- [Instructor] Google Analytics not only records the data
for each visit, it gives us tremendous power and flexibility
in how we can view and analyze the data
that's being stored in this database.
This video's gonna show a few of the most common
and useful ways we can view and visualize the data.
So for example, let's start out here.
We're down at the channels report.
We come under Acquisition, All Traffic,
and click on Channels.
Now, this is one of my favorite reports.
In fact, this is the first one I'm gonna go to
if I have a new client 'cause it provides
so much information about the current state of the site,
and how they generate, engage, and convert their traffic
all in one convenient report.
So we have all of our traffic segmented
into these dimensions, these rows here.
In the primary dimension,
it says the Default Channel Grouping that's gonna represent
the ways the visitor came to our site.
Now, on the other side of the table,
these columns or metrics are split into three sections
that group their function.
We have the acquisition, the behavior, and the conversions.
For example, new users metrics tells us all about traffic
we are acquiring.
And the average session durations speaks to the behavior
of those users once they're on the site.
And then the goal completions metric is gonna tell us
whether or not they converted once they were there.
Now, this tabular format, it presents a lot of data,
but for us humans, it's not particularly easy to remember,
compare, and interpret this information
when it's just a bunch of numbers in this big table.
So Google Analytics gives us some idea
of how to visualize this information.
It's gonna help us gain some of those insights.
The first option is to take these numbers
and turn them into a pie chart.
Go ahead and click up here on the pie chart
and it will do that.
Here, we see our channel groupings just like we had before.
We see the number of users and we see that broken down
visually here in the pie chart.
So of course, we see up here in the table that
the majority here, 33,000 users, come from organic search
and that's very obvious.
If we look in the pie chart, we can see that.
Then we see there are three more that make up
about an equal percentage of each other,
and then a whole bunch of those kind of also-rans here
that are a much smaller percentage.
We look down the numbers, we see that follows here as well.
Now, if you don't like pie charts and many people don't,
you prefer bar charts, we can do that as well.
Click on the bar charts
and we get that exact same thing here.
We see this visually represented.
And the problem I have with this
is that we have these numbers
and then we see essentially the exact same thing
over here again.
What we can do is we can take advantage
of the fact that we have two columns
and we can understand one from a quality metric
and one from this quantity.
For example, instead of repeating Users here again,
what if I switch to something like Bounce Rate?
Okay, so now, what I have here is an understanding
of the quality, bounce rate.
We know the bounce rate is generally a good metric,
meaning, we want to have a lower bounce rate.
And then over here, we see the percentage of people.
This is still based on users.
So while this may display, may have an incredibly high
bounce rate, it also has a very lower percentage
of the users.
And down here in Referral, we see a very low bounce rate,
not quite as much traffic.
And then somewhere in the middle here,
the vast majority of our users have a somewhat
middle-of-the-road bounce rate.
Now, performance is really what we're focusing on here.
So we have volume on the right,
we have the performance on the left.
And so if I look at these, one of the questions I have is,
well, I'm not sure if these are good or bad bounce rates.
One of the things we can do is switch to the next one,
which is a comparison, and it's going to tell us
how this compares to the site average.
So again, we wanna switch this metric
to something like Bounce Rate.
And so here, we'll see the number of users
and then we'll see the bounce rate for each of those.
So what do we see right away
is that there are only two of these that are in the green,
the Direct and the Referral.
Another thing that I see is that
Referral and Social, these are gonna bring about
the same amount of traffic to the site.
But they perform very differently once they get to the site
in terms of Referral having a relatively low bounce rate
and Social having a very high bounce rate.
So even though they bring the same amount of traffic,
what they're worth to me is very different.
Now, speaking of worth, we can also switch this
to something like E-commerce Conversion Rate.
And now, this is interesting here.
We see a huge discrepancy between Social and Referral here
with that last click conversion rate.
In fact, we can switch this to Total Revenue.
Come down here.
And we can see that the curve as a conversion rate
is so good for this Referral channel
that despite the relatively smaller number of visitors,
it contributes more overall revenue as a channel
than anything else by far.
Now, if you wanna take this to the next level,
you can come over here to the pivot.
And we can see the same kind of notion
where we have quality and quantity
by saying Users there and let's say Bounce Rate here,
and we see those.
Or actually, let's go ahead and look at Revenue.
Now, we can group this.
Right now, we've got it again by these channels,
but we can do something completely different.
For example, we can put in here by Country.
And now, we can see the Default Channel Grouping over here,
the rows, and then we can see the countries here.
And for each country, we can see both
the quantity and the quality.
And what's interesting, things that jump out here.
For example, there are twice as many people
coming from India, but absolutely no revenue,
versus Canada only gets half the users.
And one might think, oh, I'm only getting
half the traffic from Canada.
But tons more revenue here, right?
So we actually have attributed revenue,
both quality and quantity, versus lots of quantity,
but no performance coming in from that.
And lastly, up here, we have the term cloud
and this is basically just a word cloud.
I think this is honestly borderline useless in most cases.
So these aren't the most useful for analysis,
but it's here if you want it.
Okay, hopefully, you're becoming more comfortable
with how to navigate the basic visualization options.
As we get deeper into our analysis,
we'll find out that we can uncover far more actionable data
if we know how and when to take advantage
of each of these options.