Library Analytics (Part 3)

In this third post of an open-ended series looking at the OU Library website under Google analytics, I’ll pick out some ‘headline’ reports that describe the most popular items in one of the most popular content areas identified in Library Analytics (Part 2): databases.

Most Popular Databases
I can imagine that a headline report that everyone will go “ooh” about (notwithstanding the fact that the report is more likely to be properly interesting when you start to segment out the possibly different databases being looked at by different user segments;-) is the list of “top databases” (produced by filtering the top content report page on URLs that contain the term “database”)

So how do we work out what those database URLs actually point to? Looking at the HTML of the http://library.open.ac.uk/find/databases page, here’s where the reference to the most popular database crops up:

<a title=”This link opens up a new browser window” target=”_blank” href=”/find/databases/linking.cfm?id=337296″ onClick=”javascript:pageTracker._trackPageview(‘/databases/database/337296’);”>LexisNexis Butterworths</a>

The implied URL http://ouseful.open.ac.uk/databases/database/337296 doesn’t actually go anywhere real… it’s an artefact created for the analytics tracking (though it does contain the all important internal OU Library database ID (337296 in this case)).

It is possible to create a set of ‘rewrite’ rules that will map these numerical database IDs onto the name of the database. Alternatively, I’m guessing that when the database collection page is written, the HTML could track against dtabase name, rather than ID (e.g. using a construction along the lines of onClick=”javascript:pageTracker._trackPageview(‘Database: LexisNexis Butterworths’);”).

For now though, here’s a quick summary of the top 5 databases, worked out by code inspection!

1. LexisNexis Butterworths (337296);
2. JSTOR (271892);
3. Westlaw (338947)
4. PsycINFO (208607)
5. Academic Search Complete (403673)

Just to show you what I mean by things being more interesting when you start to segment the most popular databases by identifiable, here’s a comparison of the referral source for users looking at Academic Search Complete (403673), PsycINFO (208607) and Westlaw (338947).

Firstly, Academic Search Complete:

In this case, there is a large amount of traffic coming from the intranet. Bearing in mind a comment on the first post, this traffic may be coming from personal bookmarks?

I may be in the error bar (i.e. outlier), but I do almost all my research / library work at home – but I log into the OU and go onto the library via the “my links” bit set to the OU journals and OU databases www page. So that would show as in intranet user? but I work remotely.

I could be wrong of course – so that’s one question to file away for a later day…

Secondly, PsycINFO (208607), the Content Detail report for which is easily enough found by searching on the Content Detail report page:

Here’s the source of traffic that spends some time looking at PsycINFO:

Here, we find a different effect. Most of the identifiable traffic is coming from direct links or the VLE, and the intranet is nowhere to be seen.

Note however the large amount of direct/unidentifiable traffic – this could hide a multitude of sins (and mask a multitude of user origins), so we should just remain wary and open to the idea we may have been misled!

So how can we try to gain an insight into that direct referral traffic (the traffic that arises from people typing the URL directly into their browser, or clicking on a browser bookmark)?

Well, to check that the traffic isn’t coming from direct traffic/bookmarks from users on the OU network other than via the intranet, we can look at the Network Location segment:

No sign of open university there in any significant numbers – so it seems that PsycINFO is more of a student resource than an onsite researcher resource.

Thirdly, Westlaw (338947). Who’s using this database?

It seems here that the majority single referrer is actually the College of Law.

We can segment against network location just to check the direct traffic isn’t coming from users on campus via browser bookmarks:

But some of it is coming from the College of Law? Hmmm.. Could that be a VPN thing, I wonder, or do they have an actual physical location?

Summary
So what insight(s) have we picked up in this post? Firstly, a dodgy ranking of most popular databases (dodgy in that the databases appear to be used by different constituencies of user). Secondly, a crude technique for getting a feel for who the users of a particular database are, based on original source/referrer and network location segmentations.

I guess there’s also a recommendation – that the buyer or owner of each database checks out the analytics to see if the users appear to be who they expect…!

And finally, to wrap this part up, it’s worth being sceptical no matter what precautions you put in place when trying to interpret the results; for example: How Does Google Analytics Track Conversion Referrals?.