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