The data to understand your current position already exists.
But collecting it, interpreting what it means, and building the infrastructure needed to track it over time? That's where most approaches fall short. Most institutions don't have the capacity for it.
That's what I bring.
See the frameworks ↓Higher ed marketing has a natural gravitational pull toward more — more applications, more keywords, more emails. Volume is trackable. The numbers go up. The harder question is whether any of it is producing better outcomes — and that's a question we're not asking as often as we should be.
My work starts from a different premise. Understanding the structure of demand, the content of communication, and the shape of institutional narrative surfaces the information needed to make better decisions.
Better decisions have stakes beyond any dashboard. There are students on the other end of every enrollment decision. That's what makes the harder questions worth asking.
Each framework was built to solve a specific problem colleges actually face — not adapted from a general template. The data and methodology behind each one reflects that. Different problems, purpose-built approaches.
Every framework is built on a classification system that creates structure. The analysis doesn't stop at what the numbers show — it goes deeper into what the patterns mean. The goal is measurement that corresponds to what's happening, and interpretation that surfaces why.
The output of every engagement is a clear executive narrative — what the data shows, what it means, and where to go next. Recommendations are prioritized and actionable, grounded in the data rather than general best practices. The detail is in the appendices, so the findings aren't just credible, they're verifiable.
Your institution invests considerable effort into emails sent to prospective students. But how often do you step back and look at what all of them, together, are actually saying?
What themes dominate? Does the messaging feel student-centered on the receiving end? How does the cadence and narrative compare to the broader market?
This framework benchmarks your prospect email communication against a dataset of 9,000+ emails from 200 U.S. institutions — not to suggest you should sound like everyone else, but to give you a clear picture of where you stand, where the market clusters, and whether what you're sending reflects what you're actually trying to achieve.
CPC, CTR, and CPA get tracked. Keyword strategy gets attention. But the actual search terms triggering your ads? Those often go unexamined.
That gap between who you're targeting and what's actually triggering your ads is where budget leaks — and where opportunity gets missed.
This framework applies a segmentation system custom-built for higher education — covering 1,000+ program types, 150+ degree designations, and nearly 1,500 institution names — to classify a full 12-month search term history. The first layer surfaces where budget is leaking to low-intent or misaligned traffic. The deeper value is in what the search behavior itself reveals — about demand patterns, competitive presence, and how campaign structure is actually functioning in the real search environment.
AI is actively answering questions about your school. The discomfort comes from having no idea what it's saying.
When someone asks AI about your campus, what does it say? How does it compare you to your competitors? Is any of it even accurate? We can't answer these questions with confidence unless we actually look.
This framework runs a systematic prompt audit across the full enrollment funnel — awareness, consideration, decision — on every major AI platform: ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, and Google AI Overviews. The output isn't a list of best practices; it's a current-state picture of how AI is representing your institution right now.
You gain an understanding of what's being said, what's missing, what sources AI is drawing from, and what information is outright wrong. The framework also closes the tracking gap — making sure the right infrastructure is in place so you can see how often prospective students are actually arriving from AI platforms, and whether that's shifting over time. With both pieces, you gain a baseline — an understanding of your current state and the ability to measure changes over time.
The question isn't whether AI is shaping how prospective students see your school. It's whether you know where you stand, what to do about it, and whether what you've done is working.
Institutions invest significant resources in brand positioning and messaging. But public perception isn't shaped by what you say about yourself — it's shaped by what gets said about you.
Earned media, search behavior, and social amplification all contribute to how your institution is perceived externally. But without a structured way to measure those signals together, it's difficult to know whether your public narrative is aligned with your intended positioning — or quietly drifting away from it. This framework builds that picture: translating fragmented external signals into a clear assessment of where the narrative stands, what those signals mean for your institution, and where attention is needed before the drift becomes harder to reverse.
Competitive analysis in higher ed tends to happen at the institutional level. But your competitor for biology isn't necessarily your competitor for nursing. And your competitors for undergraduate enrollment aren't the same schools you're competing with for graduate students. Strategy is executed at the program level — most competitive analysis isn't.
This work is.
This framework applies a structured analytical approach to IPEDS federal data — the most comprehensive source of institutional data in U.S. higher education — to build a robust picture of your competitive landscape and where you stand within it at a program-specific level. The result is a market position analysis built around how your institution actually competes, not a one-size-fits-all list of nearby schools.
The frameworks didn't come from a consulting playbook. They were built from the ground up — after years of experience both on campus and within agencies — to answer enrollment strategy questions that standard tools can't answer.
Using quantitative data to show the what, and digging in qualitatively to understand the why — that's the method. Most analysis stops at the what. Understanding the why is how better decisions get made.
Most conversations start from the same place: a sense that something isn't working, but not enough visibility to know exactly what or why. That's what this work is for.
Everything starts with a conversation: figuring out whether there's a fit before anything else.