Marketing Strategy
By Angela Brown
Let’s talk about A/B testing. Every good marketer does it, right?
But while your team is high-fiving over a winning email variant, there’s a hidden cost you might not be thinking about.
To run an A/B test, you split your list. Version A goes to half your audience, Version B goes to the other half. You wait, pick a winner, and then…what?
Half your list got the message that lost. In enrollment marketing, where lists are finite, segments are hard-earned, and prospects are barely hanging on to the funnel, that's not an ideal testing methodology.
This is the rub for traditional A/B testing in higher ed email marketing: 50% of your audience will always receive your second-best message. We treat this as optimization when in reality it’s controlled underperformance.
The Stats Already Know Something You Don't
Students are telling us over and over that relevance and timing matter more than volume, even if we're still measuring the wrong things.
MailerLite's 2026 benchmark data, taken from over 3.6 million campaigns, shows higher education email performance sits at a 43.98% open rate, a 2.15% click rate, and a 9.15% click-to-open rate. The click rate is modest, but the click-to-open rate tells you that when the right person gets the right message, they act.
And as you’ve probably heard, open rates are increasingly unreliable because Apple Mail Privacy Protection inflates them. Clicks are becoming the more honest indicator of real engagement and intent. That means every wasted send, every "good enough" subject line, and every B-version message that goes out to 10,000 prospects, is chipping away at the metric that really matters.
The industry is already moving toward richer, more predictive decision-making. Is your enrollment marketing strategy moving with it?
Why Traditional A/B Testing Fails Enrollment Teams Specifically
A/B testing was built for high-volume consumer email. E-commerce. SaaS. Industries where lists can be enormous, where you can wait 96 hours for statistical significance, and where burning a segment doesn't cost you an application cycle.
Enrollment marketing is none of those things.
Your lists are small and irreplaceable. You're not Amazon. You don't have millions of email addresses. You have prospects, and each one represents a real opportunity in a finite recruitment window. Exposing half of them to a suboptimal message is a big risk.
Statistical significance is a lie at most list sizes. Here's a dirty secret in the testing world: small samples make "winners" look random. If you're testing on a few thousand contacts, and you see a 0.4% click rate difference between variants, you almost certainly haven't found a winner.
Your cycles are too short for slow learning. You can't wait for a clean test, analyze results, and iterate before the application deadline or the event registration window closes. Higher ed email marketing runs on academic calendars, not optimization sprints.
Personalization at the segment level isn't enough. Most A/B tests optimize one variable: subject line, CTA, image. But snappy subject lines don’t inspire prospects. They want a message that speaks to who they are and what they care about. Testing at the population level misses this entirely.
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What Predictive Email Simulation Does
Predictive simulation flips the model.
Instead of sending to your real audience and measuring what happens, simulation runs that test against a synthetic audience first. The AI evaluates your message against what it knows about your specific prospect population: their engagement history, their profile signals, their funnel position. Then it predicts how a message will perform before a single real email goes out.
This is the same principle that drives personalization at scale in every high-performing digital marketing environment, applied specifically to enrollment communications.
As a result, your whole list gets the message that’s most likely to resonate with them. Not 50%. All of them.
What Beta Partners Are Seeing in the Real World
Lynn University: 109 Applications From Prospects Who Weren't Going to Apply
Lynn University came to Halda's AI Campaign Agent beta with a familiar problem: nearly 50,000 names in their prospect pool who hadn't crossed the inquiry threshold. Students who'd shown some interest but hadn't committed to anything.
Director of Enrollment Services Lori Kukuck was one person trying to manage personalized campaigns across six distinct geographic segments: the Northeast, Mid-Atlantic, South, local Florida, the rest of Florida, and international markets. She could manage four versions. Not forty.
Three pilots later, the results were hard to dismiss.
The undergraduate application campaign converted 109 submitted applications from that 45,000-name prospect pool. The graduate application campaign generated 20 submitted applications from a population already receiving other messaging. The campus visit campaign drove 60+ scheduled visits across two campaign launches.
And maybe more importantly, Kukuck got time back.
Holy Family University: A Whole Team Gets Their Strategy Back
Holy Family had already done the hard work. They'd ditched the inconsistent OPM voice, rebuilt internal collaboration, and improved their messaging philosophy. And yet Director of Enrollment Communications Christopher Jester was still spending enormous amounts of time on prewriting: the process of meeting, mapping, and building campaigns before a single word went out the door.
"You spend so much energy just getting to the starting line," Jester said.
After joining the AI Campaign Agent beta, the team found something they hadn't expected: an objective lens on their own content. When the AI pulled from Holy Family's public-facing site to generate campaign messaging, it surfaced an outdated application deadline. The machine found something prospective students were also finding, without anyone knowing.
The behavioral data surprised them too. Prospects who'd expressed interest in a specific graduate program were clicking through to explore completely different programs and social media links. People weren't just looking for an application prompt. They were looking for a reason to believe in a community.
That insight is now reshaping how Holy Family approaches all of its enrollment communications.
The Practical Case for Simulation Over Testing
Let's make this concrete for enrollment marketers running real campaigns.
You stop burning your list on experiments. Every contact who received your B-version was a missed opportunity. Simulation means you put your best message in front of your whole audience, every time.
You get speed without sacrificing accuracy. You don't need to wait for statistical significance. Simulation runs before the send, not after.
You learn without the lag. Traditional A/B testing tells you what worked. Simulation tells you what will work, informed by behavior that already happened.
You can personalize at a level that was previously impossible for a small team. Lori Kukuck at Lynn couldn't manage six geographic segments by hand, but the AI Campaign Agent could.
The Benchmark That Should Change Your Thinking
Here's what the data is telling us in plain English. Higher ed email performance is good but not great on clicks. A 2.15% click rate means 97.85% of your audience didn't act. Every version of that email you send that isn't the best version of that email makes that number worse.
Clicks are the intent signal that matters now. And the way to maximize clicks isn't to test two subject lines on a split list and pick a winner. It's to know, before you send, which message will drive the most clicks from this specific audience.
This is the big change that needs to happen. From post-hoc optimization to pre-send prediction. From testing on people to testing before people ever see the message.
Higher Education Email Marketing Best Practices Are Changing
The higher ed email best practices conversation is overdue for a rethink.
For years, A/B testing has been treated as the gold standard of email optimization. It's rigorous, it's data-driven, and it sounds scientific.
But in enrollment marketing, with the list sizes, timelines, and personalization expectations your prospects now have, traditional testing is often the wrong tool. You're doing precision work with a blunt instrument.
Predictive simulation doesn't replace human creativity or strategic judgment. Lori Kukuck still shapes the messaging. Chriser Jester still drives the strategy. The AI doesn't decide what matters, but it predicts what will resonate given what it knows about your audience.
Enrollment teams that are serious about higher ed email marketing performance aren't going to get there by testing their way into better results over months of split campaigns. They're going to get there by knowing what works before the list is touched.
What to Do With This
If you're running email marketing for a college or university and still defaulting to A/B testing as your primary optimization lever, it’s worth taking a beat.
Ask how many contacts received your B-versions this last cycle. Ask what that cost you in click-through rate. Ask whether your list size gives you the statistical power to trust your test results.
Then ask what a different approach could produce.
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As Halda’s Director of Marketing, Angela Brown brings more than 15 years of experience leading marketing and content teams in education and B2B SaaS. When she isn’t at her computer, you can find her reading, watching a true crime documentary, or driving her son to basketball practice.
