Using Predictive Modeling To Manage and Shape Your

Enrollments

Kevin CrockettPresident 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/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

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

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%)

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%)

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

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

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%

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.

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

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

Lowest interest

Most interested

Web site

E-mail

E-newsletters/ communications

Direct mail

Student calls

Professional staff

Alumni

Faculty

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

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%)

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 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

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 institution-specific 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