Enrollment Forecasting
“How many students will enroll next semester?”
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A real-world example
How many students will enroll next semester?
Enrollment forecasting errors of 5-10% force universities to either over-hire faculty and over-allocate housing (wasting $3-5M) or under-prepare and deliver poor student experiences. Traditional funnel models treat each applicant independently, missing the network effects: when competing institutions change aid packages, when a program's reputation shifts in applicant peer groups, and when marketing campaigns reach connected prospective students. For a university with 5,000 incoming students, a 5% improvement in yield prediction saves $2-4M in misallocated resources.
How KumoRFM solves this
Graph-powered intelligence for education
Kumo connects applicants, programs, demographics, financial aid offers, and marketing touchpoints into an enrollment graph. The GNN learns yield patterns from the applicant network: how peer group decisions correlate, how aid package competitiveness affects yield by demographic segment, and which marketing sequences drive deposits. PQL predicts enrollment counts per program per semester, with enough lead time for resource planning.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
APPLICANTS
| applicant_id | program | status | gpa | test_score |
|---|---|---|---|---|
| APP001 | Computer Science | Admitted | 3.8 | 1420 |
| APP002 | Nursing | Admitted | 3.5 | 1280 |
| APP003 | Business | Admitted | 3.6 | 1350 |
PROGRAMS
| program_id | name | capacity | historical_yield |
|---|---|---|---|
| PROG01 | Computer Science | 400 | 38% |
| PROG02 | Nursing | 200 | 52% |
| PROG03 | Business | 350 | 41% |
DEMOGRAPHICS
| applicant_id | state | income_tier | first_gen |
|---|---|---|---|
| APP001 | California | High | No |
| APP002 | Texas | Medium | Yes |
| APP003 | New York | High | No |
FINANCIAL_AID
| applicant_id | merit_aid | need_aid | total_package |
|---|---|---|---|
| APP001 | $15,000 | $0 | $15,000 |
| APP002 | $8,000 | $12,000 | $20,000 |
| APP003 | $10,000 | $0 | $10,000 |
MARKETING
| applicant_id | campus_visit | email_opens | event_attended |
|---|---|---|---|
| APP001 | Yes | 12 | Open House |
| APP002 | No | 4 | None |
| APP003 | Yes | 8 | Admitted Students Day |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(APPLICANTS.applicant_id, 0, 90, days) FOR EACH PROGRAMS.program_id WHERE APPLICANTS.status = 'Admitted'
Prediction output
Every entity gets a score, updated continuously
| PROGRAM | ADMITTED | PREDICTED_ENROLLED | YIELD_RATE |
|---|---|---|---|
| Computer Science | 1,050 | 415 | 39.5% |
| Nursing | 380 | 205 | 53.9% |
| Business | 860 | 362 | 42.1% |
Understand why
Every prediction includes feature attributions — no black boxes
Program: Computer Science -- Fall 2025 enrollment
Predicted: 415 enrolled (39.5% yield, +1.5% vs historical)
Top contributing features
Campus visit rate above average
42% visited
28% attribution
Aid competitiveness vs peer institutions
Above median
24% attribution
Applicant peer group deposit signals
Strong
20% attribution
Program ranking improvement
+5 spots
16% attribution
Marketing engagement (email + events)
High
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A university with 5,000 incoming students saves $2-4M by predicting enrollment within 2% accuracy per program. Kumo's enrollment graph captures peer group effects, aid competitiveness, and marketing attribution that funnel models miss.
Related use cases
Explore more education use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
