[MID TEMPO MUSIC PLAYING]
KRISTA SEIDEN: Hey, everyone.
I'm Krista Seiden.
And I'm here today with Stefano, who is a product
manager on Google Analytics.
And he's going to walk us through a deep dive
of user analysis techniques in analysis
and Google Analytics 4 properties.
Specifically, we're going to look
at the User Lifetime and the Segment Overlap
techniques, which are going to be really great information.
So Stefano, thank you so much for being
here with us, and over to you.
STEFANO MENTI: Thank you, Krista.
And for this one, let's jump straight into the demo.
Here we are on the Analysis tab.
To get into the User Lifetime technique,
as all other techniques, you just
click on the User Lifetime one.
And that opens up a user lifetime analysis.
These techniques allows you to deep dive on your users.
So instead of looking at user behavior
like other techniques we have seen in other videos,
where you see what kind of events and journey users
do on your app or site, these techniques is specifically
meant to give you information about your user,
and not just about date range you have selected.
So as all technique, you have a date range selected here.
But this only tells you the users,
filters the user, that are active in this date range.
But the information that we present in this technique
is for the entire lifetime of the user.
So if you have a user in your property
that has been active on the property for the last year,
all these lifetime information are about that user
from the first time they visited your property all
the way to today.
And here we will have a few different type of information.
So from a dimensional perspective,
you will see some first seen and last
seen dimension, including some acquisition dimension.
These are all characteristic of the user, which--
in this case, which are the campaigns and medium
that acquired that user.
On the first seen and last seen, what was the first time
the user visited the property?
What was the last time the user did purchase on the property?
And you can select those information
and add them to your variable panel
to be able to use in these visualization.
From a metric perspective, we have a couple
of different type of metrics.
Other than the typical user count, we have lifetime metric,
where each metric is about the entire lifetime of the user.
And we don't have only the total,
the typical sum you see in G, but we have different version
of that metric.
We have, of course, the total.
But we have the average.
And we also have percentile.
How are percentile helpful?
Let me show you through an example.
So let's pick the LTV, the Lifetime Value.
Let's look at the 10 percentile, the 50, and the 90 percentile.
So if I add those in my visualization here,
let's leave the user Medium.
So I'll break down those figure for user Medium.
I already have Average here.
Let me delete these other two information.
And let's add the 10 percentile, the 50 percentile,
and the 90 percentile to the visualization.
These gives me a sense of the distribution of that metric
for each group of users.
So here, let's look at the first row.
The first row is organic user.
These user-- this is how many they are.
This is their average LTV.
But you can also answer with this technique,
how is this average distributed for this group of user?
In this case, you see $0.00.
That means if you have even the 90 percentile that is $0,
it means that most of these user, like 90% of them,
have $0 in lifetime value with your property.
And this average is given only by a handful of users that
are in the top 10 percentile.
Another example is these row here,
where you see that these many user have these amount
of average lifetime value.
And the 90th percentile is $44.
So you know that the top 10% of user
all have a lifetime value that is greater than $44.
And this structure is available for all the other lifetime
metric.
We have lifetime value, lifetime engagement duration, lifetime
transaction, and a few others.
Another very interesting type of metric that we have available
here is predictive metric.
If your property is enabled for prediction,
we show you the probability of churning and purchase
with the same structure of average and percentile
for each group of user that you have selected.
Another interesting thing that you can use the User Lifetime
technique for is using the dimension
that I showed you earlier.
You can have last purchase date and first purchase date
and see how many user had, in this case--
of the user that purchased the last time on November 16,
what was the first date they purchased?
And these can help you understand the, for instance,
pattern of repeat purchase behavior
or user in the same way we have last active date--
user that haven't been active more recently in your property.
And that's it on the user lifetime technique.
KRISTA SEIDEN: This is great, Stefano.
I know that user lifetime value is
such an important topic for so many Google Analytics users.
And so being able to look at this in their Google Analytics
4 property is going to be super valuable.
STEFANO MENTI: Thank you, Krista.
And the last technique that we want to cover on this video
is the Segment Overlap.
As always, you open up a new tab and look at a Segment Overlap
technique.
These techniques allows you to take different segment
of the user and see how common they
are, what's the overlap of user between those two segment.
Let's look at an example.
So of course, you have to create a segment to do this.
We have a few segment pre-created here.
But let's go ahead and look how we'll create a new segment.
And let's do a user segment.
And let's look, for instance, at purchasers.
So have purchased.
So this is a segment.
I give a name.
This is the segment of my purchasers--
so all the users that have done a purchase event.
And let's create another segment for this example of user
that have viewed a promotion.
We have an event here that is View Promotion.
And these will be the Promotion User segment.
Let's save the segment as well.
Those two segment have been applied here.
And now we can see the overlap between the purchaser,
the small circle, and the promotion-- the user
that have seen a promotion, the bigger circle.
You can see all the different potential combination
of the data down here.
As an example, here is the group that we might be interested in.
These are the purchasers that have also seen the promotion.
One thing that you can quickly see from this segment overlap--
that is, in this case, most purchaser actually
have seen a promotion.
There are very few user in this property
that have made a purchase and have not seen a promotion.
On these visualization, you can also
add additional information.
So I'm only looking at the active user.
But I could come here and add new type
of metrics, search for revenue, the revenue metric.
And I have the revenue metric here.
And it will show me next to the amount
of user for each segment--
show me the revenue--
like for all the other metric.
And of course, the usual right-click-- and you
can create action.
These will be a more interesting segment.
You can create a segment that is the combination of those two
segments.
And with that, that's it for the Segment Overlap.
KRISTA SEIDEN: I love this technique, Stefano.
There's so much value that can be
found in looking at the different overlaps
and even being able to create audiences
of those overlaps for re-marketing purposes.
This is such a cool technique.
I hope users can really go ahead and take advantage of this.
Thank you so much, Stefano, for walking us
through User Lifetime Value and Segment Overlap techniques
in Google Analytics 4 properties.
There's so much value here.
And I hope you guys can start taking advantage of it today.