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Enrollment

Grow enrollment and net tuition, and recruit more efficiently

Forecast the class against the market, process applications, transcripts, and transfer credit, and plan for every student.

Built for VP Enrollment

Why enrollment is hard to run today

1. Every call runs on a partial picture

Each system records transactions, so simple questions take a week of exports.

CRMInquiries, events, and visitsexport
Processing queueApplications and transcriptsre-key
Student systemAdmits and depositsforward
InboxesCounselor conversationscopy
SpreadsheetsMarket data and goals

2. Students wait weeks while their files sit in a queue

Applications, transcripts, and transfer credit are keyed and checked by hand.

SubmittedApplication and transcripts arrive
QueuedKeyed and checked by hand
EvaluatedTransfer credit, course by course
DecidedWeeks later, if the student is still waiting

3. A drop in applications can be a gain

Without the market backdrop, teams chase the wrong problem.

鈭�5%Your applications from one market
鈭�12%That market鈥檚 college-bound graduates

You gained share in a market that shrank

Illustrative

2 路 The ontology we build

The enrollment model

We bring your systems, outside data, and policies together, model the domain, and compute what it runs on.

Enrollment ontology: entering class, segments, market, prospects, applications, transcripts, and recruitingmakes upbelongs tolives indeliversreachestestsassignssubmitscompletesincludeslistscounts asSegmentpopulationenumresidencyenumtargetintegerEntering classtermtermgoalintegercensus_datedateclass_forecastintegerMarketregiongeohs_graduatesintegercollege_going_ratepercent茠market_sharepercentCampaigntacticenumaudienceenumcostmoney茠added_deposit_costmoneyTouchpointchannelenumoccurred_ondaterespondedbooleanProspectstageenumfirst_sourceenumintended_majorstringlikely_to_enrollpercentnext_best_steptouch茠behind_pacebooleanApplicationentry_termtermsubmitted_ondatedecisionenum茠missing_itemsintegerRelease decisionTranscriptinstitutionstringreceived_ondategpadecimal茠transfer_creditsdecimalExperimenthypothesistextmetricenumends_ondate茠measured_liftpercentMilestonekindenumdue_bydatecompleted_ondateCoursesubjectstringnumberstringcreditsdecimalPrior coursetitlestringcreditsdecimalgradestringApprove equivalencyML model 路 Entering classclass_forecastCensus enrollment for each segment,with a range, refreshed daily.From prospect, application, and marketBacktested on past cyclesML model 路 Prospectnext_best_stepThe touch most likely to work next.From experiment, touchpoint andmilestoneUplift model, holdout tested茠Expression 路 Transcripttransfer_creditssum(prior_courses  where grade 鈮� C  鈫� counts_as.credits)Your systemsOutside dataPolicies and memoryAdmissions CRMStudent information systemTranscripts and documentsGraduate projectionsNCES and IPEDSNational Student ClearinghouseAdmission policyTransfer credit rulesCounselor notes and email

A slice of the full model: the labeled objects are the ones this work runs on most.

Object

Prospect

A person considering the institution, from first inquiry through census.

Properties and logic
stage
enum
first_source
enum
intended_major
string
likely_to_enroll
Chance this prospect enrolls by census. From touchpoint, milestone, application, and market.
next_best_step
The touch most likely to work next. From experiment, touchpoint, and milestone.
茠behind_pace
milestones completed < expected_by(stage, days_to_census)
Relationships
  • belongs toSegmentone
  • lives inMarketone
  • is reached byTouchpointmany
  • is assigned toExperimentmany
  • submitsApplicationmany
  • completesMilestonemany
Fed by
Admissions CRMNational Student ClearinghouseCounselor notes and email

What it enables

3 路 What we deploy on top

Applications, agents, and workflows on the model

Each one reads the model and changes it only through governed actions.

App

Enrollment HQ

Where the enrollment team runs the cycle: the class forecast against goal by segment, every market鈥檚 share, prospects behind pace, and live experiments.

Reads
Acts through
Release decision

Release decision: Director signs off on exceptions to policy

Agent

Recruiting agent

Works the prospects who are behind pace. It sends the next best step within approved messages and logs every touch back to the model.

Reads

Counselors approve the messages it can send

Workflow

Application, transcript, and transfer credit processing

Reads every application and transcript, matches prior courses to equivalencies, and computes transfer credit. Exceptions go to an evaluator.

Reads
Acts through
Approve equivalencyRelease decision

Approve equivalency: Department chair for any new equivalency; Release decision: Director signs off on exceptions to policy

4 路 Outcomes

What the enrollment model lets you do

1. Forecast against the market backdrop

Share shows next to raw counts, so down 5% in a market that fell 12% reads as a gain

Enrollment leadership
What the model sees

Your applications and deposits set against each market鈥檚 graduates, college-goers, and where they enroll

2. Process every application, transcript, and transfer credit evaluation

Decisions go out faster, and every student gets an academic index that feeds D, F, and W forecasts downstream

Admissions and registrar
What the model sees

Every application, transcript, and course, read and structured

3. See who is falling behind, and the best next step

Your team or agents get the next step that matters most for each student

Your team decides
What the model sees

Each student鈥檚 progress against the milestones that predict enrolling

4. Run experiments across the funnel and measure them

Every campaign, message, or outreach change shows its real impact

Enrollment leadership
What the model sees

Holdouts and matched groups, tracked through census

5. Learn which recruiting tactics work, and feed them back

What works becomes part of the model and next cycle鈥檚 plans

Enrollment leadership
What the model sees

The visits, fairs, messages, and campaigns that moved students, by segment

How we measure it

Projected value when a change goes live, and realized value against a holdout, a matched group, or the prior cycle, reported separately.

Net tuition per added student, every year they stay

Projected net tuition against goal

Likelihood to enroll when a file is complete within 14 days

Start with Enrollment

We scope the first domain with your team and agree on the outcomes to measure.