Library Analytics (Part 6)
In this post, I’m going to have a quick look at some filtered reports I set up a few days ago to see if they are working as I expected.
What do I mean by this? Well, Google Analytics lets you create filters that can be used to create reports for a site that focus on a particular area of the website or user segment.
At their simplest, filters work in one of two main ways (or a combination of both). Firstly, you can filter the report so that it only covers activity on a subset of the website as a whole (such as all pages along the path http://library.open.ac.uk/find/databases). Secondly, you can filter the report so that it only covers traffic that is segmented according to user characteristics (such as users arriving from a particular referral source).
Here are a couple of examples: firstly, a filter that will just report on traffic that has been referred from the VLE:
Using this filter will allow us to create a report that tracks how the Library website is being used by OU students.
Another filter in a similar vein lets us track just the traffic arriving from the College of Law:
A second type of filter allows us to just provide a report on activity within the eresources area of the Library website:
Note that multiple filters can be applied to a single report profile, so I could for example create a report profile that just looked at activity in the Journals area of the website (by applying a subdirectory filter) that came from users on the OU campus (by also applying a user segment filter).
So how does this help?
If we assume there are several different user types on the Library website (students, onsite researchers, students on partner courses (such as with the College of Law), users arriving from blond Google searches, and so on), then we can use filters to create a set of reports, each one covering a different user segment. Adding all the separate reports together would give us the “total” website report that I was using in the first five posts in this series. Looking at each report separately allows us to understand the different needs and behaviours of the different user types.
Although it is possible to segment reports from the whole site report, as I have shown previously, segmenting the report ‘on the way in’ through the application of one or more filters allows you to use the whole raft of Google Analytics reports to look at a particular segment of the data as a whole.
So for example, here’s a view of the report filtered by referrer (college of law):
Where is the traffic from the College of Law landing?
Okay – it seems like all the traffic is coming in to one page on the Library website from the College of Law?! Now this may or may not be true (there may be a single link on the College of Law website to the OU Library), it may or may not reflect an error in the way I have crafted the rule. One to watch…
How about the report filtered by users referred from the VLE?
This report looks far more natural – users are entering the site at a variety of locations, presumably from different links in the VLE.
Which is all well and good – but it would be really handy if we knew which courses the students were coming from, and/or which VLE pages were actually sending the traffic.
The way to do this is to capture the whole referrer URL (not just the “http://learn.open.ac.uk” part) and report this as a user defined value, something we can do with another filter:
Segmenting the majority landing page data (the Library homepage) by this user defined value gives the following report:
The full referrer URLs are, in the main, really nasty Moodle URLs that obfuscate the course behind an arbitrary resource ID number.
Having a quick look at the pages, the top five referrers over the short sample period the report has been running (and a Bank Holiday weekend at that!) are:
- EK310-08: Library Resources (53758);
- E891-07J: Library Resources (36196);
- DD308-08: Library Resources (54466);
- DD303-08: Library Resources (49710);
- DXR222-08E: Library Resources (89798);
If we knew all the VLE pages in a particular course that linked to the Library website, we could produce a filtered report that just recorded activity on the Library website that came from that course on the VLE.