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In Google Analytics Essential Training, Corey Koberg shows how to use the Google web analytics platform to generate and evaluate information about the visitors to a web site, including data on site traffic, user behavior, and marketing effectiveness. This course covers the out-of-the-box functionality, from account creation to reporting fundamentals, and explains how to glean insights from the vast array of data available.
One of the most critical steps of doing Web analysis is defining and comparing date ranges. This is a powerful feature in Google Analytics, and could be used in nearly every report in the interface. In each report, we will only see the data for the date range selected. We'll come here, and select the profile that we're interested in doing analysis on. And when we first login, what we're going to see here is the last 30 days, up until yesterday. The reason for this is because the current day is not yet complete, and so it could skew our data. If I want to select the entire month of October, I simply click on the month of October.
If I want to select the entire month of September, I click on September, and it will auto pre-fill those dates for me. I click Apply, and then I will see the date range change to the month of September, and the corresponding data down here will update to show just that date range. I can also select any custom date range that I want. For example, I could put up here, going all the way back to 2009, and I can put my cursor here to select the particular date that I want. In this case, I want to do September 1, 2009 through September 1, 20011.
Click Apply, it will automatically update. Now in this case, because I've selected a long date range, I probably want to back this down to either weeks or months in order to smooth out some of the volatility that we'll see. Okay, that looks better. I can understand trends much easier. Now, note that I see a large drop off here at the end. That's because my date range only includes up to September 1. So, because I'm looking at this on a monthly basis, the month of September of 2011 only includes data for one day. In this case, I probably want to modify this to go up through August 31, to be a more accurate comparison on a month by month basic.
One of the most powerful features is to be able to compare the current time period, versus a time period in past. For example, if I come here, and I want to select the month of October 2011, versus the previous month: September. I select the Compare to past check box, and you can see it pre-fills in the time period. I can change the time period that I'm comparing to by putting my cursor here in the first box, and selecting whichever period I want. In this case, I can compare August 1, or the entire month of September. One thing to be careful for is noting, when you're changing the date range, which particular box you have selected.
Notice that the top one always needs to be the most recent, versus the most past. So in this case, if I wanted to do October 2011, I need to put that here in the most recent, versus the past of September. Now that I have the bottom one selected, I can select September, and it will give me the accurate October versus September, which are the dates I want to analyze. Now, notice what you see here. Because I'm still selected on month by month basis, I only see two single data points for the month of September, versus the month of October. I want to go back and push this back to the daily granularity, so that I can see each of those months overlaid one on top of the other.
In this overlay, I can see the performance for any individual metric, and how one month that versus the others. As I can see, they both had a bit of a run up here in the beginning, they both tended to smooth out, there was some separation here, and then there was a large bump for the orange one here, which represents the month of September, as we got towards the end of the month, and then they came back down towards together at the end. Getting a high level view on the data over time graph up here is interesting, but it's not just here that changes with the date range comparisons; it's every report inside of Google Analytics. For example, if I were to come down here to the Traffic Sources report, and look at my All Traffic sources report, I would see different sources and mediums over those particular date rages.
Here we can see that from October versus September, there was a 60% drop in the traffic that was referred from google.com. Perhaps more interestingly is referrals from reddit.com in the month of October were 5000, but just 68 back in September. So we saw 7000% increase in the number of referrals that came from the reddit.com site over those two months. In addition to the traffic sources, I may come down here to my Content reports, and take a look at some of the reports that we've got about the content of our site, and how visitors are interacting with it.
One thing that jumps out at us right away is a large spike in the number of page views here on this particular day. If I want to investigate this a little bit closer, I can go and change my date range to just include that day; October 24 versus September 24. So on the top, I select just the day October 24, versus September 24, and all of my reports are going to change to just reflect those two days. As I scroll down here, it's going to become apparent to me where those differences in page views are. I see we jumped from 2,700 all the way up to 43,000 page views for this particular page.
For example, if I compare the month of September, I have 30 days, whereas the month of October is 31 days, so we have a different number of days being counted in each one of those. Another thing to keep in mind is in September we had a holiday. So September the 5th on a Monday is not going to necessarily be the same as that same first Monday in the month of October, because one is going to be a holiday, versus the other one is going to be a regular work day. We can expect to see different numbers there, and comparing those two may not be an apples to apples comparison. We can see a few other examples where this can cause us problems in our analysis.
For example, if I look at this case, I can see some troubling results here. In January, the green line here, I can see that in almost all cases, actually in every case, things were better than they were in July. So fast forward seven months, and my traffic has dropped, my revenue has dropped, and in general, I'm doing worse, and this is very concerning. The problem here is I haven't really looked at those apples to apples comparisons. What we want to do is break this down on a year to year basis, where we're comparing the same time periods in one year, versus the same time period in the next. In this case, when we do that, what we can see is we're actually doing better.
From 2007 to 2008; in 2008, my metrics are up across the board. The reason it didn't look like it in the top was because I was comparing the Christmas time period, versus the middle of the summer, and if you have any kind of seasonal traffic, which many of you do, you maybe comparing times that don't make sense to compare against each other, and a year over year comparison may make more sense. We'll come back to this ability to restrict, include, and compare by date range repeatedly throughout our analysis, and seeing what's changed is often more powerful than the absolute value in isolation.
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