#76 The end of rules-based attribution is nigh!

Show Notes

Show notes and full transcript is available at https://bit.ly/3MYs3iH.

00:15 - Intro
 01:31 - Topic
 33:00 - Wind down
 35:20 - Outro

This week Dan and Dara are back solo this week to talk about the recent news from Google that they are removing most of the rules-based attribution models from Google Analytics 4 and Google Ads. Dara keeps asking if anyone cares or is affected by the change, and Dan gets it, but it not super happy to be without linear attribution!

The post #76 The end of rules-based attribution is nigh! appeared first on Measurelab.

What is The Measure Pod?

The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement—with a side of fun.

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 [e-mail] 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.