History of Web Analytics at Mason

George Mason has a lot of websites.

Users often move from between Mason websites many times in the course of a normal website visit.

When a Mason staff member thinks of “the website”, they think of their individual website.

When a user of our website thinks of “the George Mason University website”, they think of their entire website visit, often including multiple Mason websites.

Before my involvement, no one was responsible for university-wide digital analytics.

Here are some questions you might have asked about our analytics implementation, and the answers you would have received a year ago:

Across the university:

• 20% of websites had not implemented any analytics at all.
• 80% of websites had installed Google Analytics, most with no customization of any kind.

Of those websites which had implemented analytics, only a handful ever looked at their analytics data.

Of those, only a very few had actually had customized their analytics implementation and/or made use of the data in some way.

Even for those websites which had implemented Google Analytics, and who looked at the data, they could only see the data related to their individual department website. Everything was in separate silos.

Remember that users don’t experience “The George Mason Website” as a collection of separate websites. Instead, they move through many Mason websites as a unified experience.

The ‘real’ analytics is this aggregated picture of a users traffic across many websites in a users entire visit. Where could we see the full picture of user activity? No where. It didn’t exist.

The “Roll-Up” Begins

CHSS, having a large number of websites, started experimenting with a Google Analytics “roll-up” property within their college. The roll-up aggregated analytics data across a number of CHSS websites. Eventually, it began to expand to a few other websites outside of CHSS, including Admissions. This was the beginning of the Mason Google Analytics roll-up. But, at this point, no one was responsible for it, supporting it, working to improve it, or encouraging its adoption across the university.

In my previous position in Enrollment Management, I saw the potential value of aggregated, coordinated analytics data and began to champion the idea of an official university roll-up.

Admissions necessarily interacted with other parts of the university for both marketing and operations so, in many key areas, we needed coordination with, and data from, other departments to succeed (and to figure out if we were succeeding).

I became more involved with analytics at a university level and took a leadership role in expanding the Google Analytics roll-up beyond CHSS. I also co-chaired the new Mason Google Analytics Users’ Group with Danny Collier, director if IT for CHSS.

CHSS entrusted the fledgling Google Analytics roll-up to me, and after Danny went abroad, I took over as the chair of the Google Analytics User’s Group.

We’re not just collecting data.

• We are improving our collection methodology.
• We are establishing best-practices.
• We are partnering with units/departments to try new things.
• We are developing custom reporting and analysis.

We now have a source for “real” university-wide analytics.

I manage and coordinate the Mason Google Analytics Roll-Up. This is a shared Google Analytics property which collects data from literally hundreds of George Mason University websites in an organized way.

Before we had the roll-up, all web analytics data was siloed. Now we can see the full picture.

University Analytics Structure

In addition to the individual website analytics, and the university roll-up, we also have unit level roll-ups.

• University-Level Roll-Up
  • Unit-Level Roll-Up
    • Individual Website

We now have a forum for coordination and problem solving.

I chair the Mason Google Analytics Users’ Group. The group meets monthly, as has met without fail for more than two years now. We have representation from all major areas of the university and we work to identify and solve problems, establish best-practices, and improve our analytics coordination (e.g. campaign-tagging, event tracking).
I also present each month on updates and new analytics topics.

We now have a way to quickly implement a tailored analytics solution.

The Mason Standard Google Tag Manager Container is a GTM container I designed to allow departments to easily set-up a coordinated analytics implementation across Mason websites. In addition to page views, it tracks other critical user interactions, which in many cases have not historically been collected on most Mason websites.
This GTM container also supports additional roll-up properties as described above.

We now have standards to ‘tag’ our marketing activities appropriately so that they can be tracked.

We do all kinds of things every day which drive people to our website: radio ads, brochures, postcards, social media campaigns, news releases, events. In many cases we are not tracking the website visits generated by these activities in an attributable way.

Working with the Users’ Group, I have established standards and best-practices for campaign-tagging, so that units/departments across the university are tagging campaigns consistently. So far I have written standards for email, social media, and print campaign tracking.

Example: Sharing News Stories on Social Media

For more info, see Case Study – Sharing News Stories

Mason Campaign URL Builder

The Mason Campaign URL Builder is a custom web application that helps to coordinate campaign-tagging across the university by providing a tool to easily generate campaign-tagged URLs using the recommended tagging schema.

For more info, see Mason Campaign URL Builder Application

We can now track visits to our website from printed materials.

Each year, the Volgenau School produces an Annual Report about what the school is doing. The report is printed and mailed to a number of educational institutions and companies around the country. This year, the 2017 Annual Report will provide links (URLs) to some “web-extras”: supplementary content available on the Volgenau website. This report is obviously expensive to produce, but there has been no way to measure the ROI.

This year, I worked with Volgenau and ITS to set-up vanity URL redirects that can be tracked in Google Analytics, allowing us to measure how many visits are generated by the annual report and what those users do on the website.

We will also be able to see other information such as where these visitors are located geographically and, in some cases (for large organizations or educational institutions), we may even be able to tell specifically which organizations/institutions are using these links.

For more info, see Volgenau Printed Annual Report Tracking

We are now able to track our specific university KPIs in our analytics.

Page views are not important. Even visits are not important. What is important is whether people are taking the specific actions that we want them to take on our website.

I have worked to develop a list of university-level KPIs that we can track in our web analytics, and have begun implementing customized tracking for our KPIs.

Examples of university KPIs include:

• Inquiry Form Submissions
• Event registrations
• Application Submissions
• Enrollment Deposits
• Job Applications
• Donations

For more information, see Google Analytics: Goals/Conversions.

University Goals Real-Time Dashboard

I have also begun implementing a real-time dashboard that will show the recent status of university KPIs at-a-glance.

For more information. see the Real Time University-Wide Goals page.

We now have a custom reporting solution identify noteworthy trends in our internal website search.

I developed a custom Excel application that automates reporting for noteworthy trends in site search: the Internal Site Search Term Comparison Reporting Tool.

To use this tool, you provide two date ranges: a reporting date range, and a second comparison date range. This tool will automatically:

• query Google Analytics via the API for internal site search data for the selected date ranges,
• analyze and compare the incidence of various search terms, and
• generate “top-10” lists of the top-10 largest search term increases, largest search term decreases, and largest overall changes in search term.

Example: Internal Site Search Analysis – Week of 2017-07-31

Note that you can see the interest in orientation-related topics declining, while the interest in getting-to-campus-logistics increases.

For more info, see Mason Core Internal Site Search Analysis – Week of 2017-07-31

We now have a website to serve as a source of Mason-specific analytics information and support.

The Mason Analytics website, masonanalytics.gmu.edu is a resource for analytics information and university standards and best-practices.

The most important items to get out to the university community are the highlighted tiles on the homepage.

Web analytics methodology: Collect the data -> Analyze the data -> Do something with the data

What can we do next?

Collect the Data

We are doing a much better job of collecting data in a coordinated way.

Analyze the Data

We are now able to do reliable analysis to identify interesting insights.

But we have some many initiatives going on with so many departments across the university, there is no way that one person alone could analyze everything.

See the 10/90 rule: for every 10% you spend on an analytics tool, you shoud spend 90% on analysis. The 10 / 90 Rule for Magnificent Web Analytics Success – Avinash Kaushik

Do Something with the Data

• Improve ROI on marketing efforts by being able to measure effectiveness of campaigns and initiatives.
• Implement A/B testing to experiment and iterate with website content.