[MUSIC PLAYING]
KRISTA SEIDEN: Hey, everyone.
I'm Krista Seiden.
And today, I'm joined by Connie Trinh, who
is a product manager at Google.
And Connie is going to tell us all about conversion modeling
and Google Analytics 4 properties.
Thanks for being here, and Connie, over to you.
CONNIE TRINH: Great.
Thank you so much, Krista.
I'm super excited to be here today
to speak a little bit more about conversion modeling
capability in Google Analytics 4 properties.
So why are we doing this, what it is,
and how it impacts your reporting.
So let's hop over to the slides and listen a little bit
about this.
So what is conversion modeling?
When we encounter data that is subjected to browser or privacy
restrictions, or there's disruption in the customer
journeys across devices, the conversions
generated are unable to be tied back to any previous channels.
Therefore, marketers lose insight
into the effectiveness of each channel.
This is where conversion modeling comes into play.
Conversion modeling uses machine learning
to fill gaps in your conversion measurement
that may occur because of the absence of identifiers.
The model then assigns linkages when your conversion events,
such as purchases, downloads, or signs up,
cannot be tied to a channel, such as direct, paid search,
social, or emails.
Modeled conversions allow for more accurate reporting
so you're not left guessing where your conversions came
from and you can better optimize for your campaign.
In order to model for nonobserved slices of data
here, we actually use data from observable slices
where we know behaviors are similar to unobserved slice.
So let's take an example.
Let's say you have a portion of conversions that are not
observable in one browser type, but can be observed
on the other browser types.
So our modeling will first then understand
the behaviors in observed, measurable browsers,
and then we predict the likelihood of the conversion
events on the unobservable browser side.
So why are we modeling here?
Conversion modeling is important because the industry's ability
to tie conversions to ads and other channels
will continue to decrease.
We expect that the availability of individual-level identifiers
to continue to degrade over time.
Previously, we lived in a world of fully observable data,
where we have cookies, device identifiers.
With the changing digital marketing landscape
and a focus on privacy-safe measurement,
data-driven decision-making has become more challenging,
but at the same time, a lot more critical to preserve.
These changes are pushing the industry
to aggregated, anonymized measurement and data
collection.
As the volume of observable data continue to decrease,
the reliance on conversion modeling
to provide comprehensive margin measurement
will continue to increase.
But keeping in mind one thing--
even if all the right infrastructure is in place,
there will still be gaps no one will be able to fill.
So let's take a look at some of the signals
that we'll be using.
In order to generate prediction, we
need to train data on observable slices using
nonsensitive signals from similar slices of traffic.
Each model will have their own methodology
that determines what type of signals and how they're used.
Here are some of the examples of some of the signals
that we actually use.
So we could have device type.
It could be tablet, desktop, or mobile devices.
Another signal we're using is conversion type,
which could be any event that you
think is important, like download, purchase, or an add
to cart.
It could be the country of where the users are coming from.
And it could be the browser types
that they're using, like Safari, Chrome, or Firefox, et cetera.
With modeling, quality is actually a top priority for us.
So in order to ensure quality, we
use techniques, such as holdback validation,
to check for the accuracy of our modeling.
So let's walk through this to see how it works.
For example, we have a lot of observable data here,
and we then hold back a portion of the observable data--
let's say 10% to 20%.
We then apply modeling to that portion,
and then we compare the output of the portion with modeling
and the output of the original observed conversions
that we initially held back.
This allows us to calibrate for any biases.
This is actually a very common methodology
in machine learning that allows us to spot inaccuracies
and refine our models.
Of course, everything here assumes
that the data we train on is representative
of the data we're modeling from.
So the stronger our training data is, which is the data
that you are giving us, the more accurate the modeling will be.
We also have very rigorous thresholds for reporting.
So we only include model conversions in our reporting
when we have very high confidence
that a conversion actually occurred
as a result of an interaction.
We always aim to not overreport.
So let's take a look at reporting.
So when it comes to your analytics reporting,
model conversions can be found in conversion reports,
advertising workspace reports, and explore reports.
So here's an example in a screenshot here.
So on the right-hand side, you see
here is an example of a conversion report.
So on the left-hand side panel, you go down to Engagement.
You drill down into the Conversion report.
Select the conversion type that you'd like to view.
So in this case, you see it's an example of a purchase.
Here's a purchase in the attribution reports.
Here, you can see all the channels
that are attributed to drive your purchase
through the website.
The conversion number will include both observed
and model conversions generated by our machine learning model.
If you want to drill down even further,
you can select additional dimensions, such as Google Ad
network type, to understand further which campaign
generates which conversions.
Here's another example here of an Explore report.
On the left-hand side panel here, select Explore,
and then you can create a new explore report.
Select a dimension that you would like to drill down.
So any dimension that is under the attribution group
here on the right-hand side under the red box,
you will have conversions that include model conversions that
are generated by our machine learning model.
So that's it.
That's an overview of the conversion modeling capability
within Google Analytics 4 properties and how
it will show up in your report.
KRISTA SEIDEN: Thanks, Connie, for walking us
through conversion modeling in Google Analytics 4 properties.
This is a really exciting feature
that really helps us understand our data in a more full way.
So with that, thank you very much.