According to Ruffalo Noel Levitz’s (RNL) “2022 Cost of Recruiting an Undergraduate Student Report,” it costs:

• $2,795 per undergraduate student at a private school
• $494 per undergraduate student at a public school

The end. {Ah, if only Search Engine Optimization (SEO) were that simple.}

It’s Not an Exact Science

If your brain works like mine does, you probably have a myriad of questions swirling in your brain about how accurate those figures really are. Although RNL does describe its methodology and even includes a handy worksheet for figuring out how an individual college can arrive at its own figures, there are so many nuances to the process that an “accurate” cost is really just an estimation and an overly generic estimation at that.

Just spitballing here, but here are some questions I have:

• Only 59 schools participated in the study so that leaves out data from over 8,000 other colleges and universities. I think that’s a significant enough number to alter the final figures.
   
• Marketing and admissions departments don’t just recruit undergraduates. There are graduate students, adult learners, transfers, certificate students, doctoral students, etc. How do you know how much of your departmental budget went to just undergrad recruitment? You don’t, you make an educated guess.
   
• Branding and reputation are built over many years and also play a crucial role in the recruitment process. If Texas A&M (one of the schools that participated in the report) spent $0 during one admissions cycle on student recruitment, does that mean that they would enroll zero undergraduates in their Fall class? I doubt it. They’d still get their fair share of applications and matriculated students. So how does that factor into the final equation?

I don’t mean to bash RNL here. I think the report is great and the costs are about as accurate as we’re going to get. My issue is with data in general and how interpretive it can be.

You Are Your Own Best Benchmark

In my experience producing digital marketing campaigns for colleges and universities across the United States, one thing has become abundantly clear: each college and university should be benchmarking their data against themselves.

At Calculate, we have a process and a formula that we use with all of our clients when developing digital marketing campaigns for enrollment. One would think that similar campaigns between schools would see similar results. Alas, that just isn’t the case. Here are just a few of the factors I see that influence performance not only between schools but also between campaigns within the same school:

• The school: Let’s face it, some schools are betters draws than others and just have an easier time with recruitment.
• The location: It’s a big, diverse country and your location will have an impact on the performance of your campaign.
• The budget: Larger budgets will fare better than smaller ones.
• The time of year: When you run a campaign will affect performance.
• The tactics: This is related to the budget and a larger budget will allow for an omnichannel strategy that tends to fare better than a singular or dual platform strategy.
• The audience: The demographic and psychographic traits you utilize to target prospects.
• The type of campaign: Is it a general campaign for undergraduates, a transfer campaign, a nursing campaign, a summer melt campaign, etc.
• The execution: Digital marketing is a complex process and two schools aren’t going to execute campaigns similarly.

Here’s what this means: A NESCAC school in the Northeast running a transfer campaign over winter break with a healthy budget on multiple platforms will probably not fare the same as a small, private, school in the Midwest on a limited budget running Meta ads (Facebook and Instagram). And if you’re the underperforming school in this instance, you probably don’t want to benchmark yourself against the other school because it’s not an apples to apples comparison.

How Should I Benchmark?

As previously mentioned, you are your own best benchmark. If researching Cost per Click (CPC), Cost per Thousand Impressions (CPM), Click Through Rate (CTR) etc., for higher education digital marketing campaigns, I’m sure you’ll find some data on the Internet. However, I would caution that those figures conjure up the same myriad of questions I had before about the cost to recruit a single student. In my own research, I’ve found data for “Higher Education” but it didn’t specify that is was specifically for enrollment. That means it could have included alumni, athletic, and developmental campaigns as well and that doesn’t help answer the question for your enrollment team.

So what do you do?

If you want to know how effective a campaign is for your college or university then run one and analyze the data. There’s your benchmark.

For example, let’s say you run a nursing campaign from September through December targeting high school students and their parents via programmatic advertising, search engine marketing (SEM), and social media marketing (SMM) for $25,000. At the end of the campaign you should know:

• CPC per tactic and for the entire campaign
• CPM per tactic and for the entire campaign
• CTR per tactic and for the entire campaign
• Cost per Action (CPA) per tactic and for the entire campaign
• What actions were taken
  • Form conversions
  • Mobile Clicks to Call
  • Landing page phone clicks
  • Clicks to apply
  • Etc.
• A list of names and email addresses from your form conversions that you can then track throughout the enrollment cycle to see who eventually enrolls

And there you have it, you have all of the data you need to compare this campaign to the next one you run. However, keep in mind that you have to run the same exact campaign in order for the benchmarking to work. Change the ages, dates, budget, and/or tactics and you’ve just created a new benchmark.

In other words, if you’re CPC for the above-mentioned nursing campaign is $1.50 but then you run it again in the Spring on a smaller budget, that’s not a fair comparison. You’ve changed two of the significant campaign inputs and you should expect different results.

Summary

Data is great when used as a guide but I’ve learned over the years that data can be interpreted and manipulated to basically make it say what you want it to say. For example, is a high bounce rate on a website good or bad? The average line of thinking is that it is bad. However, in this blog article, I proposed that it’s actually a good thing: Is a High Bounce Rate Really Such a Bad Thing? 

Always remember that your college or university is unique. You have your own reputation and branding, your own geographic location, have your own internal processes and budgets for marketing and enrollment, and much more. All of these nuances will influence the data that your school generates and that is the data you should be using as a benchmark.