know if we've done
a good enough job at the beginning of this conversation, at least Dara,
just to say that this is a Google Ads thing as well as Google Analytics 4.
This is not just a reporting thing, this is going to affect how
people manage and optimise their kind of Google Ads campaigns too.
It is a broader thing than Google are stopping to support these models, not
just in Google Analytics 4, but across the kind of advertising ecosystem as a whole.
Dara: I kind of like it.
Okay it's a classically, kind of short, to the point, Google
kind of help article explanation.
You know, it doesn't go into, it's almost like the less detail you give,
the less opportunity you have to catch your, you know, get yourself caught
out or overexplain and give away some information you didn't want to.
But this is kind of what I was saying, isn't it?
It's like if in theory, using machine learning is going to come up with a
better end result than you just going in yourself and thinking, I'll just
create some relatively generic kind of rule-based attribution models.
The problem again is that you can't tinker with that in any way.
You get very limited visibility on that, but then that's probably the case with,
that's going to be the case more and more.
And you kind of hinted at this earlier, more and more of the day, and we've talked
about this on this podcast before as well, more and more of the data is becoming
modelled, that's not going to stop.
Well, we haven't reached the end of that journey.
The observed data has got gaps left, right, and center.
So those gaps have to be plugged in some way.
So maybe this is just another example of that and the fact that
you can't see into that black box.
I mean, what would you do if you could see into it?
Daniel: Probably not a lot.
Dara: Not a lot.
Just look at it and say, yeah, that looks okay.
Daniel: Yeah, we'll talk about it on this podcast, and that'll
be an episode done right?
They've opened the black box, they've shut it again.
No, but I think just on back on something you just mentioned
around this idea that these kind of algorithmic models are always going
to be better than rules-based models.
I've always found that a bit divisive because yes, a machine is going to
be better at assigning rules with an unbiased, assuming there's no bias
built in, but an unbiased nature.
Like if you ask a paid search marketer what model to use versus a social marketer
versus a display marketer, they're all going to pick a different one that
makes their numbers look higher right?
And so you kind of remove that aspect of it.
The thing that I like about rules based models, and I think this
is the thing that often gets overlooked, is that they are fixed.
They are rules based, they are static in a sense that the rule that how
we attribute email, for example, isn't going to change next year.
So if I'm doing month or month or year on year reporting, the model of which
I'm assessing value has not changed.
And so I'm comparing like for like, apples to apple.
The thing about an algorithmic attribution model is the value in the way that
I'm rewarding email today is different to next week, which is different to
next month and different to next year.
So although we're still looking at a year on year or month to month report,
the whole methodology of what we're looking at is kind of you know, apples
to oranges, you know, in a sense it's an evolving thing and it's never static.
I don't know the frequency of which they update their rules.
Maybe weekly or at least it used to be in GA 360 and Universal Analytics.
So I think this is the thing is there's no consistency anymore like
in a sense, everything's always in flux, in change, evolving.
And so data-driven attribution is another one of that where
you might see that 50% of your conversions go to email this month.
Next month it might be 10% of conversions go to email, but you've done
exactly the same marketing activity.
But the modeling behind the scenes has changed and used a different
approach to email marketing, and it does beg another question of like,
how quickly can it react to change?
If I introduce a new marketing channel in today, how quickly before that
starts to get attributed value, or how long until the machine learns to
recognise it and understand the uplift or down lift that this channel makes.
Same as if I remove a marketing channel from my marketing mix, like
how long before it keeps trying to, you know, like reserve credit for a
channel that doesn't exist anymore.
And I think this is the stuff you never have to consider in a rules-based model.
It's just a fixed way of approaching things that will never have these
things thrown into consideration.
Dara: Yeah, I was just thinking as I was listening to you, if it's totally
opening the black box, it's not going to be useful because you're not
going to know what you're looking at.
But if it could combine it with some kind of like weekly or monthly insights
report where it would tell you the reason why email is now getting half
the attributed credit that it was before is because of X, Y, or Z.
And then have a kind of percentage contribution of those different factors.
So it might say, you know, you introduced a new channel and we think the likelihood
that affected email's role in the conversion journey was 70% and then
20% of it was because you changed the landing pages for your emails or whatever.
That would be useful, wouldn't it?
Because with what you're saying, I get it.
It's like you won't necessarily know what it was that you, you could have
changed so many different things because you would think the model wouldn't
change how it's crediting a channel unless something does genuinely change.
So even if your email activity is exactly the same, something
else must have changed.
Or the model, when it gets updated, it won't change how it's
crediting email unless something else in that chain has changed.
But you don't know what that is, so if it could kind of also give you
a little report each time that the model updates and say, this is why
you're seeing this, or this is the likelihood of why you're seeing this,
then that would be useful, and then maybe people would be okay about it.
But it's not going to tell you anything it's just going to give
you, it's just going to output the numbers, and you're just going to
have to trust that they're reliable.
Daniel: Well yeah, trust is a huge part of this anyway, and I think if you're
using, if you don't trust Google, then you won't be using Google Analytics anyway.
So I think everyone, whether you like it or not, has an inherent trust placed in
Google to track all your data and do all this modeling and sync it to Google Ads,
or you're advertising through Google Ads.
I bet someone's using some automated system there whether it's automatic
bid optimisations, budget adjustments, performance max campaigns, you know,
even looking at some of the audiences, the audience expansions or the kind of
the new version of lookalike audiences.
Like you've got to have a level of trust that Google knows what they're doing and
they're targeting the right people right?
So I think it's just another one of those.
But just on the subject of Google Ads though.
So we talked a lot about Google Analytics 4 and how, I suppose I've
used it or we've used it in the past.
Specifically thinking about the Google Ads side, and I think this is something
that was announced about a couple of weeks ago and they say in the coming weeks,
so I don't know exactly when this is.
But Google Ads, anyone with a Google Ads account might've got this email recently,
but they're now moving over to using the Google Analytics 4 attribution model.
So, at the moment, if you select something like data-driven attribution
or last click attribution in Google Ads, it's only going to do that attribution
model on top of the data that Google Ads has, which is only Google Ads
click and impression data, right?
What this update says is that they're moving over to using the
data-driven attribution conversion values from Google Analytics Four.
So this is in a sense, it's not, I don't think just a coincidental
timing of two features releasing.
I think it's all moving towards one thing, which is we are going to make
data-driven attribution the default.
And when you use your conversions from Google Analytics 4 in Google
Ads, we are going to output or export for Google Ads to use the data-driven
attribution credit for Google Ads.
So it's no longer just going to rely on, you know, the last click data from
Google Analytics, which a lot of people didn't use anyway in Google Ads because
it was never as good as the pixel.
I think if we look at all of these things kind of in total, holistically,
Google Ads is moving over to using Google Analytics for conversions.
I think all signs are pointing towards that, I would even put money that
on in the next year to two years that we are going to see a switch
off of the Google Ads pixels and the floodlight tags as well, just because
like everything's moving over to GA4 and the modeling that it's doing
there, the data-driven distribution modeling that it's doing there.
So if everything's moving that way, if we take that on faith
that, you know, that might happen.
If they're moving over to using data-driven attribution as the
default model in Google Analytics 4 and data-driven attribution credit
is going to be exported to Google Ads for optimisation, there's a lot of
things happening at the same time.
But from a marketing perspective, what it means is that you're going
to be using different data to credit your campaigns and which feeds the
model over there, the optimisation and the, you know, the advertising and
the, the bid adjustment models too.
So the kind of ROI, the CPA, you know, all of that kind of lovely data's
going to be changing because you're going to be using different underlying
data alongside all this other changes.
It's all data driven, of course, but you're now using the data from Google Ads.
All of this is happening in May, by the way.
So at the time we're recording, it's the beginning of April.
And so by May this is going to be for all new accounts set
up, it's going to be this way.
And then eventually they're going to switch off these old attribution
models in both platforms by September.
So in terms of the timeline, it's pretty quick, I don't think that they are.
Well, I think that they are very heavily related features, you know?
Dara: I think you're right, and that's a theory you've, I can't remember if
you've mentioned it on the podcast before, but it's certainly something
you've said to me in real life.
And I wouldn't argue with you, it seems like they're simplifying and they are
kind of converging the different products and the focus will be on GA4 which does
make, it does make complete sense and goes back again to the point about if
you are tied in, shouldn't say tied in.
If you're using the Google stack, then you're going to accept this, you're
going to understand that, you know, Google is going to make these decisions
that are you know, at times potentially favour them, but why wouldn't they?
It's their tech at the end of the day.
You mentioned people need to have a bit of trust, I think they don't
even need to have that much trust.
They need to have some trust, obviously, but you don't have a
choice if you're using Google, and that's what you've always used then
the cost to move away, not just financial costs, but of retraining
people and learning new systems is so high that people will just accept it.
It's a new change, they'll accept it and they'll move on.
Daniel: This kind of harks back to what we were saying about how you can just
use the lazy excuse of just saying, yeah, just do it yourself in BigQuery, right?
Another option there is just use a different product.
Like when they announced the switch off of Universal Analytics and every
analytics vendor came out of the woodwork saying, you know, Google Analytics isn't
dead over here, we've replicated the dashboards, we've got the thing over here.
I think this is going to be another, a bit of catnip for those other analytics
vendors that are going to be like, we've still got all the attribution
models, we're still doing attribution.
Which I, again, I don't think any of these things are real deterrence for the
customers that use Google because it's all going to be in the service of the Google
ecosystem, which is really huge, right?
I mean, that's the reality.
And obviously it's free, but nothing's truly free, like what
you were saying is there's going to be compromise or cost in some way.
Either you pay for the product and it is, you know, in a sense non Google and
it's yours or you get a free product like Google Analytics, but you pay for
it in a sense of, you know, a different way in terms of data or using their
tailored models that maybe Google weighted and, and things like this.
I mean, you've always got choice and I would always say
assess all options all the time.
You know, especially if you're making a change.
You know, assess all options, including not Google Analytics but I think the
other reality, and I think this is just another fact of the digital analytics
marketing life is that everyone will end up using Google Analytics in some
way anyway, because they'll run ads through Google or the Google ecosystem.
Just another bit on the whole convergence of all these products
is that, you know, remember recently they moved everything over to the
Google tag, the one Google tag.
And so now you put one tag on your website that does all floodlights,
Google Analytics, Google Ads tags, and so you don't need to have three
separate pieces of code anymore, it's one tag that does it all.
So in a sense, now when you implement Google Analytics or Google Ads, you
are implementing the same products, you're implementing the Google Tag.
And actually all of that technology is built on Tag Manager anyways.
The point is, is that there is no difference in the implementation
now between Google Ads, Google Analytics, and floodlights.
And I think what they're going to do now that everyone's implementing the same
literal code on their website or app.
They're just going to move the behind the scenes stuff and
just move it over to one place.
So I think, you know, with all this stuff, I think, you know, we know what
we're getting into, we know who we're buying from here in a sense, right?
We know it's Google, we know Google have got a vested interest in making
money, and that's through advertising so, you know, you can't blame them.
Dara: Again, I'm feeling very charitable today, far less cynical than usual.
But you know, there's some benefits I guess, if they tighten up the integrations
between the two products, between Google Ads and GA4, then it, you know, at
least in theory, it should reduce some of the discrepancies that you see when
you're comparing numbers between the two, it's less implementation as well.
So there are some benefits you know, it's cleaner, simpler implementation.
This should be tighter matching of numbers between the two.
Obviously you'll still have differences between, you know,
expected differences between metrics like clicks versus sessions.
There are some benefits to the, to this as well and yeah, nothing comes free does it?
So you have to accept as a result of that, that it could become a little
bit more opaque or you'll have to accept that some of the numbers may
have some bias in them potentially, but when has that ever not been the case.
Daniel: To be more cynical if you are not going to be Dara.
I would say that this is one step closer to the idea that you spend your
money through Google to advertise.
It's got an audience, which you can't see, it then tells you how well it's doing
through models that we can't access and reports on its own performance in a way
that we can't tell if it's correct or not.
So I think this is a, a classic sense, if I use the term of just
them marking their own homework.
So I think this is, you know, to be the cynic here, it's one step closer
towards being unable to validate.
Not only, you know, things that are already removed, like keywords from SEO
or you know, actual audience, people in audiences and things like third
party cookies going away and audience matching and lookalikes all disappearing.
And then you've got machine learning trusting in Google's idea of machine
learning and use of it that they are spending your money, that people are
really clicking on it, that they're reaching the people that they say they're
reaching, and then we are saying, and then tell me how well they went, how much
money did we make from that campaign?
All from a perspective of the company that's serving the ad is doing the
full thing, which, you know, as a independent analytics agency that we
are at Dara, you know, that's one of our kind of bread and butter statements
is when we go work with clients, it's like, well, your agency might do that.
Your agency might be running your campaigns and then marking their own
homework by using sort of attribution models or data points or ways of viewing
data that make it look more profitable or valuable than maybe we could provide
as an unbiased, agnostic perspective.
But in a sense Google's closing the full loop, they're closing that loop
so that you know it's going to be very hard for other people to be able to
be sort of a, an objective or kind of third party in that whole system right?
Dara: Yeah, and you're bringing my cynicism back out again,
which I'm quite happy about.
In a way it's amazing how long we got away with being able to look
at a cleaner picture of attribution within GA because one of the benefits,
you've probably done this too.
You would push somebody to use GA data rather than Google Ads data because of
the fact that GA included all the other channels and treated them equally with
the exception of direct, obviously.
So you could say, well, look, in GA you get to have a, a clear view and
whether you like last click attribution or not, all channels are being treated
the same with the exception of direct.
So it was kind of surprising in a way that you could do that in a platform that was
basically provided mostly for free as a result of people spending on Google Ads.
And now, finally, now that they have the opportunity, I guess, to do it with maybe,
maybe they've calculated this is the point in time where it's going to cost the
least amount of controversy to do this.
And to actually say, look, we're getting rid of these.
You're going to have data driven, last click, that's it.
Daniel: It's going to be interesting to see where the product of Google
Analytics goes, sort of post this and what the next change like this will be.
What's the next step towards this kind of unity of all of their product
suite or their advertising suite here?
Dara: One interface.
Daniel: One interface, yeah god.
The thing that we call keep talking about on this is just that they really
want to rock the boat of Google Ads.
Because Google Ads is like 90% of alphabet's revenue.
It's like over a hundred million or billion a year or some,
something like crazy like that.
So it's like, do they risk even rocking the boat gently and some
of that spilling over the edge?
And I think maybe, maybe they do, but I think it's you know, when you read
stories like, Facebook, Meta lost 10 billion last year because of a change
iOS rolled out and it's like, well, if Google had that, it's going to be a lot
bigger than 10 billion because they're a bigger advertising company and so all of a
sudden it's like, if that's the evolution of marketing is kind of restriction
and lockdown of like tracking and third party pixels and things like that.
If Google's going all in on machine learning and AI to solve these
gaps, then why the hell would they not move everything over to it?
Because some of their sort of material collateral is, they've quote, unquote,
solved the privacy issue in Google Analytics 4, so why would they not
use that solved version of the truth in all their advertising stack, and
they get a bit more control there.
Dara: Is there an exploration technique you can use to look at
different touchpoints in a conversion journey, or is that information just
not going to be available anymore?
Unless you use the raw export to BigQuery.
Daniel: So there's no exploration technique as such.
I suppose you could look at funnels and things like that, or you could
look at the user explorer, which gives you a bit of a, like a CRM
record which is a bit crap to be fair.
But there is the conversion partners report in the advertising workspace
that does give you, that gives you every interaction bar direct, of course,
leading up to a point of conversion.
So I think you've still got that data, you've still got the visibility into
that, but it's still not going to be, it depends what you want to do with it,
because it's quite a micro level report.
Like you're going to look at every unique journey and quantify them.
It's not going to, I don't know if it's going to give you what you need.
Dara: Well that's the one actually, that's what I was thinking of.
In the past at least that was useful if you wanted to see how often a channel
played a part if you didn't really care too much about what the part was.
How many conversions has this channel contributed in some way too.
That was useful, I assumed wrongly, thankfully, that
was getting removed as well.
Daniel: No, so all of those reports will stay, but the attribution
model that it's using is going to be removed except for data driven.
And I think this is the key part.
The one thing I would say on that, and I know exactly what you mean
because we are looking at the contribution attribution, right?
It's the contributed value of like total journeys it's kind of existed in.
The one thing that GA4 doesn't allow you to do, in which Universal
did, which I find really annoying, is that you can't do a filter.
You can't search that to just show you all journeys with a specific channel, and
you can't just search and get the number.
It's so annoying, it's so frustrating.
Dara: Back to my first question.
How big of a deal is this?
How much should people care?
And how much do people care?
Have you, are you in the thick of it?
Are you part of any groups of rebels complaining online about this?
Or are you just thinking, you know what, this is fine, it'll happen not a big deal.
Daniel: I'm in the camp that it is just going to, it's inevitable.
We don't have a say, this is not a democracy right.
Google has decided and told us that something's happening.
I'm in the camp of like, whatever, sure, another change.
I have to update some training content and collateral that I've made, you know,
that kind of stuff that I'm a little bit peeved about, but it's just inevitable
that things like that will happen.
I think it's been a bit of an equal split, some people welcome the change, some
people are against the change, but a lot of the circles that I'm reading through
or listening on is analytic circles.
And I think what's going to be interesting for me is going to be the marketing
circles and wondering the impact that this is going to have alongside that other
change of using the GA data in Google Ads.
So I think it's a bit of a split bag at the moment, you know, there's I would
say 50/50 just to be, you know, safe.
I'm just going to say it's a 50/50 split in terms of sentiment.
I will miss it, but I also appreciate, you know, or understand it a little bit.
You know, just say like, you know, it is what it is, it's happening regardless.
But like I said, I'm going to be interested to see where the marketers,
the search marketers, the display market, anyone using Google ads, I'd
love to get their opinion on this.
Dara: That's a really fair point actually because I guess mainly why
I'm kind of asking that question in a bit of a flippant way because
it's maybe something that I don't always need to worry too much about.
And often the data coming from GA has been the standard last
non click attribution model.
But the change affecting ads is bigger because what's going to happen
to your performance if you, so you either make the change in advance,
you accept that it's going to change.
So what did you say?
You said from May I think new properties, and I guess that
would apply to new ads accounts, wouldn't let you change the default.
Then from September, all accounts will lose the option to select
any of those other models.
So if you've been bidding based on an attribution model that's going
to get taken away, that could mess up your whole account temporarily
while it all readjusts itself.
That would be a reason to care?
Daniel: Well yeah, if you are currently got a marketing or bidding strategy based
on something that's not data driven, then I would, I would be kind of, you
know, there'll be a sweat drop running down my head and just thinking about how
much work have I got to do to rebalance this portfolio of ads, and especially
on some of these bigger ads accounts.
So I would be moving down that way, but like I say, combining this with
the other change I think maybe you could do two birds, one stone and not
have to redo the whole thing twice.
But the move over to using the kind of data-driven export from Google
Analytics, not the data-driven attribution from Google Ads, I think
that's going to be the next step.
I will tell anyone right now, it doesn't matter who you are, if you are not
exporting your GA4 conversions into Google Ads, you need to start right now.
Even if they're secondary conversions purely to run in parallel
alongside your Google Ads pixel, you know, while it's still there
you know, all that kind of stuff.
But I would always in a sense, be doubling up.
I'll be doing the Google Analytics export to Google Ads for conversions,
and I might even be using that already, but I'd at least have it ready to
go so that it's not a shock or a surprise to you, so you're not kind
of like worried about that change.
So yeah, maybe do two at once but I would be, I mean, for me, it removes a tool
in my tool belt when I'm doing marketing analytics reporting, budget adjustment,
you know, kind of end of campaign analysis, reporting, things like that.
I'm not spending tens or hundreds of thousands of dollars a month on
someone's account that's now about to potentially have to change.
You know, I don't have that level of dependency on these right now.
Dara: And there's a, a, an open call to our listeners, if anybody out there
is working in the kind of Google Ad space and is seeing repercussions of
this that maybe we're not paying enough attention to, then let us know and
if you want to come on and chat to us about it, then you can do that as well.
Okay, I think that's all we can really say about this for now, Dan.
We've given our take on it and given people some things to think about, but
I guess we just kind of like with a lot of these changes we continue to kind of
watch and see what the fallout is like.
So it's been a while, I think I mentioned this at the start.
It's been a while since it's been just you and I so we've been off the
hook for our wind downs for a while.
So that should have given you plenty of time to do something interesting.
So what have you been up to, to kind of switch off from work lately?
Daniel: Well, I tell you what Dara, I bought a steam deck not too long ago.
So this is a handheld gaming pc.
And I think I may have mentioned this before, but I've been playing
this lovely game called Sable, and it's made by two people in London,
so it's just a two person outfit.
And they made this beautiful sort of narrative adventure puzzle game,
and I can't recommend it enough.
It's a non-combat game, and it's about solving puzzles and exploring
and uncovering this narrative around why you're there and collecting
things and doing other things.
It's very stylized, very artistic, and I give it a double thumbs
up, Dan's double thumbs up.
So if anyone's out there thinking of something to play next, something that's
chill, relaxing, and maybe 10 to 20 hours long, which is a real big positive, rather
than these 100 to 200 hour epics that I like to play to then check out Sable.
Dara: My mind, even though you told me about the steam deck when you said it,
I thought about like some kind of steam based contraption, like for pressing
your trousers or something like that.
Like an old school iron type, you know, are the ones they have in dry cleaners.
Daniel: Like a trouser press.
Dara: Yeah exactly, a trouser press yeah.
So it just goes to kind of, well reiterate how little I know about gaming.
Daniel: What about you, Dara?
What have you been doing to wind down?
Dara: So, I've been on holiday at least at the time of recording.
So I'm going to just back I got back two nights ago, we went to
Tenerif for a bit of sunshine.
Lots of outdoor activity, lots of hiking, time in the sea, it was really nice.
Daniel: Oh, beautiful.
Dara: And very little time on the screen, which is always great.
A true wind down.
Daniel: Yeah, a true wind down, an escape, amazing.
Dara: That's it for this week, to hear more from me and Dan on GA4 and other
analytics related topics, all our previous episodes are available in our
archive at measurelab.co.uk/podcast, or you can simply use whatever app you're
using right now to listen to this, to go back and listen to previous episodes.
Daniel: And if you want to suggest a topic for something me and Dara should be
talking about, or if you want to suggest a guest who we should be talking to,
there's a Google form in the show notes that you can fill out and leave us a note.
Or alternatively, you can just email us at podcast@measurelab.co.uk to
get in touch with us both directly.
Dara: Our theme is from Confidential, you can find a link
to their music in the show notes.
So on behalf of Dan and I, thanks for listening, see you next time.