| name | enrollment-funnel-and-yield |
| description | Model the enrollment funnel stage-by-stage (inquiry->apply->admit->yield->melt), compute yield and melt, and find the leaking stage before spending at the top of the funnel. Every benchmark carries a definition + retrieval date + verify-at-use; cohort-level only, no student PII. |
Enrollment Funnel & Yield
The class size everyone quotes is an output. This skill decomposes it into the stage rates that actually produce it, so you move the stage that's leaking instead of buying more inquiries by reflex.
Advisory, not compliance advice. Funnel benchmarks and yield/melt rates are volatile and institution-specific. Every specific here is [verify-at-use] against the institution's own IR definitions. No student PII — model cohorts, never individual records.
The funnel
| Stage | Definition (attach the institution's own) | The rate it feeds |
|---|
| Inquiry | A prospective student who has expressed interest | Apply rate = applications / inquiries |
| Application | A submitted, complete application | Admit rate = admits / applications |
| Admit | An offer of admission extended | Yield = deposits / admits |
| Deposit / commit | An enrolled-intent (deposit or equivalent) | Melt = (deposits − census enrollments) / deposits |
| Census enrollment | Enrolled at the official count date | The class |
The rule: a target class = inquiries × apply rate × admit rate × yield × (1 − melt). Change the term that's actually low, not the one that's easiest to buy.
The math that matters
- Yield = deposits ÷ admits. The single most-watched — and most-defendable — funnel rate.
- Melt = the share of deposits that never enroll (summer melt for a fall class). Cheap to defend, expensive to replace.
- Marginal net student = the net tuition revenue of one more enrolled student at the current discount scenario — the number that decides whether a lever is worth pulling (see
../financial-aid-and-discount-rate/SKILL.md).
Metrics table
| Metric | What it tells you | Watch for |
|---|
| Apply rate | Top-funnel interest quality | High inquiries + low apply = weak fit or friction |
| Admit rate | Selectivity / class-shaping | Rising admit rate propping up a soft funnel |
| Yield | Competitiveness of the offer | A yield drop = an aid/timing/fit signal, not just noise |
| Melt | Post-deposit erosion | Melt spikes reward a melt-season touch |
| Net tuition revenue | The real target | Bigger class + higher discount can mean less revenue |
Workflow
- Pull the stage counts and compute each stage rate — attach the institution's definition for each (
[verify-at-use]).
- Compare each rate to prior year and to any benchmark (dated) — find the stage that moved.
- Identify the cheapest lever to move that stage (top-funnel spend, admit-quality, aid, melt-season touch).
- Model the net-revenue impact of the fix vs the alternative before recommending.
Anti-patterns
- Spending on inquiries when the leak is at yield.
- Propping up the class with a rising admit rate.
- Reporting headcount without net tuition revenue.
See also
