Library Analytics (Part 1)

Having had a wonderful time at ILI2007 last year (summary of my talk, according to Brian Kelly – “For most of the people, most of the time, Google’s good enough – get over it…”, though I like to think I was actually talking about the idea of search hubs), I’ve joined forces with Hassan Sheikh from the OU Library on a paper this year’s ILI2008 on the topic of using Google analytics to track user behaviour on the Library website…

First up, it’s probably worth pointing out the unique organisation of the OU, because this impacts on the way the Library website is used.

The OU is a distance learning organisation with tens of thousands active, offsite students; a campus, which is home to teaching academics (course writers), researchers, “academic related” services (software developers, etc.), and administrators; several regional offices; and part-time Associate Lecturers (group tutors), who typically work from home, although they may also work full- or part-time for other educational institutions.

The Library is a “trad” Library, in that it is home to books and a physical journal collection (as well as an OU course materials archive and several other collections) that are typically used by on-campus academics and researchers. The Library has also been quite go-ahead in obtaining online access to journal, ebook, image and reference collections – online access means that these services can be delivered to our student body (whereas the physical collections are used in the main by OU academic and research staff…. I assume…!;-)).

Anyway, to ease myself back into thinking about “Library Analytics”, (I haven’t looked at the Library stats for several months now), here are some warm-up exercises/starting point observations I made, for whatever they’re worth… (i.e. statements of the bleedin’ obvious;-)

Firstly, can we segment users into onsite and offsite users? (I’m pretty sure Hassan was running separate reports for these different gorups, but if he is, I don’t have access to them…)

Even from just the headline report, it appears that a ‘just about significant’ amount of traffic is coming from the intranet.

Just to get my eye in, is this traffic coming from the OU campus at Walton Hall? If we look at the intranet as the traffic source, and segment according to the Network Location of the user (that is, the IP network they’re on), we can see the traffic predominantly local:

By the by, if I’m reading the following report correctly, we can also see that most of the intranet traffic is incoming from the intranet homepage…

And as you might expect, this traffic comes on weekdays…

So here’s a working assumption then (and one that we could probe later for real insight in any principled cases where it doesn’t hold true!): most referrals from the OU intranet occur Monday to Friday, from onsite users, via the intranet homepage.

Secondly, how well is the Library front page working? Whilst not as quick to read as a heat map, the Google Analytics site overlay can provide a quick way way of summarising the most popular links on a page (notwithstanding it’s faults, such as appearing not to disambiguate certain links…)

A quick glimpse suggests the search links need dumping, and more real estate should be given over to the “Journals” and “Databases” links that are currently in the left hand sidebar, and which get 20% and 19% of the click-thrus respectively. Despite the large areas of the screen given over to the image-based navigation, they aren’t pulling much traffic. (That said, if we segment the users it might well be the case that the images in the middle of the page disproportionately attract clicks from certain sorts of user? I don’t think it’s possible to segment this out in the general report, however? For that, I guess we need to define some separate reports that are pre-segmented according to referrer?)

Just chasing the traffic a little more, I wonder if there are a few, popular databases or whether traffic is distributed over all of them equally? The Library databases page is pretty horrible – a long alphabetical list of databases – so can the analytics suggests ways of helping people find the pages they want?

So how are things distributed?

Well – it seems like some databases are more popular than others… but just how true is that observation…?

Let’s do a bit more drilling to see what people are clicking through to from the databases pages… I have to admit that here I start to get a bit confused, because the analytics are giving me two places where databases are being reached from, whereas I can only find one of the paths on the website…

Here’s the one I can find – traffic from:

And here’s what I can’t find on the website – traffic from:

They both identify the same databases as most popular though, though which databases those are I’ll leave for another day…because as you’ll see in a minute, this might be false popularity…

Why? Well let’s just see where the traffic for one of the most popular databases is coming from over the sample period I’ve been playing with:

Any idea why the traffic isn’t coming from the OU, but is coming form other HEIs???

Well, I happen to know that Bath, Brighton and Durham are used for OU residentlal schools, so I suspect that residential school students, after a reminder about the OU online Library services, are having a play, and maybe even participating in some information literacy activities that the OU Library trainers (as well as some of the courses) run at residential school…

Data – don’t ya just love it…? ;-) It sets so many traps for you to fall into!

Author: Tony Hirst

I'm a Senior Lecturer at The Open University, with an interest in #opendata policy and practice, as well as general web tinkering...

7 thoughts on “Library Analytics (Part 1)”

  1. All very interesting stuff Tony.
    Your comments about coming via the intranet and making the sensible inference that they were from on-site users.

    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.

    The reason I go through the intranet is probably ’cause of your
    comments re library www page. But it is getting better isn’t it
    with an excellent update to ORO this week

    Look forward to part II,
    cheers, Mark

  2. I’m not sure if this is helpful, but as a designer visiting the OU Library site for the first time, I could see something of a reason for the data you observed.
    Upon loading, the eye seems to travel in a diagonal motion from top left to bottom right. The most prominent data on the page is actually the Library news, even if there are not a lot of observed clicks. The page’s “weight” seems to be uneven to the right, causing this observed eye motion.
    As Jakob Nielsen’s research suggests, the eye naturally moves in an F-shaped motion on conventional web pages.
    For some reason most likely based on conventional expectations, I then felt drawn to the featured links. These happen to be the useful links, so it’s natural that they’re popular.
    If the search box deserves to be more prominent, it should be so — both semantically and stylistically.
    The picture links are red herring — they’re pictures of people, but the pictures themselves have little to do with the links or the University. They look so much like stock photos that ignoring them feels like the right thing to do — is the middle one Angie Harmon?
    I could get much more specific about things, but those were the biggies that jumped out and had the most to do with the analytics data you observed.

  3. Brad –

    Thanks for the insight from a designer’s point of view – if you were willing to be even more specific about any of the points, I’d be all ears…

    …similarly if there were any reports you wanted me to run to test any hypotheses you have about user behaviour, just ask and I’ll see what I can do..

    (The idea of posting this stuff in public is to try and learn from as many people as possible about how to read/ask questions of/interpret the analytics data…:-)

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