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Conference presentation from the Texas Association of Graduate Admissions Professionals (TxGAP) 2012 Professional Development Conference. Author: Jeancarlo Bonilla Director of Graduate Enrollment Management Polytechnic Institute of New York University Description: Learn how to use predictive modeling techniques and apply them to the area of graduate enrollment management. For more information, visit www.txgap.com.
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Running the Numbers: Improving Your Position for Enrollment Planning and
Forecasting”
TxGAP – Summer Conference July 20th, 2012
JeanCarlo (J.C) Bonilla
Director of Graduate Enrollment Management New York University, Polytechnic Institute
The Plan for the Session: • Overview of predictive modeling & optimization for enrollment
management • Case 1: Do cycles have memory? The case of the 3-yr adjusted yield (4
examples) • Case 2: Am I making my class? Modeling for scenarios forecasting (3
examples) • Case 3: The magic ball, ranking and an enrollment predictor (2 examples) • Case 4: Opps, I ran out of time, but this is a very cool model
Worksheets download at EnrollmentAnalytics.com
A little bit about me & where I work...
Where is the industry today with the idea of business analytics &
intelligence?
Standard Reports
“what happened”
Ad hoc Report “how many, how often, where”
Query “what is exactly the problem”
Alert “what actions are
required”
Degree of intelligence
Descriptive Analytics
Statistical Model
“why is this happening”
Randomized testing
“what happens if we try this”
Predictive Model/Forecast
“what will happened next”
Optimization “what is the best
that can happened”
Degree of intelligence
Predictive Analytics
Informatics/Analytics industry is moving
from small data to big data from data
analytics to data scientist
So, what do we know so far about
predictive modeling for Enrollment Management?
TACTIC
SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW
Standard Reports “what happened”
Ad hoc Report “how many, how often, where”
Query “what is exactly the
problem”
Alert “what actions are
required”
TACTIC
Statistical Model “why is this happening”
Random Testing “what happens if we
try this”
Predict & Forecast
“what will happened next”
Optimization “what is the best that
can happened”
Predictive Analytics
SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW
Examples of Enrollment Predictive Modeling • Case 1: North Dakota University – Type of Model: inquiry model using geo-demographic
– Predictive Power: 36% of students who will enroll & 97% of student who will not enrolled
Examples of Enrollment Predictive Modeling • Case 2: University of Minnesota – Type of Model: application generation model using, ACT and geo-
demographic information
– Predictive Power: 85% of applicants to a “large research university” are from within the same state or form a neighboring state
Examples of Enrollment Predictive Modeling: • Case 3: State University of New York – Type of Model: lead modeling using geo-demographic, academic data,
and financial aid data
– Predictive Power: 45.67% of applicants predicted to enroll did in fact matriculate and 82.16% who where predicted not to enroll did not matriculate
Better predictive power with students who
do not matriculate than with model that forecast actual students enrollments
The “technique” is used in other consolidated markets... if it works for them, it should work for us!
It requires quantitative analysis of past student characteristics to predict probabilities of
future results
Your predictive modeling team should have people who are confortable doing:
The modeling guy: 1. Regression Analysis (logistic regression)
2. Business analytics
The computer guy: 1. Database architecture & design
2. Database querying 3. Data aggregation & integration
4. Data reporting
Access to historical data is required!
Modeling 101: Defining Model Attributes
Student Behavior (influences, emotions,
competition)
Student Characteristics (geo-demographic, academic,
financial aid)
...and off course, there is a problem with that!
SUSPECTS> PROSPECTIVE> APPLICANTS> ADMITS> DEPOSITS> NEW
Stealth Applications 30%-40% of adult students
Source: Aslanian Market Research
...an approach for predictive modeling in enrollment management
Applicants Prospective Students
New students Applicants
CASE#1:
Do cycles have memory? The case of the 3-yr adjusted yield
Predictions through the admissions funnel
Download worksheets at: www.EnrollmentAnalytics.com
Recommendations
1. Rapid “back-of-the-envelop” modeling
2. You can go “up” or “down” the funnel
3. Need for historical data (static snapshots of cycles)
4. Student characteristics add more resolution to the model
5. Use of adjusted 3-year cycles are useful for historical modeling
6. Historical validity: account for new initiatives
CASE#2: Am I making my class?
New Student forecaster
Download worksheets at: www.EnrollmentAnalytics.com
CASE#3: The magic ball
ranking and an enrollment predictor (2 examples)
Download worksheets at: www.EnrollmentAnalytics.com
5-Stage Admissions Funnel
Prospects
Applications
Admits
Deposits
New 2%
Example: say that you have 20k leads in your cycle and only 300 matriculate, then you have a
2% conversion rate
Now, you “observe” that 200 out of your 300 new students presents a subset of 5% of your
prospective students pool. This means that 1000 prospective students (5% of 20k) converted into 200 enrollments, which
means that your conversion rate for this subset is 20%
95%
5%
33%
67%
20,000 prospective students
300 new students
New conversion rate or predictability of 20%
20,000 prospective students
300 new students
FT-‐Dom, 20%
PT, 40%
FT-‐Int'l, 40%
FT-‐Dom, 5%
PT, 40% FT-‐Int'l, 55%
..and if you get really good at understanding your students...
Build a model that does the following:
Student Name Status Predictor
Hall, Joy
Inquiry 11/16/10
0.4 App. N/A Adm. N/A Conf. N/A Enr. N/A
Li, Xiao
Inquiry 12/22/10
0.6 App. 12/24/10 Adm. 3/23/11 Conf. 4/2/11 Enr. N/A
Lopez, Jose
Inquiry 12/5/10
0.2 App. 1/5/11 Adm. 1/29/11 Conf. 3/16/11 Enr. N/A
Mitchell, Tamara
Inquiry 12/20/10
0.2 App. 2/3/11 Adm. N/A Conf. N/A Enr. N/A
Smith, John
Inquiry 1/26/11
0.4 App. 1/28/11 Adm. 4/16/11 Conf. 5/5/11 Enr. N/A
Troy, Bryan
Inquiry 12/13/10
0.9 App. N/A Adm. N/A Conf. N/A Enr. N/A
So, how can I build a model like that predicts enrollments?
Student Uncertainty & Variance
Academic
Financial
Geographical
Demographical
Behavior & Personal Life
FT vs intl vs PT
Recommendations for Advanced Models
1. It gets complicated
2. Its “easy” to model for student characteristics, but complexity increases when accounting for student behavior
3. Models are better at predicting student who do NOT register
4. Every school is different, so every model is also different
5. Trust your instincts! No one knows students better than you... Your job is then trying to articulate and generalized characteristics and behavior
CASE#4: Opps, I ran out of time, but this is a very
cool model
Download worksheets at: www.EnrollmentAnalytics.com
Final Recommendations
1. Plan for good, bad, and what you think is going to realistic
2. Avoid predictions but give options
3. Its about resource allocation
4. Work with other groups in your institution
5. Trust your GEM instincts
6. Its earsier to account for student characteristics, but modeling and forecasting behavior is very complex
