- [Pavel] Hey guys.
This is Pavel for MeasureSchool.com
and today's topic is custom reports.
If you've been playing with Google Analytics for some time
you probably got to the moment when you said yourself,
okay, do I really have to pull out that many data
and then try to tie them together
outside Google Analytics?
Or do I have to see that many dimensions
and metrics in report when I only need two of them?
If this is the case, then custom reports
are very handy and this is the moment
where you learn how to use them.
So we will show three examples
of custom reports types
available in Google Analytics,
and then we will build one from the scratch
which will show us whether there is a potential
for a conversion rate optimization on your website
by fixing simple bugs you might have
in particular devices.
So let's go straight to it.
If we want to build custom report from the scratch
we have to go to Customization tab
and then Custom Reports.
So here we are, I will minimize the left bar.
And if this is the first time you are trying
to build a custom report,
you won't see any line here.
This is just the report I prepared for you.
We'll get to it later.
But let's now build a report from the very scratch.
So we have to do it by clicking on New Custom Report,
and then you should see something similar like I do.
What we have here, first of all, the types of reports.
There are three of them.
Explorer.
This is something you are familiar with
from standard Google Analytics reports,
because this is the way how 95% of reports
look like in Google Analytics.
And then there is a flat table.
This will be probably something new for you.
And then there is a map overlay.
So let's build a simple one, which is a explorer.
First of all, we have to select the dimension
we want to see as a data breakdown.
Let's start from the very scratch.
I will choose device category as a dimension.
And then let's use three simple metrics.
So users,
sessions,
and then, for example, bounce rate.
So a very simple configuration.
We can name it as
Measure,
Measure School video, and let's save it
just to show you what happens once it's done.
So by clicking on Save and waiting
for a couple of seconds
we should see something like this.
This is the way how 95% of reports
look like in Google Analytics.
But the great thing here is
that you can select the particular dimensions
and metrics you're interested in.
So you can see all the important data
and information you are used to consume
on a regular basis in one place.
Since every time you create a report
and save it, you can just reload it
and you don't have to configure it again from the scratch.
So this is the great thing about it.
All of the standard features available
in default reports are also available here.
So for example, you can select
any date range you want, any granularity.
So day, week, month, everything works here.
Also with segmentation, if you want to,
and also using any secondary dimension you want to.
So this was a simple example of a explorer type report.
Let's show another type, which is flat table.
So I will right now go to Edit mode.
And this is the second report type.
I would like you now to focus
on what will happen once I click on a flat table.
The metric group and dimension group will swap.
So by clicking here, we can see
now device category is above the metrics.
What is the difference between these two reports?
First of them is the way they look like.
We will show that in a moment.
But the second thing why this report is here
is that in standard reports,
we only can see two dimensions
next to each other, right?
The primary and the secondary dimension.
Whereas here, in this report, we can see
up to five dimensions next to each other.
So let me show you how it looks like.
When I will start adding another dimension here,
for example, let's use operating system,
then, for example, browser,
for example, browser size.
I'm just trying to show you
how many of them is possible.
And then, browser version.
Right, you can see that I was able
to select up to five of them.
And if I will click on the Save right now
even the report will look differently.
There is no time series chart
as we are used to from the default reports.
But what we see here
is that we see up to five dimensions
and then the metrics we selected.
Why this report is great?
Sometimes you want to see a much more granular report
than just seeing two dimensions next to each other,
and this is exactly where a flat table takes place.
As is it stated in its name, it's flat table.
So the ideal way how to use this report
is we can see by combination
of five dimensions and three metrics,
we get to 37,000 lines.
So it's not easy to analyze it here.
So the best approach I guess
is to export the data outside Google Analytics
and then play with it, for example, in Excel,
Tableau or any other tool,
which can also visualize the data.
So by building a pivotal table
or any other visualization that you prefer.
So just feel free to export the data
into any format that works for you the best
and play with the data.
So this was the second reports type.
And the third one, I will again
click to Edit, is called map overlay.
This is the report you already probably spotted
when you were exploring the audience
and then geo table.
So by going here, the first two levels
of drill down available here is the zoom level,
which is available here,
or then dimension level, which is available here.
So let me, for example, switch it from continent to country.
I will leave here the three metrics
similar or the same
as we used in the previous reports.
If I will click right now on Save,
we'll see something,
I guess, we are all familiar with,
which is a world map.
What do we see here?
It's a simple visualization showing us
by the darker the color,
the higher the metric.
Right now we have selected users.
So we can see there's the highest volume of users
is from United States.
And if we drill down by clicking on the map
we'll go to the lower geographical level,
which in this case is region.
Even by clicking here, we can easily
drill down to, in this case to the city level.
So I guess this report is pretty straightforward.
What you can do with it
if you have international business
or the business where geographical analysis
is important for you,
this is the great report, which can help you
to easily see which regions, countries
or continents works best for you.
So this were the three simple examples,
or I would say general examples,
I wanted to share with you.
And now comes the time when I want to share with you
the specific report I prepared for you
to show you whether you have a potential
for conversion rate optimization.
So I will go to customer report
and here's the one I already prepared.
So let's click on it,
and I will show you how it's built from the scratch.
If I will click on the Edit right now,
there's one more feature that explorer report has.
It allows us to also do drill down
and it works in a way that by clicking
on the upper level dimension
and then by seeing a lower level dimension,
the report in the lower level is automatically
pre-filtered by the dimension
we clicked on the upper level.
I will show you that in a minute.
But what I decided to build here
is the report with four dimensions:
device category, operating system,
browser, and screen resolution.
And by clicking on every dimension
I will drill down lower
and see whether we don't have
the specific dimensions combination
that will tell us, okay, guys,
you probably have a problem
or potential for conversion rate optimization
for a particular dimensions combination
which be operating system and browser,
browser and screen resolution,
and so on and so forth.
So these four dimensions and three simple metrics:
users, bounce rate, and e-commerce conversion rate.
Let's save this report
and show you the data under it.
I decided to choose top to bottom approach here.
Since I want to go from the broadest device description
to the lowest possible level there,
which would be screen resolution.
So why I chose users is probably quite clear
since I want to see whether a particular device
or then a screen resolution
has significant volume of users.
Then there is a bounce rate
which is a more of a helpful metric.
Then I decided to choose e-commerce conversion rate.
It doesn't have to be necessarily
e-commerce conversion rate,
but any conversion rate or any conversion,
which is your hard conversion
by which you evaluate your website.
So what do I want to show you
with this simple exercise?
I want to spot whether we don't have an issue
with a particular device category
and then drill down dimension.
So where I would start is okay,
my average conversion rate for Google merchandise store,
it's not mine, but Google merchandise store, is 2.9%.
I can see the desktop is almost 4%
and the mobile only 0.65,
which is eight times less than on desktop.
So this is probably something where I would like
to see whether I don't have any issues.
So by clicking here
we'll be able to spot the drill down, right?
If I will scroll to the very top,
I can see that I already have
pre-filtered device category mobile,
and all the data I see here are already pre-filtered.
So the next dimension I chose is operating systems.
As I said, I decided to choose top to bottom approach
when drilling down.
So Android and iOS are of course the two major ones.
And I can see that even between them
there's quite a difference in conversion rate.
The average is 0.65.
The user's distribution is almost 50-50
but I can see, okay, it's like quite a lot
of difference between Android and iOS.
So let's drill down to, at first place to Android.
I want to see whether that half percent conversion rate,
it's pretty much the same for all the other dimensions
I have or there might be some pattern
which if I will fix, then I can probably expect
increasing conversion rates.
So another dimension I have here is browser.
Okay. I can see that 90%
of all browsers on Android are Chrome,
which isn't a surprise,
but going even one level lower,
which is a screen resolution,
let's have a look on conversion rate here.
So the average for all Android devices on Chrome is 0.5%.
And by looking on lines, I can see, okay,
let's have a look on line number four,
which has more than twice conversion rate
comparing to the average.
Okay, this is a specific resolution
which has like a set twice conversion rate.
By going another level lower to the line number five,
I can see that there is zero conversion rate.
It doesn't have like very low volume of users,
so more than 1000 of them.
So it's probably significant
but I can have a strong assumption
that there's probably something very wrong
on this resolution.
There can be like thousand reasons for that.
But this is the place where I would start.
And in general, looking on the data
I would start to search for the patterns.
Can this be with particular height
or width of screen resolution where probably
the website isn't displayed correctly?
So this would be my initial thought about it.
So this was the situation for the Android.
If I will go back right now
to the level of device category,
which was mobile in this case,
we saw, okay that the iOS looks like it's working
quite good, but that might be a mistake.
Even though it's better comparing to Android
I would still do the same quick exercise
drilling down a bit, whether I won't be able
to spot something.
And even here on the level of browser,
I can see that, okay, just 15% of users
who use Chrome as a default browser on a Safari,
the conversion rate is twice as good
as comparing to Safari one,
but since there's much more volume of users
on Safari, I want to drill down a bit here
and see whether that 0.76
is pretty much average on all screen resolutions
or there is a difference.
And even by comparing the line number one
and line number two, we can see
that the conversion rate for this resolution
is twice as good as line number one.
I expect that all of them are iOS users.
So there shouldn't be much of a difference
in a target group
but there's a lot of difference in conversion rate.
So again, try to do the same exercise
as for the Android and see the patterns there.
Okay, looking on the line number five,
we can see even like significantly
higher conversion rate
comparing to line number one.
So try to spot patterns
if it's not again given by a specific thing,
for example, not being displayed correctly
on a particular device,
like add to Cart button or redirection to check out
doesn't work or a set that there can be.
Another thousand reasons why is it so.
This is the place where we should start
by trying to do the same customer journeys
as your users do using these devices.
So this is the place where I would start.
So that was it guys.
Try to do the same simple exercise
by drilling down a bit
and you might be surprised
how many screen resolutions, for example,
you may find where we can improve
your customer experience and conversions.
So hope you enjoyed it and see you in the next video.
- All right, Pavel, thanks for this great information.
If this video helped you out in any way,
then we'd love for you to hit that like button.
And if you want to find out more about Google Analytics,
then check out this playlist right over there
where we have many different topics
waiting for you.
Now, from everybody here at Measure School,