Most higher education marketing campaigns are optimizing toward the wrong thing, and the platforms won’t tell you.

Adult learners don’t search the way they did a year ago, and the platforms reaching them don’t work the way they used to. AI has rewritten how Google and Meta find and convert prospective students, how those students research programs before they ever raise their hand, and how schools can even measure whether any of it is working. The higher education marketing campaign structures and measurement habits most institutions still run on are losing ground without anyone noticing. The schools pulling ahead aren’t spending more. They’re sending the platforms better data, answering students’ real questions, and getting comfortable with a level of testing that higher education has historically avoided.

Key takeaways

• Paid media platforms now run on machine learning, so the quality of the signal you send them matters more than the keywords or audiences you pick. Optimizing toward an undifferentiated lead form is a fast way to buy volume that never enrolls.
• Channel-by-channel, last-click measurement is breaking down. Leaders are moving toward holistic, full-system measurement and incrementality, but the real obstacle is executive buy-in, not the tools.
• Zero-click and AI-driven search have raised student expectations. Prospective students now expect clear answers, transparent outcomes, and a specific reason to choose you, and they’ll find what you hide.
• Speed-to-lead and personalization are no longer nice-to-haves. Students comparison shop more than ever, and a slow or generic follow-up sends them to the competitor down the street.
• The institutions winning right now share one trait: a tolerance for testing and risk. Smarter platforms reward advertisers with the systems and the nerve to use them, and they widen the gap for everyone else.

The platforms changed, and the old playbook is now a liability

A year ago, higher education marketers structured paid media around granular campaigns, tight keyword lists, and conversion pixels that fired when someone filled out a form. That was the best practice. It’s now the thing holding institutions back.

Ernie DeCoite, SVP of Media at Level Agency, puts it plainly: the campaigns fundamentally work differently than they did a year ago, and the way accounts need to be structured and optimized relies on different mechanisms. Google released AI Max. Meta rebuilt its system around Andromeda. Performance Max matured into a primary channel rather than an experiment. Across all of them, the direction is the same: consolidation over granularity, and machine learning doing the work that manual structure used to do.

The throughline connecting every platform is signal. As DeCoite frames it, differentiated signal is the lever that separates campaigns that drive enrollments from campaigns that drive noise. Point these systems at a generic conversion pixel and they’ll happily optimize toward it, delivering a flood of leads that look fine in the platform and never show up on campus. This is why digital marketing for higher education now starts with the data you send the platform, not the settings you choose inside it.

Why higher education breaks the model Google built

Google designed its advertising engine for e-commerce, and that design choice creates a specific problem for schools.

In e-commerce, the signal of a quality conversion arrives instantly. A purchase happens, the value is known, and that information flows back to the platform within seconds. Trends also shift quickly, so the system is built with a short memory.

Higher education works in the opposite direction. As DeCoite explains, a lead might not reveal its true outcome, enrollment, for days or months after it arrives. The platform, meanwhile, wants a value within roughly three days or it can’t use the information. Enrollment volumes are also far smaller than e-commerce purchase volumes, so the sample sizes are thin. And the factors that predict whether a lead enrolls, things like geography, program, and timing, don’t change nearly as fast as fashion trends do, which means a longer look-back actually produces better decisions.

That mismatch is the gap most schools fall into. Level’s answer, the thinking behind its Level.Signal approach, was to build a predictive model on a two-year look-back of lead-to-enrollment performance. The model scores every lead in real time and assigns a value, which lets the platform behave as if it were getting the instant, high-quality signal it was built for. Every lead gets a value, not just the small subset that eventually enrolls, and the platform optimizes toward the prospects most likely to become students.

The institutions that can’t tap into broad historical data, their own and the market’s, are left competing with a fraction of the information available to those that can. That disadvantage is growing inside a lot of accounts right now, even when the in-platform metrics still look healthy.

On social, creative does the targeting

The shift on Meta and TikTok is just as fundamental, and it falls on the creative team rather than the media team. Targeting settings are no longer where social campaigns are won. The creative is what determines who sees the ad.

Open targeting means the algorithm mines pockets of audience based on how people respond to what they see. The implication is direct: feed the system a large, diverse library of creative and let it find the audiences. DeCoite notes that the old standard of four or five ads has given way to platforms asking for twenty, with constant refreshing.

The word that matters is diversity, not volume alone. This isn’t twenty iterations of one winning concept. It means genuinely different ideas, different formats, and different production styles: polished video alongside authentic UGC-style video, smiling-people statics alongside text-overlay statics. Different prospective students, at different stages of their decision, respond to different things, and the system needs enough variety to reach all of them. Meta has always rewarded a steady supply of fresh creative, and the Andromeda era raised that bar considerably.

For most institutions, this is an operational challenge before it’s a creative one. Producing that range of concepts on a continuous cycle requires a creative engine most in-house teams aren’t built to run. Without one, competing on social gets harder, not easier.

Measurement is shifting from channels to the whole system

While the platforms changed underneath marketers, the way leaders judge performance is changing too, and this is where Brent Davis, VP of Client Partnerships at Level Agency, sees the most hesitation.

The goal hasn’t moved. As Davis puts it, good performance for any higher education leader still comes down to enrollment growth, and it always will. What has changed is the path to it. Last-click is fading, and so is the habit of evaluating each channel in its own silo, judging search on its cost per enrollment and paid social on its own separately. Davis describes the shift toward a holistic picture: thinking about whole-system performance and incrementality rather than channel-by-channel results.

The harder part is the buy-in, not the math. Davis sees plenty of marketers having the right conversations about measuring holistically, whether through media mix modeling or causal analysis. What stalls is executive confidence. Leaders still want to see exactly where every dollar goes inside each channel, and that desire for control is precisely what this new approach asks them to loosen. His prescription is to start small and start testing. Google has put out measurement tools that are relatively low lift, and the first move is less about big investment than about making the organizational decision to accept that the world has changed and to empower teams to work differently.

Zero-click search raised the bar on content and transparency

The biggest change to user behavior is happening inside the search box itself. AI-driven results are now the default experience, and DeCoite doesn’t understate it: Google took the product that drove the majority of its revenue for twenty years and blew it up, because the alternative was an existential threat.

For prospective students, this changes the questions they ask. Searches have become more conversational, with follow-up queries that reference what came before. Someone doesn’t type a clean exact-match keyword. They ask a question, then ask the engine to tell them more. No one bids on “tell me more about that,” which is exactly why context now matters more than individual keywords. The practical move, in DeCoite’s experience, is to let the platform read the full context of a conversation rather than a single query, and to feed it a wide set of long-tail keywords that signal the context a school wants to compete in.

Davis sees the same shift from the demand side, in branded search metrics and website traffic, and he is blunt about what it asks of institutions. Students now behave like any other consumer. They expect clear answers, not generic content, and a specific reason to choose your institution. The query is no longer “nursing school near me.” It is which nursing degree is most affordable, has financial aid, and leads to the specific job they want. Schools that can’t answer those questions, in content structured well enough for AI to surface, fall behind.

That puts a premium on transparency, including on the topic higher education has long preferred to avoid. Tuition used to be the subject schools steered around, Davis notes, keeping the conversation on a great education and a great job instead. That evasion no longer works. If a student asks about cost and an institution doesn’t answer, the student finds it somewhere else, very likely in AI search. He frames this as a healthy correction: being upfront produces stronger programs and tuition that lines up with real value. The institutions still hiding behind vague messaging are the ones most exposed.

Speed and personalization decide who converts

AI didn’t just change how students search. It raised what they expect after they inquire, and that pressure lands squarely on operations.

Speed-to-lead has always mattered, but Davis argues it matters more now that AI has reset student expectations. When someone reaches out, getting an admissions person in front of them almost in real time is critical, and that first conversation has to be personalized from the start. The reason is comparison shopping. Davis points to a pattern he has watched change over a decade of focus groups with one long-term school partner. Students used to say they hadn’t really looked at other programs: they heard about a school, got interested, talked to a counselor, and enrolled. Now those same focus groups routinely surface students who compared three or four schools and chose the one that was most cost-efficient or got them to a credential fastest.

That comparison behavior reshapes what happens once a prospect enters the CRM. A one-size-fits-all funnel no longer fits, because students have different needs and different ways they want to communicate. If a prospect cares about program length, the nurture journey should answer that automatically and follow up with material that gives them reasons to believe this is the right program. This is a high-stakes decision for the student, and a personalized experience that answers every question and makes them comfortable is what meaningfully lifts the odds of converting an inquiry into a student who actually shows up on the first day.

For many institutions, the gap here is structural, sitting between marketing, admissions, and IT. A better front-end experience means little if the CRM and lead management setup can’t deliver speed and customization on the back end.

Program demand is consolidating around clear outcomes

The programs gaining ground share a single quality: a clear, in-demand outcome. Skilled trades are the obvious case, with what Davis calls explosive growth over several years in hands-on programs like welding, HVAC, and electrical. That success is now widely understood, so those programs are getting more competitive.

The principle extends well beyond trades. Healthcare programs with clear credential-to-career paths, such as nursing, occupational therapy assistant, and pharmacy technician, remain strong because the outcome is unambiguous and the credential is required to enter the field. DeCoite adds a nuance on the graduate side: working adults are shifting decisively toward online formats, because upending their lives for a ground program is impractical. Yet even for online programs, lead-to-enrollment rates stay tied to geography, concentrated where the institution already has brand awareness, which is why investing in upper-funnel media before the search begins pays off.

The programs getting harder to grow are the ones without a clear outcome or differentiation. Davis points to liberal arts and general business degrees, where the old pitch leaned on the college experience itself. With rising tuition, that pitch has lost its practicality. Students want to know what job they’ll get, how much they’ll earn, and what kind of life it buys. Programs that can answer those questions, in trades, healthcare, and even business, are the ones that grow.

What this means for your 2026 plan

The pattern underneath all of this is worth stating directly. Smarter platforms and smarter students don’t make the work easier for everyone. They reward the institutions with the resources, the systems, and the willingness to act, and they widen the gap for everyone else.

DeCoite’s test for whether a media strategy is built for what comes next, or losing ground, comes down to three questions. Is the campaign structure built for today’s consolidated campaign types rather than yesterday’s granular ones? Is the account feeding differentiated signal instead of generic conversions? And is the media mix genuinely full-funnel, including upper-funnel channels like YouTube, TikTok, CTV, and even podcasting that many schools are still sleeping on?

Davis adds the organizational half of the answer, and it comes down to risk tolerance. Higher education has long played it conservative, for understandable reasons including compliance, but that mindset is now a fast way to get left behind. The institutions succeeding right now have buy-in across the board, not just at the top or just at the execution level, and they treat measurement as a learning loop: invest in MMM or causal analysis, build real plans, execute, report honestly on what worked, then iterate. The metric that matters at the end of that loop is enrollment growth, not low-level proxies like raw inquiry counts. There is no substitute for old-school A/B and holdout testing, and no substitute for being willing to place bets you know you won’t all win.

If you do only one thing this year, make it the thing both leaders point to: get comfortable testing, and improve the quality of the signal and the experience you give students. The rest of the strategy gets easier once that foundation is in place.

The full benchmarks are coming

This is a preview of the thinking behind Level Agency’s 2026 Higher Education Benchmarks Report, landing later this month. The full report maps these shifts to proprietary first-party benchmarks across our higher education portfolio, so you can see how signal quality, creative volume, channel mix, measurement, and program demand are actually performing, and where your programs stand against them. To get the report when it drops and put these benchmarks to work, start a conversation with our higher education team.