Predictive ModelingPredictive Modeling

The Key to Enrollment ManagementThe Key to Enrollment Management

GISEMGISEMNancy G. McDuffNancy G. McDuff

October 22, 2006October 22, 2006

What is Predictive ModelingWhat is Predictive Modeling

Predicts the behavior of studentsPredicts the behavior of students– How many will enroll?How many will enroll?– Who will enroll?Who will enroll?– Who will retain?Who will retain?– How much it costs to attract/keep a student?How much it costs to attract/keep a student?– Who will graduate?Who will graduate?– What they will study?What they will study?

Predictive modeling: A short Predictive modeling: A short definitiondefinition

Statistical analysis of past behavior to Statistical analysis of past behavior to simulate future results. For admitted simulate future results. For admitted students, the probability that a student will students, the probability that a student will enroll can be determined by shared enroll can be determined by shared characteristics and behaviors of students characteristics and behaviors of students who have enrolled in the past.who have enrolled in the past.

From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

Why is it ImportantWhy is it Important

PlanningPlanning– SpaceSpace– Academic ServiceAcademic Service– Auxiliary servicesAuxiliary services

Budgeting — costs and revenueBudgeting — costs and revenue

Setting and meeting goalsSetting and meeting goals

Basic Uses of Predictive ModelingBasic Uses of Predictive Modeling

How many offers of admission should be How many offers of admission should be made to enroll a certain size classmade to enroll a certain size class

How many offers must be made to achieve How many offers must be made to achieve certain characteristics of the classcertain characteristics of the class

How many students will graduate How many students will graduate

How many students will attriteHow many students will attrite

How much scholarship do you need to How much scholarship do you need to offeroffer

How do you get StartedHow do you get Started

What do you need to know (what What do you need to know (what questions are being asked)questions are being asked)

What do you knowWhat do you know

What do you wish you knowWhat do you wish you know

What is predictable What is predictable

What predictsWhat predicts

Data, Data, DataData, Data, Data

DataData

Develop tools and Develop tools and techniques to manage techniques to manage informationinformation

Decide what to collectDecide what to collect– Don’t over/under collectDon’t over/under collect

Identify where to find itIdentify where to find it– Student appStudent app– College BoardCollege Board– StateState

Determine where to store Determine where to store itit

Decide how to use itDecide how to use it

From Data to StrategyFrom Data to Strategy

Data are raw materialData are raw material

Information is refined by variable analysisInformation is refined by variable analysis– Residency, demographicsResidency, demographics

Refined information provides energy sources enabling Refined information provides energy sources enabling knowledgeknowledge– Trends, growth patterns, yieldsTrends, growth patterns, yields

Knowledge makes it possible to create strategies Knowledge makes it possible to create strategies – Marketing strategies, targeting, yield eventsMarketing strategies, targeting, yield events

Start with what you knowStart with what you know

What characteristics predict wellWhat characteristics predict well

What do you have historicallyWhat do you have historically

What are good correlatesWhat are good correlates

How comfortable are you with statisticsHow comfortable are you with statistics

Tips and SecretsTips and Secrets

Be ConservativeBe Conservative

Three models surrounding the most likely Three models surrounding the most likely casecase

Define carefullyDefine carefully

Be ConsistentBe Consistent

Give them what you know, not always Give them what you know, not always what they askwhat they ask

Questions affecting the modelQuestions affecting the model

What is the optimum tuition charge and What is the optimum tuition charge and enrollment mixenrollment mix

How many seats will you need in a How many seats will you need in a major/schoolmajor/school

How many students will live on campusHow many students will live on campus

How many students will drop classesHow many students will drop classes

Should you build a new residence hallShould you build a new residence hall

More Advanced Predictive More Advanced Predictive ModelingModeling

ACES Validity StudyACES Validity Study

Non Cognitive Variables in AdmissionsNon Cognitive Variables in Admissions

Predicting Demand for MajorsPredicting Demand for Majors

LOGIT model for enrollmentLOGIT model for enrollment

What are good predictorsWhat are good predictors

History is usually a good predictorHistory is usually a good predictor

Sometimes there are unusual correlatesSometimes there are unusual correlates

Must start with archived data or beginning to Must start with archived data or beginning to develop history….but of whatdevelop history….but of what

Numbers are good, but percentages are betterNumbers are good, but percentages are better

Enrollment ExampleEnrollment Example

Enrollment equalsEnrollment equals– Current enrollmentCurrent enrollment– Less attritionLess attrition– Less graduating studentsLess graduating students– Plus new students Plus new students

Predictive modeling isPredictive modeling is– Current + changes = NewCurrent + changes = New– Or inputs – outputs = Net loss/gainOr inputs – outputs = Net loss/gain

How to Determine EnrollmentHow to Determine Enrollment

Current EnrollmentCurrent Enrollment

Less AttritionLess Attrition

Less GraduatesLess Graduates

Plus New StudentsPlus New Students

Equals New EnrollmentEquals New Enrollment

Predicting EnrollmentPredicting Enrollment

Fall Fall EnrollmentEnrollment

- Attrition- Attrition - Graduates- Graduates + New+ New = = ReturningReturning

(next year)(next year)

FreshmenFreshmen 500500 10% (50)10% (50) 00 20 (4%)20 (4%) [500][500]

SophomoreSophomore 600600 5% (30)5% (30) 00 50 (8%)50 (8%) 470470

JuniorJunior 500500 5% (25)5% (25) 5% (25)5% (25) 20 (4%)20 (4%) 620620

SeniorSenior 450450 2% (9)2% (9) 80% (360)80% (360) 10 (2%)10 (2%) 470470

9191

TotalTotal 20502050 114 114 (5.6%)(5.6%)

385 (19%385 (19% 100 100 (4.9%)(4.9%)

1650 + 1650 + 500500

Predicting the Freshman ClassPredicting the Freshman Class

HistoryHistory AssumptionsAssumptions ActualActual

52%, 56%, 63%, 60%, 52%, 56%, 63%, 60%, 67% yield67% yield

60% average yield, 60% average yield, 1200 enrolled is target1200 enrolled is target

Make 2000 OffersMake 2000 Offers

Two years of history Two years of history only, 65% and 85%only, 65% and 85%

75% of offers send 75% of offers send depositsdeposits

1600 deposits (80%)1600 deposits (80%)

No HistoryNo History 90% of those with 90% of those with deposits attend deposits attend orientationorientation

1400 attended 1400 attended orientation (88%)orientation (88%)

So How Many Will EnrollSo How Many Will Enroll

Did the averages workDid the averages work

What other indicators are thereWhat other indicators are there– Housing ContractsHousing Contracts– Financial Aid/Scholarships AcceptsFinancial Aid/Scholarships Accepts– RegistrationsRegistrations– Meal ContractsMeal Contracts

What Do You PredictWhat Do You Predict

Looking at History Different WaysLooking at History Different Ways

0%

10%

20%

30%

40%

50%

60%

70%

2001 2002 2003 2004 2005

Are you ready for the next generation Are you ready for the next generation of students?of students?

Between 1995 and 2015, 20% more students are projected to enroll Between 1995 and 2015, 20% more students are projected to enroll in U.S. colleges and universitiesin U.S. colleges and universities

80% of the increase in college-aged students between 1995 and 80% of the increase in college-aged students between 1995 and 2015 will be under-represented students2015 will be under-represented students

Business week (2004) 40% of the increase in the college age Business week (2004) 40% of the increase in the college age population will be in the bottom income quartilepopulation will be in the bottom income quartile

The South will have the largest growth at 18.7% by 2017-18The South will have the largest growth at 18.7% by 2017-18

Georgia can expect between 26% and 45% growth in H.S. gradsGeorgia can expect between 26% and 45% growth in H.S. grads

From: Noel-Levitz. “Doing More With Less: Building Efficiencies and Effectiveness into Your Enrollment Management Program”, WICHE “Knocking at the College Door”

What factors influence What factors influence college choice/retention?college choice/retention?

Academic reputationAcademic reputation

Rankings/SelectivityRankings/Selectivity

Institution typeInstitution type

SizeSize

Proximity to homeProximity to home

AmenitiesAmenities

Quality of student lifeQuality of student life

SafetySafety

Personal touch/RelationshipsPersonal touch/Relationships

Class size & student to faculty Class size & student to faculty ratioratio

Academic programs (study-Academic programs (study-abroad, learning communities, abroad, learning communities, Honors)Honors)

Programs of studyPrograms of study

State and institutional financial State and institutional financial assistanceassistance

Receiving scholarshipsReceiving scholarships

Campus visitsCampus visits

Athletics/Campus AppearanceAthletics/Campus Appearance

Challenges facing institutionsChallenges facing institutions

Fluctuating economyFluctuating economy

Fewer students with the ability to pay for the Fewer students with the ability to pay for the increasing costs of higher educationincreasing costs of higher education

Strong scholarship, grant, and need-based aid Strong scholarship, grant, and need-based aid programs to attract students are becoming more programs to attract students are becoming more prevalentprevalent

Static endowments and state support for higher Static endowments and state support for higher educationeducation

From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

Challenges Facing Institutions Cont.Challenges Facing Institutions Cont.

Operating in an increasingly competitive Operating in an increasingly competitive environmentenvironment

Changing demographicsChanging demographics

More aggressive marketing and recruiting by More aggressive marketing and recruiting by both public and private institutionsboth public and private institutions

More sophisticated marketplace…plans, More sophisticated marketplace…plans, systems, and advanced tools being developedsystems, and advanced tools being developed

From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

Challenges of Predictive Challenges of Predictive ModelingModeling

Can lead horseCan lead horse

Models need to be Models need to be developed over time – developed over time – numerous years numerous years

Models can alter by Models can alter by changes in policieschanges in policies– Financial aidFinancial aid– TuitionTuition

Models can be costly – Models can be costly – time, accuracy, moneytime, accuracy, money

Modeling usually is Modeling usually is homogeneous (a model homogeneous (a model for freshmen recruiting for freshmen recruiting usually would not fully usually would not fully apply to transfers.)apply to transfers.)

ChallengesChallenges

ChallengesChallenges

ChallengesChallenges

Summary and ConclusionsSummary and Conclusions

Modeling is only part of the puzzle.Modeling is only part of the puzzle.

Use multiple modes of recruitmentUse multiple modes of recruitment

Predictive modeling provides a sense of the data Predictive modeling provides a sense of the data pool accuracy – but inputs must be correctpool accuracy – but inputs must be correct

One can leverage enrollment by finances and One can leverage enrollment by finances and characteristicscharacteristics

Resources and ReferencesResources and References

https://ra.collegeboard.com/https://ra.collegeboard.com/ Enrollment Planning Services Enrollment Planning Services

www.nslc.orgwww.nslc.org// National Student ClearinghouseNational Student Clearinghouse

http://www.amstat.org/index.cfm?fuseaction=mainhttp://www.amstat.org/index.cfm?fuseaction=main American Statistical American Statistical AssociationAssociation

http://www.collegeresults.org/http://www.collegeresults.org/ The Education Trust The Education Trust

https://www.noellevitz.comhttps://www.noellevitz.com Noel-Levitz Noel-Levitz

http://www.airweb.org/http://www.airweb.org/ Association for Institutional Research Association for Institutional Research

Hopkins, K. Noel-Levitz. (2003, July). “Building and Developing an Effective Hopkins, K. Noel-Levitz. (2003, July). “Building and Developing an Effective Enrollment Management Enrollment Management Plan for Colleges and Universities.” National Plan for Colleges and Universities.” National Conference on Student Retention. Conference on Student Retention.

Topor & Associates. Topor & Associates. A Contemporary Approach to Marketing Higher A Contemporary Approach to Marketing Higher Education.Education.