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Predictive Modeling. The Key to Enrollment Management GISEM Nancy G. McDuff October 22, 2006. What is Predictive Modeling. Predicts the behavior of students How many will enroll? Who will enroll? Who will retain? How much it costs to attract/keep a student? Who will graduate?

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Predictive Modeling

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predictive modeling

Predictive Modeling

The Key to Enrollment Management


Nancy G. McDuff

October 22, 2006

what is predictive modeling
What is Predictive Modeling
  • Predicts the behavior of students
    • How many will enroll?
    • Who will enroll?
    • Who will retain?
    • How much it costs to attract/keep a student?
    • Who will graduate?
    • What they will study?
predictive modeling a short definition
Predictive modeling: A short definition

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

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

why is it important
Why is it Important
  • Planning
    • Space
    • Academic Service
    • Auxiliary services
  • Budgeting — costs and revenue
  • Setting and meeting goals
basic uses of predictive modeling
Basic Uses of Predictive Modeling
  • How many offers of admission should be made to enroll a certain size class
  • How many offers must be made to achieve certain characteristics of the class
  • How many students will graduate
  • How many students will attrite
  • How much scholarship do you need to offer
how do you get started
How do you get Started
  • What do you need to know (what questions are being asked)
  • What do you know
  • What do you wish you know
  • What is predictable
  • What predicts
  • Data, Data, Data
Develop tools and techniques to manage information

Decide what to collect

Don’t over/under collect

Identify where to find it

Student app

College Board


Determine where to store it

Decide how to use it

from data to strategy
From Data to Strategy
  • Data are raw material
  • Information is refined by variable analysis
    • Residency, demographics
  • Refined information provides energy sources enabling knowledge
    • Trends, growth patterns, yields
  • Knowledge makes it possible to create strategies
    • Marketing strategies, targeting, yield events
start with what you know
Start with what you know
  • What characteristics predict well
  • What do you have historically
  • What are good correlates
  • How comfortable are you with statistics
tips and secrets
Tips and Secrets
  • Be Conservative
  • Three models surrounding the most likely case
  • Define carefully
  • Be Consistent
  • Give them what you know, not always what they ask
questions affecting the model
Questions affecting the model
  • What is the optimum tuition charge and enrollment mix
  • How many seats will you need in a major/school
  • How many students will live on campus
  • How many students will drop classes
  • Should you build a new residence hall
more advanced predictive modeling
More Advanced Predictive Modeling
  • ACES Validity Study
  • Non Cognitive Variables in Admissions
  • Predicting Demand for Majors
  • LOGIT model for enrollment
what are good predictors
What are good predictors
  • History is usually a good predictor
  • Sometimes there are unusual correlates
  • Must start with archived data or beginning to develop history….but of what
  • Numbers are good, but percentages are better
enrollment example
Enrollment Example
  • Enrollment equals
    • Current enrollment
    • Less attrition
    • Less graduating students
    • Plus new students
  • Predictive modeling is
    • Current + changes = New
    • Or inputs – outputs = Net loss/gain
how to determine enrollment
How to Determine Enrollment

Current Enrollment

Less Attrition

Less Graduates

Plus New Students

Equals New Enrollment

so how many will enroll
So How Many Will Enroll
  • Did the averages work
  • What other indicators are there
    • Housing Contracts
    • Financial Aid/Scholarships Accepts
    • Registrations
    • Meal Contracts
are you ready for the next generation of students
Are you ready for the next generation of students?
  • Between 1995 and 2015, 20% more students are projected to enroll in U.S. colleges and universities
  • 80% of the increase in college-aged students between 1995 and 2015 will be under-represented students
  • Business week (2004) 40% of the increase in the college age population will be in the bottom income quartile
  • The South will have the largest growth at 18.7% by 2017-18
  • Georgia 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 college choice retention
Academic reputation


Institution type


Proximity to home


Quality of student life


Personal touch/Relationships

Class size & student to faculty ratio

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

Programs of study

State and institutional financial assistance

Receiving scholarships

Campus visits

Athletics/Campus Appearance

What factors influence college choice/retention?
challenges facing institutions
Challenges facing institutions
  • Fluctuating economy
  • Fewer students with the ability to pay for the increasing costs of higher education
  • Strong scholarship, grant, and need-based aid programs to attract students are becoming more prevalent
  • Static endowments and state support for higher education

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 environment
  • Changing demographics
  • More aggressive marketing and recruiting by both public and private institutions
  • More sophisticated marketplace…plans, systems, and advanced tools being developed

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

challenges of predictive modeling
Can lead horse

Models need to be developed over time – numerous years

Models can alter by changes in policies

Financial aid


Models can be costly – time, accuracy, money

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

Challenges of Predictive Modeling
summary and conclusions
Summary and Conclusions
  • Modeling is only part of the puzzle.
  • Use multiple modes of recruitment
  • Predictive modeling provides a sense of the data pool accuracy – but inputs must be correct
  • One can leverage enrollment by finances and characteristics
resources and references
Resources and References
  • Enrollment Planning Services
  • Student Clearinghouse
  • American Statistical Association
  • The Education Trust
  • Noel-Levitz
  • Association for Institutional Research
  • Hopkins, K. Noel-Levitz. (2003, July). “Building and Developing an Effective Enrollment Management Plan for Colleges and Universities.” National Conference on Student Retention.
  • Topor & Associates. A Contemporary Approach to Marketing Higher Education.