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![Page 1: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/1.jpg)
Using Predictive Modeling To Manage and Shape Your
Enrollments
Kevin CrockettPresident and CEO
February 21, 2008
![Page 2: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/2.jpg)
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
![Page 3: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/3.jpg)
What is predictive modeling and how can it support your
enrollment management
efforts
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Resource scarcity requires enrollment managers to effectively understand and
manage student propensity to enroll/re-enroll
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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
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Predictive modeling(pri*dik*tiv mod*el*ing)
Statistical analysis of past student behavior to simulate future results
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Why is funnel qualification important?
• Focuses scarce time and resources on those students with the greatest propensity to enroll/re-enroll
• 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
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Nationally…enrollment funnel dynamics are changing
Source: Noel-Levitz 2006 Admissions Funnel Report
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Predictive modeling has become more important as the distinction between stages has become blurred
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The ultimate goal is to build a critical mass of “good fit”
students throughout the
enrollment funnel
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How are predictive models built and how well do they
work?
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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
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Modeling converts each trait or behavior into a statistical value
Sample inquiry model
Relative Strength of Model Variables
27.7%
23.4%8.7%
12.6%
10.1%
7.4%
6.0%
4.2%
Initial Source Code (27.7%)
First Major as Inquiry (23.4%)
Enrollment Planning Service Code (8.7%)Categorized Days as Inquiry (12.6%)
Email Indicator (10.1%)
Categorized Income (7.4%)
SCF Code (6%)
Prob. of "Mainstream Families" Group (4.2%)
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Sample admitted student model
Relative Strength of Model Variables
20.9%
24.3%
22.4%
11.5%
9.1%
4.0%
4.3%
3.4% Enrollment Planning Service Code (20.9%)Campus Visit Flag (24.3%)
Categorized No. of Days as Admit (22.4%)SAT Composite Score (11.5%)
Primary Academic Interest (9.1%)
Binned Distance from Campus (4%)
Multiple Self-Initiated Contacts Flag (4.3%)Prob. of "Settled In" Cluster (3.4%)
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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
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Sample model performance chart
60% of non-enrollers scored <.30 while less than 4% of enrollers had these scores
Distribution of Model Scores
0%5%
10%15%20%25%30%35%40%45%
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Model Score
Enrolled
Not Enrolled
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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
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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%
Total 81,057 7,449 9.2% 2,917 3.6%
At .90 or greater, 11% of the inquiry pool produced 67%
of the applications and 78% of the enrolled students.
At .90 or greater, 11% of the inquiry pool produced 67% of the applications and 78% of the enrolled students.
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Fall 2007 average client model performance
83% of the deposited students came from the highest scoring 45% of the inquiry pool.
83% of the deposited students came from the highest scoring 45% of the inquiry pool.
Score Range Inquiry Applicant AdmitGross
DepositApplicant/ 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
Total/Average 1001426 136774 99148 31162 13.7% 9.9% 3.1% 1.00 1.00 1.00
7% of the deposited students came from the lowest scoring 34% of the inquiry pool
7% of the deposited students came from the lowest scoring 34% of the inquiry pool
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Applying predictive modeling
technology to your marketing and
recruitment program
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Increase the size of your inquiry pool through more effective mining of your prospect pool (pre-prospect and prospect models)
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Assign communication channels based on propensity to enroll
Strategically created groups
Lowest interest
Most interested
Web site
E-newsletters/ communications
Direct mail
Student calls
Professional staff
Alumni
Faculty
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Shape enrollment through targeted communication campaigns
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Focus admissions travel
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Applying predictive
modeling to your retention efforts
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We have found that blending a predictive model with data gleaned from a motivation/attitudinal survey
produces a powerful data combination
Relative Strength of M
odel Variables
26.8%
12.5%
13.0%
12.2%
10.1%
10.7%
8.2%
6.6%
Percent Financial Need Met-Binned (2
6.8%)
High School GPA-Binned (12.5%)
Total Family Contribution-Binned (1
3%)
Average Household Income (12.2%)
Days as Admit-Binned (1
0.1%)
Commuter Flag (10.7%)
Distance from Campus (8
.2%)
County Code (6.6%)
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The predictive model provides OBSERVED risk factors
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While the motivation survey produces ACKOWLEDGED risk factors
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Risk categories can be used to design both programmatic and
student-specific interventions
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It is critical in this approach that you blend the observed and acknowledged risk factors to
create an agenda for action
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Implementation of this combined approach improved retention rates across entry terms
and campuses for this institution
Campus Fall 04 Fall 05 Change Spring 05 Spring 06 Change Summer 05 Summer 06 ChangeCampus 1 65.5 71.0 5.5 64.7 67.6 2.9 76.0 82.1 6.1Campus 2 65.1 61.1 -4.0 64.6 68.9 4.3 77.4 72.0 -5.4Campus 3 61.5 58.9 -2.6 48.3 58.2 9.9 67.1 58.1 -9.0
Campus 4 67.5 66.5 -1.0 48.0 60.2 12.2 69.4 74.3 4.9
Campus 5 56.1 58.4 2.3 46.2 59.0 12.8 49.7 57.2 7.5
Campus 6 60.2 79.7 19.5 52.2 64.6 12.4 72.0 82.2 10.2Campus 7 63.7 72.8 9.1 68.2 71.3 3.1 60.5 76.2 15.7Campus 8 68.3 80.2 11.9 56.6 67.8 11.2 74.2 87.1 12.9Campus 9 54.9 58.7 3.8 45.5 62.0 16.5 73.7 78.3 4.6
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Some concluding thoughts
![Page 33: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/33.jpg)
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
![Page 34: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/34.jpg)
Identify a resource to develop your institution-specific models and score your current files
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Establish project goals and aggressively measure your results…remember the goal is to
beat the model!
![Page 36: Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008.](https://reader033.fdocuments.in/reader033/viewer/2022050805/56649f1c5503460f94c32c03/html5/thumbnails/36.jpg)
Use the modeling process to improve data collection and data management protocols on
your campus….
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…while most schools have reasonably good data on student characteristics, the weakness tends to
be in tracking student behavior
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Observations and questions
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