The higher education website you manage is generating revenue, but how much? If you manage websites or digital campaigns for a higher education enrollment website you can gain additional clarity into your audience and degree page performance (products) by mapping requests for information or admission application submissions to monetized e-commerce transactions using Google Analytics.
What are you talking about? My website isn’t e-commerce.
I ask you to suspend belief on your concept of what an e-commerce website is. If your organization has an enrollment-focused website for prospective students there are a lot of commonalities with a traditional e-commerce website. You’ll have a product listing page (list of degrees), product detail pages (individual degree program detail pages) and those sets of pages all should lead to one of two primary conversion points, request for information by program or submitting an admission application for a specific program. A large percentage of revenue and growth comes from newly enrolled students in order to meet internal budget and revenue goals, this is a critical set of pages to focus your marketing, content strategy and advertising efforts to get the highest return on investment and to find the highest intent audience that is most likely to convert and matriculate into a student.
- Establishing a target cost-per-acquisition (CPA) to be used for e-commerce transaction values for requests for information or application submissions is key. As we start the project we need to identify the target CPA for the type of conversion we’ll be mapping to e-commerce transactions.
Estimating target cost per acquisition
Loosely defined, cost per acquisition can be defined as enrollment marketing budget divided by total of number of leads or applicants for a given time period. Ideally, at least one year’s worth of data to accommodate for differing acquisition cycles.
In order to find the target CPA for an application submission I’ve previously used a 3 year average of net revenue by program divided by total number of submitted admission applications by program for said time period. This will give you a ballpark figure to better understand the revenue your website is generating. Request for information CPA can be calculated in a similar manner but may require additional analysis work to match historical submitted RFIs to matriculated student.
Target CPA is needed in order to assign a monetary value to the e-commerce transaction in Google Analytics your conversion points. Finding your target CPA also allows your marketing team to optimize all paid media toward that CPA.
Once e-commerce transactions are in place, Google Analytics will calculate total revenue by program (products) and associated pageValue for pages converting users visited, which can be used to optimize degree detail pages to improve outcomes, return on investment from Google Ad campaigns, and revenue per user can be calculated.
Requirements for tracking request for information submissions:
Requirements for tracking admission applications submissions:
Tracking Admission Application submissions can be more challenging to set up. In my experience, this is primarily due to 3rd party admission applications that are widely adopted across higher education and their ability to support 3rd party tracking.
A real-world example from higher education
Researching and understanding your target cost per acquisition for prospective student request for information submissions and admission application submissions will help you to identify an estimated target value for CPA.
Even a lowball estimate in revenue per user will demonstrate incredible value of your website, helping organizations to allocate appropriate resources for their primary marketing channel.
If your website’s forms collect personal information from visitors, for example, request for information submissions, newsletter sign ups, and admission applications, you can treat these leads as a revenue generating conversion point.
Visitors who provide their personal information, assuming it’s valid info, are highly engaged visitors, think about that, would you be willing to share your personal information, name, email address, phone number with a business or organization which you are not interested in? The answer is, most likely, no. If users submit an inquiry, they can be targeted for more specific messaging to help you meet your organization’s enrollment goals further down the funnel.
A real-world scenario:
I worked in higher education for 12 years developing digital strategies for user experience and web analytics tracking of prospective student website conversions. The trend over the last 10 years for higher education enrollment websites is a shift towards a pseudo e-commerce model; clear primary navigation for audiences to self-select into the section of the website that will best meet their needs.
In this example, we’ll be exploring the prospective student website journey in an effort to align user experience (UX) and Google Analytics reporting data to better understand and segment this target audience.
When prospective students arrive to a dot edu website, best practice suggests they typically find themselves exploring degree program detail pages. These pages, which I referenced above as “product” pages, analogous to an e-commerce site, will typically be organized by academic level (undergraduate and graduate), and filtering options such as field of study or subject area. The primary calls-to-action are typically, request information and apply.
Higher education enrollment marketing websites are, in the simplest of terms, lead generation websites. There is value in the prospective student’s personal information that is submitted via request for information web forms.
Submission of prospective student information forms are a critical step in the enrollment funnel because it exposes your highest intent audience, which can then segment in Google Analytics to improve enrollment outcomes and submitted application rates by using digital marketing best practices such as remarketing, email drip campaigns and text messaging to nurture the prospective student down the enrollment funnel.
For the sake of simplicity in this post, let’s focus on tracking admission application submissions.
Putting it all together:
Once a cost per acquisition is established for the given conversion point you can follow this template for the values to be used in your Google Analytics e-commerce transactions.
Load GA e-commerce transaction:
Load the products array:
This method doesn’t support interest in multiple programs however E-Nor can help if that is your use case.
Now that you’ve gone through the effort of estimating your cost per acquisition for a given conversion point, setup your Google Analytics e-commerce code and having validated your data, you’re ready to enjoy the additional benefits unfolded in the GA reporting interface.
What additional data do I get with E-commerce transactions?
E-commerce transactions provide additional dimensions and metrics that were not available out of the box with a standard Google Analytics implementation. Adding e-commerce transactions is where Google Analytics starts to sing with additional reporting capabilities.
Additional dimensions and metrics
Days to conversion, sessions to conversion, highest revenue generating degree program pages, lowest revenue generating degree program pages can all be uncovered once using monetized e-commerce transactions in Google Analytics for inquiries or enrollment application submissions.
A real-world optimization opportunity
In the screenshot below we see a report for our fictitious higher education website used to demonstrate a proof of concept.
What cool stuff can we see in this report? We can see the top 10 converting programs, we can also see an estimation of revenue by program and in aggregate.
PageValue: Once you have data collected for all of your product (degree) pages, you can use the pageValue dimension to compare performance of pages with the same HTML template. For example, if you find one degree program detail page has a higher pageValue than another you can open both pages side by side in a browser and compare the differences, perhaps the hero image could be improved or one of the pages lacks content that’s assisting in conversion such as career outcomes, or a prospective student focused video. My favorite technique using pageValue, suggested to me originally by Brian Clifton, is to take the top performing 10% of a group of similarly designed pages and the bottom 10% of your least performing pages and look for opportunities to optimize your content strategy by comparing top performers to bottom performers.
Using the PageValue dimension to optimize similar pages
Let’s use the PageValue dimension to improve the performance of degree detail pages. We’ll start with the example provided below. Since both degree detail pages below are using the same HTML templates we can look at the PageValue metric to examine differences across like pages. In the example below we can see that the BS Microbiology Degree detail (A) page uses as classroom photo modeling what an in class experience might be like. The BS Computer Science (B) page uses a stock photo of computer programming code. Perhaps the degree detail page with human subjects is more compelling to prospective students? Let’s first look at the pages respective pageValue metrics in Google Analytics (C).
Exhibit A – BS Molecular Biology
Exhibit B – BS Computer Science
Our friend, the PageValue dimension:
Once you start pushing monetized goals or e-commerce transactions Google Analytics will automatically calculate pageValue for pages visitors passed through during their session to conversion. This is an incredibly useful metric as it shows the pages that are actually generating revenue off your website.
The pageValue metric is a simple calculation. Total revenue of monetized e-commerce transactions (or goal conversions) divided by the total number of pages visited in that session. Let’s say for the sake of simplicity your goal or transaction value is $100. If a visitor visits 10 pages and then completes the transaction valued at $100, Google Analytics divides the total value ($100) by the number of pageViews, 100/10. Each page would receive $10 value in this example.
e-commerce Revenue + Total Goal Value
Number of Unique Pageviews for Given Page
In the example below (C), we can see that both the BS in Molecular Biology degree detail page and the BS Computer Science degree detail page have roughly the same amount of pageViews for the given time period however the BS Molecular Biology page has a higher pageValue. We’ve collected enough data at this point to more closely scrutinize the two pages and make a hypothesis as to why MS Molecular Biology has a higher pageValue. Perhaps our original hypothesis on the difference in human subjects in the hero photo is improving conversions? Good news! This hypothesis can easily be tested using Google Optimize to determine the most engaging content for the audience. Google Optimize is a free product which allows website administrators to perform A/B or multivariate tests to examine changes to the user experience and measure those changes against conversion rate or other KPIs to improve outcomes.
PageValue example (C)
We’ve discussed a lot here; primarily, the value of monetizing prospective student conversion points on higher education enrollment marketing websites, estimating target cost per acquisition for degree related conversions to better inform our digital marketing efforts and to understand our website’s revenue potential, optimizing two like degree detail pages using the PageValue dimension and changing the way we think about revenue generating lead generation websites. I hope you enjoyed this overview. Please feel free to reach out with any questions. E-Nor is here to help!
About the Author
Digital Analytics Consultant
Before joining E-Nor, John led digital analytics strategy in the higher education vertical, including large-scale enterprise implementations at the University of San Francisco and Arizona State. John holds a B.A. from The Evergreen State College and an M.S. from Arizona State University. His Master’s research examined the relationship between improved user experience and conversions for a large scale website redesign.