[MID-TEMPO MUSIC PLAYING]
KRISTA SEIDEN: Hi, everyone.
I'm Krista Seiden.
And today, I am here with Stefano,
who is a product manager on Google Analytics.
And Stefano is going to tell us all about a deep dive on user
behavior analysis techniques within Google Analytics 4
properties.
We're going to go through pathing, funnels, and cohort
analysis.
And I couldn't be more excited for this.
So Stefano, thank you so much for being
here, and over to you.
STEFANO MENTI: Thank you, Krista.
So with these three techniques, there
are three different ways to explore what could a user do
on your app or site.
Pathing-- with pathing, you can take a starting point or ending
point and see how user go from that starting point
or how user get to that ending point.
With funnel, you have a specific user flow in mind.
And you can look at the performance
of that user journey over time.
And with court, you can select a start point and an ending point
in your app or website and see how
user go from one to the other over time and how that changes.
And with that, we can jump into the demo.
And here we are on the Analysis Hub page.
You can click on the Path Analysis
to open the pathing technique.
By default, it opens up with a pre-created path analysis.
You can always start over and create the one
that you want to do.
In here, you can select either the path
from a starting point or the path to a starting point.
In our case, let's explore path to an ending point.
And you can select either an event name
as an ending point or a page title or screen name.
In our case, let's pick an event name.
And let's pick Purchase, since this is an e-commerce property.
We want to see how people get to the purchase event.
The first opening of a pathing will have only one step.
You can expand the step up to 10 step
by just clicking on each node.
It will expand it to the previous mode.
Here you see event name--
will show the previous event name.
But you can change that.
You can mix and match event and pages.
And in this case, let me look at what was the page that
happened before event name.
Check-out confirmation.
If I go one step before, there was check-out review,
and so on and so forth all the way
to the beginning of the path.
You can also see all the other paths to that specific point.
And a few interesting feature for this technique-- you
can apply a breakdown.
For instance, I can look at device category.
And that will show me this path by the difference device.
Most of this path is done on desktop.
But you can see how mobile and tablet perform.
If you click, it will persist.
And you can also--
by over and over, you can see the detail of the breakdown
there.
You can, of course apply segment,
apply filter into the technique.
And one last very cool feature in pathing
is this selection here.
The View Unique Node by default is selected.
And this allows you, when a user does multiple event
on the same page, for instance, to see
that page only as one step.
If I un-select this, if a user does
multiple event in the same page, there
will be multiple step in the path.
Selecting or un-selecting this option
would allow you to answer specific use cases where
you might want to see a user doing multiple times
the same action versus user only doing multiple event
in the same page in the same step.
And with that, that's everything about pathing.
KRISTA SEIDEN: This is awesome.
Thank you so much, Stefano, for showing us pathing.
I think this is probably one of the most useful reports
that people can look at to see how people, in this case,
are getting to your purchase event,
which can be used to then optimize
the user flow on your website.
STEFANO MENTI: Thank you, Krista.
And with that, we can move on the next behavior techniques.
The next one is funnel.
The first thing you have to do on a funnel
is define the step that you want to create for the funnel.
For the sake of time, I've pre-created
these funnel [INAUDIBLE] here that is
looking at the purchase funnel.
In the Step Builder, I'll show you how that looks like.
This is what I've created.
You can define-- give a name-- each step-- step 1, step 2,
step 3--
of the purchase funnel.
You select the condition that defines the step,
gives the step a name, and then apply that list of step
to create your funnel.
Here you see all the drop-off for each step of the funnel,
see how many user pass through the funnel
and how many drop off at each stage.
You can see that both in a visual form as well as in table
form here.
And you can make the funnel--
very interesting functionality.
You can make the funnel open and close.
Close, as is the default--
the user needs to start a funnel from the first step
to be counted.
An open funnel is a funnel where the user
can jump in into the funnel at every point in time.
In this case, we can see this group of users here
that didn't go to the home page, but start directly
to the shopping cart.
Other important functionality in funnel
is the ability to see elapsed time.
So that adds the average time it takes user
to do each step of the funnel.
And you can also add the next action.
So for each funnel step, you can look
at what are the most common events that the user do
after a specific step in the funnel.
And last but not least, as in all techniques in analysis,
you can right-click on a visualization
and create segment of your user from that selection.
And you can do that for the user that do pass
through the funnel or the user.
If you click down here, the user create segment
from abandonment.
So you can create a segment from the people that have not
moved on on the next step of the funnel
that you are interested about.
And that's everything for the funnel technique.
KRISTA SEIDEN: That's awesome, Stefano.
There is so much value in this technique
for users to be able to build and actually see how users are
progressing through a funnel.
And not only that, create segments and audiences
of the drop-off to bring them back.
So I love this.
I hope you guys can take advantage of this technique.
STEFANO MENTI: Awesome.
Thank you, Krista.
And the last technique that we want to show you on this video
is the cohort technique.
On the cohort technique, you can pick a starting point
and an endpoint and see how user go through those two points.
You do that through selecting the inclusion and the return
criteria.
The inclusion criteria is your start point--
is how do user get assigned to a cohort.
And the return criteria is how do user
get evaluated as coming back on each of the subsequent time
period-- in this case, week.
But you can change cohort variety to daily or monthly.
The example we have here is first touch date.
So these are the cohort of user that
gets acquired to this website.
So they first visit the site on these weeks.
And the return criteria is any event.
Here you can select all the events
that you have available as the return criteria.
We have any event, which is the use
case of looking at retention.
Of these many user that were on the site for the first time
during the week of October 18, how many
came back and did anything during any
of the following week?
A couple of very interesting features for cohort technique
is the ability to look at metric type here.
By default, we have the sum.
But with the sum is-- it can be hard to compare
two different cohorts because they
might have different overall number of users.
So if we look at a metric type of per cohort user,
it turns the number in percentages.
So it makes it a lot easier to compare one cohort
to the other.
So here, we can clearly see that in week 1,
the best-performing cohort is the third cohort here,
that there's a 4.4% retention compared to the other cohort.
Another interesting functionality
is the calculation type.
By default, we have standard calculation,
which evaluates the return criteria for the specific week
we are looking at.
So in this case, these 410 user are the user
that are active that have done any event on that week,
second week, after they were first
acquired during this week.
But there are other use cases where
we might want to know different type of calculation.
Rolling calculation is the example
where you want to see user that did return criteria not just
on week 2, but did return criteria in any week up
to that week.
The other interesting feature is that you don't have
to look only at active user.
You can look at any of the metric
you have available in analysis.
For instance, since this is an e-commerce property,
we have purchase revenue.
And this gives you the purchase revenue
for the user that are returning on that week.
And that, coupled with the third acquisition,
the cumulative calculation, gives you
a very interesting insight on what's
the cumulative revenue of that cohort.
So here you can see, for instance,
this $25,000 is the cumulative revenue of the user that
have been acquired on the week of October 18
all the way to four week after they have been acquired.
And that's everything for cohort technique.
KRISTA SEIDEN: This is really awesome
because there is so much more here
that you can do in cohort techniques
now in analysis and Google Analytics 4 properties than you
ever would have been able to do in Universal Analytics.
So I'm super excited for this.
I hope users are, too, because there is lots of great insights
to come here.
Thank you so much, Stefano, for walking us
through all of those techniques within analysis
and Google Analytics 4 properties.
There is so much exciting things here to try out.
I hope you guys can get started today.