In this video you're going to learn how to properly use Google Analytics reports and use some of the
key features in those reports in order to manipulate the data.
So to run through this let us open up an overview report for our audience in here we can look at some
of the key features that you're going to see on almost every report and the first thing to look at is
the date range in the top right hand corner here.
Right now you can see that this is showing for the last 30 days and the dates that are selected and
shown in this report are highlighted in blue here.
You can change that if you want.
We could look at just yesterday last week or even the last seven days and just apply that and then this
report and any other report that we look at is going to be limited to that date range.
So let's just go back and set the last 30 days and we can just say click apply or not.
And one thing to note about this is actually you can see today is not included and that is because today
is not yet over.
So there isn't a full dataset for them to include in that report.
You can also use the compare to feature to get more insights into your data.
So if I take this I can see that in this report I can look at the end compared to the previous period
so let's apply this and see how it changes the data below then we can see in orange.
This is the previous period and in blue now is the current period.
So we're getting a comparison in this chart in a visualization of the change and also down below here
if we look at our metrics really now it's comparing the previous period where the current pre-read to
the previous period so we can see that this month we have 6.7 percent more users compared to the previous
period and it'll also as well as show increases in these metrics show you anywhere where there's been
a decrease.
So for example we're down 1.4 4 percent in average session durations the men's time people spend on
the side when they visit.
So it's very handy feature of the reports.
If you just want to get a quick analysis on how we're doing compared to last month and if you do want
to does the like and this comparison you can just de-selected and hit apply.
And what we're at the top.
We also have the option to add a segment and that gives us the option to add a subset of our data we
can build up that subset and really decide what that is going to be.
It could be from a particular marketing channel or pick a particular demographic or country.
Later on we'll be showing you how to set these up.
But there is that option to add these segments right at the top of every report.
They're moving on a little bit further down.
We're getting to see the line graph here.
And one thing to note is that this line graph is really showing in terms of number of users.
So we can see really this total figure here in the line graph representing the number of users.
We can change that if we want to look at any of the other metrics like say for example the balance raise
or let's have a look at the average session duration.
So it is important to note that this graph here really represents whatever metric is shown in this box
here.
So we can change that back to users any time we want or type anything in there if you want to find a
particular metric.
Now I'm looking across the line graph we can seem really day by day.
The number of users that were on the site so we can see Saturday 18th of November just over 2000.
We can see yesterday just over 9000.
So quite a range there and that's interesting to see on a daily basis.
We can also look at this graph on an hourly basis or even a weekly basis if you just want to get that
higher level of view.
Probably the most useful is just to leave it on a daily basis.
One cool thing about seeing it on a daily basis like this is that you can actually select one of the
days and you can also add a note in there so you can use this annotations feature to add a note and
maybe explain why there's been a peak in traffic like that and I've added up before he going to type
it in and click save.
Perhaps it's the start of a December sale.
Not really explains that spike in traffic and adding these little annotations can be very helpful if
you're looking back on your own reports you can't quite remember three months ago we had a big spike
in traffic and by making these little notes then you can really look at OK.
That really worked before.
Maybe I could do that again.
Adding these annotations is also very useful if you are working with colleagues and you want to share
that information for them or for clients or if someone is taking over your role.
At least they have a history and an explanation of these spikes or dips in traffic.
So the annotations feature is very useful and you can just add those in easily there underneath the
line graph.
So we've covered a lot here in the lyrics are the audience overview report.
We've touched on just the lay out of some of these metrics.
There is a graph here representing a comparison of new visitors versus returning visitors.
And down here as well if you want to change the information that's appearing in this table you can just
click on one of these links here and it's going to show you for example the browser was most popular
over the last 30 days.
We can see here it is chrome with 68 percent of the users.
So we'll go back now and we're actually going to dive into a more advanced report so we can really look
at how that is laid out also and some of the key features there.
So what come down to the location report.
You can see we still have our date range the option to arm those segments.
We have a graphical representation with that one metric here as well.
But really what we want to explain now is and this data table and how you can navigate and use this
and manipulate the data in here.
So firstly how is this table laid out.
Well it's very simple.
We have attributes of our users here like the countries that they're from.
These are going to be in the rows of the data table and in the columns of the data table we have metrics
related to those characteristics of our users.
So we can see here that our users from the US.
That's over 37000 people in the last 30 days representing 45 percent of our total users.
So it's very simple the way these are laid out.
We've got our roads which are characteristics of our users and the columns which are at the metrics
for those individual characteristics.
The language that Google Analytics likes to use is dimensions for these characteristics of our users
and metrics for the metrics themselves.
Now it's important to note that right now we can see that the primary characteristic of this report
here is country but we can change that and look at this table based on another characteristic like City.
So we're now seeing the top cities of our users over the last 30 days.
And you can change that to continent as well.
You can also add in a secondary dimension if you want.
So we could look at for example the browser that was used in the top city which is chrome.
So the point is that you can add in secondary dimensions as well.
If you look at that drill down further into your data and if you want to remove those you can just click
that and it brings you back to that one primary dimension that you're looking at the data table and
the way this is laid out.
Right now it's sorted in terms of the top city and the users from that top city.
If we wanted to change that we could look at the another column of data and really sort the table based
on balanced rate and not on number of users.
So right now we know that this table of data is sorted by users because the little down arrow where
we could change this to be balanced rate.
Now all of a sudden you can see that the table of data has changed and now we can see the cities with
the top bounce rates.
And if we want to extend this the rows in this data table we can go ahead and do that.
We can get a look at reading more and more roads here.
Show up at the top of these reports we also have the option to visualize some of the data before we
do this.
Just come back to look at our country and also look at our number of users.
And right now we have this little icon here means we were looking at the data table that's all we're
looking at now.
We can also look at a pie chart and a breakdown of that sometimes very useful to look at that down.
We can also look at a bar chart.
We want to see that type of visualization and we can also see a comparison to the side average which
can be very useful and we can look at different metrics as well so if we want to look at say the average
session duration from each of these countries we can see that compared to the side average the US is
24 percent greater than the side average.
And we can look at some of those countries where people don't spend as much time on the site.
So now that you understand how to navigate ad the Google Analytics reports maybe a quick exercise that
you can do is just come in.
Change the date range compared to previous period so you can really see the changes that that makes
And the reports.