Why Higher Education Websites Need Google Analytics

computer display showing analytics dashboard

Why do university websites need Google Analytics?

The purpose of Google Analytics (GA) isn’t to enumerate visitors, devices or browsers, it is to help understand if a site’s objectives are being achieved and to quantify the level of success.

Sites need Google Analytics (or similar web analytics services) to collect the data to know if site or specific page outcomes are being met and, how well they are being met.

Outcomes are usually marketing or communications objectives. For example, we can use GA to answer questions such as: what proportion of our newsletter recipients click through to the landing page and how does the click-through rate compare with our expectations?

Accurate data helps measure outcomes, identifies content, design or technical issues and lets evidence-based changes be made. In higher education, solid data also persuades decision makers to recognise the expertise of web designers and developers.

How extensively is Google Analytics used in higher education?

Given the sound ‘commercial’ rationale for gathering website visitor activity and behaviour data, how have higher education institutions responded? The rough answer is, very well. Survey evidence shows that most main higher education websites install GA, suggesting that institutions collect valuable visitor acquisition, activity and behaviour data.

A more nuanced response is that institutions may do more counting than measuring outcomes. To measure marketing and communications objectives more effectively, GA needs to be paired with Google Tag Manager (GTM). GTM lets marketing and communications staff be very specific in tracking and ‘tagging’ visitor behaviour without needing to code. In GTM’s absence non-technical staff wait for code to be added to pages to make precise campaign effectiveness measurements. As campaigns usually can’t wait, measuring their effectiveness can get skipped.

Our research shows that GTM implementation rates on main higher education websites runs at roughly 60%. Forty percent of sites are missing out on a straightforward mechanism for recording on-page visitor behaviour.

However, focusing on GA implementation rates on main university and college websites, overlooks the fact that most higher education websites are federations (web estates) of school, division, faculty, centre or institution sites. GA and GTM implementation rates drop quickly at the ‘sub-site’ level. Our recent survey of 300 sub-sites at a large north American research university, found GA implementation at 75% and GTM implementation at 8%. In other words, ¼ of the sites cannot measure if they “work” and over 90% of them are missing out on a free service that can help in assessing if they are achieving their intended goals.

There are many reasons for running a website, none of them includes not knowing if it is achieving its objectives. 

We have three recommendations:

  1. Install Google Tag Manager. It is free, it is easy to learn, it is well documented and it is supported by a pool of knowledgeable individuals.
  2. Once Google Tag Manager is installed, use it to implement Google Analytics.
  3. Install Google Analytics on all institutional sub-sites to ensure data is being collected even if the data is used until later.

In the second post in this series we will examine how to think about measuring outcomes, how to select judiciously from GA’s standard report set in pursuit of that aim and how to determine which data can measure outcomes.

The balance of this post assumes that Google Analytics is installed (or our recommendations have been followed) and some familiarity with GA terminology and will address:

  • configuring GA to collect good or clean data, and
  • how to become familiar with the data elements GA collects and develop a sense of which of these will be useful.

Organising data collection

To supply clean outcome measure data a GA View need fastidious configuration. Two recent client review exercises uncovered basic setup missteps that generated confusion in measuring outcomes. In both cases, the administration settings had not been reviewed for several years.

Four elements of a View set up that we’ve found need adjusting are:

  • The default website’s URL – with the migration to secure connections this often needs updating to https://
  • The time zone – we encounter north American installations set to Pacific Time, which makes analysis of time of day activity confusing if you are not actually in that time zone
  • Bot filtering – leave this turned off in your base ‘raw data’ View, but turn it on for all other Views
  • Site search tracking – turn this on. About 5% of organic search results will pass keywords to GA for analysis, but 100% of internal site search keywords can be available.

We recommend, at least, two Views of your data.  Set one View to be the “raw data”, even bot traffic, so that if data is missing in other filtered-Views it can be ‘reconstituted’ from the raw data. 

A second data View should exclude internally-generated traffic so as not to overstate activity measures or skew on-site behaviour traffic from internal users. This View should be the principal source for detail and summary reporting. A third View might just record internally-generated traffic to measure differences between on- and off-campus activity or behaviour.

Further Views can be created as needed and as outcome measurement becomes more sophisticated.

Understanding your GA data

One of GA’s strengths is its set of Audience, Acquisition, Behaviour and Conversion reports. One of its weaknesses is its set of standard reports, because in pre-packaging the reports users don’t acquire a ‘feel’ for the underlying data. Without a ‘feel’ for the GA data it’s harder to think in terms of the questions the data might answer and easy to get into ‘counting’.

Google provides an excellent solution to developing a sense of GA data: Google Data Studio. Google Data Studio (GDS) is a data visualisation service that readily grabs data from GA views and lets the data be presented as graphs, charts and tables.

We highly recommend performing the following exercise with GDS.  Connect to one of your GA views and drag and drop a table onto the GDS canvas.

GDS will automatically populate the table with a Source (a traffic dimension) and the number of Sessions (a traffic metric), we’ve added Pageviews as an additional metric in the example below:

Each of the available dimensions can be added or dropped from the on-canvas report, by selecting from a drop-down list. Each time a new dimension is displayed, the relevant values are pulled from GA and displayed in the report. The data for every dimension can be systematically reviewed and compared and using live website data – in a way that isn’t achievable in GA reports.

All the available metrics (sessions, Pageviews, average time on page etc.) can be reviewed similarly.


In an hour, most of a site’s GA visitor acquisition, activity and behaviour data can be reviewed and its type and scope better understood.

Our experience in using GDS to ‘review’ GA data in this way is that the process prompts constructive questions about general website and specific web page objectives and what can be measured.

We recommend trying it, ahead of this two-part series' next article.


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