Institution inputs — edit to match any client
| Program group | Starts/yr | Inq→start | Spend share | Tuition/start |
|---|
Enrolled starts by program group, 2026–2031
Ghosted lines are the budget-as-usual world — flat demand, flat spend. The gap between solid and dashed is the conversation most institutions are not having yet.
Why this matters
More than half of private universities ran operating deficits in 2024, over 100 colleges sit at elevated closure or merger risk, and a midsize university can reach insolvency at a 1–3% annual enrollment decline — exactly the slope the cliff produces. When volume drops against a stale baseline, the media budget gets blamed and cut mid-year. The agency that brings this model to the budget meeting stops defending last quarter and starts owning the next five years.
The money view, year by year
| Year | Starts | Media spend | $ / start | Tuition rev | vs baseline |
|---|
Tuition revenue = enrolled starts × first-year tuition revenue per start. "vs baseline" is the gap against the flat-demand, flat-spend world the current budget assumes.
Board memo — one screen for the provost and CFO
One-pager: the cliff planner
The high-school graduating class peaked at 3.9M in 2025 and falls toward 3.4M by the early 2030s (Grawe; WICHE). American confidence in higher education has dropped from 57% in 2015 to 42% in 2025, softening conversion on top of shrinking pools. Institutions that budget on last year's enrollment will read the demographic decline as media underperformance — and cut the agency mid-year. The defense is to lead the planning conversation, with the decline priced in before it shows up in the funnel.
- A six-year starts forecast by program group under three demand scenarios, with the budget-as-usual baseline ghosted for contrast.
- Three budget strategies priced in the same units — spend, cost per start, and tuition revenue — so "protect volume or protect efficiency" becomes arithmetic, not a debate.
- A mix-rebalance lever toward counter-cyclical online/adult programs, and a four-sentence board memo written automatically from the chosen plan.
demand(t): UG ×0.975/yr, Grad ×0.99/yr, Online ×1.01/yr through 2030, flat after
severe adds conversion decay: ×(1 − 0.025·t) to −10% by 2030
response curve: starts ∝ spend^0.7 (diminishing returns; rising marginal cost)
protect volume → spend = base × (demand·conv)^(−1/0.7), floored at base
protect efficiency → spend scales so $/start holds; volume floats with demand
The same model fed by real inputs: WICHE/Grawe regional projections joined to each institution's actual catchment geography and program mix, agency benchmark response curves replacing the synthetic elasticity, and actual conversion funnels by program. Refreshed annually when WICHE updates, and delivered as the centerpiece of every client's budget-planning meeting — the artifact that moves the agency from vendor to planning partner.
I spent six years inside higher-ed performance marketing as Director of Solutions at Sparkroom, closing and implementing $1.5M/yr of enrollment-marketing analytics for schools facing exactly these funnels. At Meta I was a senior data scientist on forecasting and media analytics at platform scale. And a renewable-energy MSc taught me long-horizon scenario modeling — planning capital decisions against demand curves decades out. — Jeff Pinto · jeff@jeffpinto.com · jeffpinto.com