Using Predictive Modeling
To Manage and Shape Your
Enrollments
Kevin Crockett
President and CEO
February 21, 2008
According to the 2008 Institutional Fact Finders
submitted in preparation for this conference…
• 14% of institutional respondents reported using
predictive modeling in their marketing and
recruitment programs
• 36% reported that they systematically contact
inquiries to code their level of interest
• 29% reported that they use data analysis to
predict dropout proneness
What is predictive
modeling and how
can it support your
enrollment
management
efforts
Resource scarcity requires enrollment
managers to effectively understand and
manage student propensity to enroll/re-enroll
Means of qualifying student interest in and
commitment to your institution
•
•
•
•
•
•
Research/data analysis
Tracking student contacts/behavior
Telecommunications
Personal contact
Reply mechanisms in all correspondence
Predictive modeling
Predictive modeling
(pri*dik*tiv mod*el*ing)
Statistical analysis of past student
behavior to simulate future results
Why is funnel qualification important?
• Focuses scarce time and resources on those
students with the greatest propensity to enroll/reenroll
• Facilitates better relationship-building
• Enables university staff and advocates to follow-up
with students that are genuinely interested in your
school
• Provides cost-savings by not communicating equally
with every student
• Enables greater personalization with students
• Increases the precision of enrollment forecasting
Nationally…enrollment funnel dynamics
are changing
Source: Noel-Levitz 2006 Admissions Funnel Report
Predictive modeling has become more important as
the distinction between stages has become blurred
The ultimate goal
is to build a critical
mass of “good fit”
students
throughout the
enrollment funnel
How are predictive
models built and
how well do they
work?
Models can be built from each stage of the enrollment
funnel but they should ultimately predict
enrollment or re-enrollment
Pre-prospect model
Prospect model
Inquiry model
Applicant/admit model
Retention/progression models
Modeling converts each trait or
behavior into a statistical value
Relative Strength of Model Variables
Initial Source Code (27.7%)
4.2%
First Major as Inquiry (23.4%)
6.0%
27.7%
7.4%
10.1%
Enrollment Planning Service Code
(8.7%)
Categorized Days as Inquiry (12.6%)
Email Indicator (10.1%)
Categorized Income (7.4%)
12.6%
8.7%
23.4%
SCF Code (6%)
Prob. of "Mainstream Families" Group
(4.2%)
Sample inquiry model
Sample admitted student model
Relative Strength of Model Variables
3.4%
Enrollment Planning Service Code
(20.9%)
Campus Visit Flag (24.3%)
4.3%
4.0%
20.9%
9.1%
Categorized No. of Days as Admit
(22.4%)
SAT Composite Score (11.5%)
Primary Academic Interest (9.1%)
11.5%
24.3%
22.4%
Binned Distance from Campus (4%)
Multiple Self-Initiated Contacts Flag
(4.3%)
Prob. of "Settled In" Cluster (3.4%)
The “Hold” and “Main” Files
Models should be built using one half of your
historical file so that they can be tested
against the other half of your file
This ensures that you understand the
performance of your model before you
ever use it to prioritize your follow-up with
prospective students
Sample model performance chart
Distribution of Model Scores
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Enrolled
Not Enrolled
0
0.1
0.2 0.3
0.4
0.5 0.6
0.7 0.8
0.9
1
Model Score
60% of non-enrollers scored <.30 while less than
4% of enrollers had these scores
A model’s output
ENROLLED
1
ENROLLED
Kate Black
.99
Highly Likely
Mike Miller
.85
Highly Likely
Dave Hamilton
.72
Likely
Jerrica Zwick
.68
Likely
Angie Mabeus
.46
Somewhat Likely
Audrey Keppler
.41
Somewhat Likely
Brian Schuler
.21
Less Likely
Jordan Clouser
.17
Less Likely
NOT ENROLLED
0
NOT ENROLLED
Sample predictive model performance
Model Score
Inqs
Apps
Conv. %
Enrolled
Yield
0-.20
3,913
21
.5%
4
.1%
.20-.29
9,349
87
.9%
12
.1%
.30-.39
13,772
107
.8%
14
.1%
.40-.49
14,602
172
1.2%
40
.3%
.50-.59
10,369
242
2.3%
56
.5%
.60-.69
9,085
337
3.7%
66
.7%
.70-.79
5,870
512
8.7%
139
2.4%
.80-.89
5,305
1,006
19.0%
297
5.6%
.90-1.0
8,792
4,965
56.5%
2,289
26.0%
81,057
7,449
9.2%
2,917
3.6%
Total
At .90 or greater, 11% of the inquiry pool produced 67%
of the applications and 78% of the enrolled students.
Fall 2007 average client
model performance
Score Range
Inquiry
Applicant
Admit
Gross
Deposit
Applicant
/ Inquiry
Admit/
Inquiry
Gross
Deposit/
Inquiry
Applicant
Lift
Admit
Lift
Gross
Deposit
Lift
0.00-0.09
3452
101
43
10
2.9%
1.2%
0.3%
0.21
0.13
0.09
0.10-0.19
25455
710
304
79
2.8%
1.2%
0.3%
0.20
0.12
0.10
0.20-0.29
101900
3801
2202
466
3.7%
2.2%
0.5%
0.27
0.22
0.15
0.30-0.39
205783
11685
7770
1782
5.7%
3.8%
0.9%
0.42
0.38
0.28
0.40-0.49
216739
18109
12482
3090
8.4%
5.8%
1.4%
0.61
0.58
0.46
0.50-0.59
153786
19813
14017
3891
12.9%
9.1%
2.5%
0.94
0.92
0.81
0.60-0.69
119424
21496
15641
4593
18.0%
13.1%
3.8%
1.32
1.32
1.24
0.70-0.79
86453
22442
16463
5327
26.0%
19.0%
6.2%
1.90
1.92
1.98
0.80-0.89
58264
22035
16804
5927
37.8%
28.8%
10.2%
2.77
2.91
3.27
0.90-1.00
30170
16582
13422
5997
55.0%
44.5%
19.9%
4.02
4.49
6.39
1001426
136774
99148
31162
13.7%
9.9%
3.1%
1.00
1.00
1.00
Total/Average
83% of the deposited students came from
the highest scoring 45% of the inquiry pool.
7% of the deposited students came from
the lowest scoring 34% of the inquiry pool
Applying predictive
modeling
technology to your
marketing and
recruitment
program
Increase the size of your inquiry pool through more
effective mining of your prospect pool
(pre-prospect and prospect models)
Assign communication channels based
on propensity to enroll
Strategically
created groups
Web site
E-mail
E-newsletters/
communications
Direct mail
Student calls
Professional
staff
Alumni
Faculty
Lowest
interest
Most
interested
Shape enrollment through targeted
communication campaigns
Focus admissions travel
Applying
predictive
modeling to your
retention efforts
We have found that blending a predictive model with
data gleaned from a motivation/attitudinal survey
produces a powerful data combination
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The predictive model provides
OBSERVED risk factors
While the motivation survey produces
ACKOWLEDGED risk factors
Risk categories can be used to design both
programmatic and
student-specific interventions
It is critical in this approach that you blend the
observed and acknowledged risk factors to
create an agenda for action
Implementation of this combined approach
improved retention rates across entry terms
and campuses for this institution
Campus
Campus 1
Campus 2
Campus 3
Fall 04 Fall 05 Change Spring 05 Spring 06 Change Summer 05 Summer 06 Change
65.5
71.0
5.5
64.7
67.6
2.9
76.0
82.1
6.1
65.1
61.1
-4.0
64.6
68.9
4.3
77.4
72.0
-5.4
61.5
58.9
-2.6
48.3
58.2
9.9
67.1
58.1
-9.0
69.4
74.3
4.9
49.7
57.2
7.5
64.6
12.2
12.8
12.4
72.0
82.2
68.2
71.3
3.1
60.5
76.2
11.9
56.6
67.8
74.2
87.1
3.8
45.5
62.0
11.2
16.5
10.2
15.7
12.9
73.7
78.3
4.6
Campus 4 67.5
66.5
-1.0
48.0
60.2
Campus 5 56.1
58.4
2.3
46.2
59.0
Campus 6 60.2
79.7
19.5
52.2
Campus 7 63.7
72.8
9.1
Campus 8 68.3
80.2
Campus 9 54.9
58.7
Some concluding
thoughts
Apply modeling to the regions of your funnel that
hold the greatest promise for improving your
enrollment management outcomes
Pre-prospect model
Prospect model
Inquiry model
Applicant/admit model
Retention/progression models
Identify a resource to develop your institutionspecific models and score your current files
Establish project goals and aggressively
measure your results…remember the goal is to
beat the model!
Use the modeling process to improve data
collection and data management protocols on
your campus….
…while most schools have reasonably good data
on student characteristics, the weakness tends to
be in tracking student behavior
Observations and questions