Transcript Understanding Retention

Analytical Tools for Improving Access,
Retention and Net Tuition Revenue
College Board Middle States Regional Forum 2008
Daniel J. Rodas, Long Island University
Heather Gibbs, Long Island University
Jim Scannell, Scannell & Kurz, Inc.
Thursday, February 14, 2008
Overview
Key Objectives
Increase student access
 Improve undergraduate retention
 Increase net tuition revenue
 Achieve key enrollment objectives
 Mission attainment

3
Long Island University

Founded in 1926 as a private, coeducational, non-sectarian institution
 Mission of “Access and Excellence”
 18,600 degree-seeking students
 600 degree and certificate programs
 $360 million operating budget
 $100 million endowment
 $1 billion replacement value/physical
assets
4
Long Island University

Two residential campuses:
Brooklyn
 C.W. Post


Four regional campuses
Brentwood
 Riverhead
 Rockland
 Westchester

653 full-time faculty
 162,000 living alumni

5
Core Methodologies

Quantitative modeling



Table analysis
Regression
Qualitative research



Document analysis
Interviews
Focus groups

Competitive analysis
 Benchmarking of best practices
6
Case Studies
Undergraduate Pricing and Net Tuition
 Graduate Program Review
 Retention and Persistence
 Discussion

7
Discounting Policies &
Practices
Types
Historical/Incremental
 Table Analysis
 Predictive modeling & regression
 Optimization

9
Historical / Incremental

Discounts based on historical experience:
incremental, benchmarking, trial and error
 Tied to a pre-determined discount rate
 Works best in a steady state marketplace
and stable class characteristics
 Problematic under:



Changing market conditions
(e.g. competitors raise scholarship offers)
Rapid tuition growth; and/or
Attempts to reconfigure the class profile
(e.g. socio-economic, geographic mix, academic
ability, etc.)
10
Optimization Models



Strategic approach to allocating financial aid
Comprises quantitative techniques to
understand the relationship between grant
and student characteristics on the probability
of enrollment
Tools include:
Table analysis
 Predictive analysis
 Price sensitivity analysis

11
Table Analysis: Example 1
YIELDS FOR A PARTICULAR QUALITY LEVEL
Need

$10,001-$12,000
Aid $ >$7,000
.50
$6,001-7,000
.47
$5,001-6,000
.45
$4,001-5,000
.20
$2,001-4,000
.15
$1-2,000
.11
0
.08
Yields for Quality Level B: SAT 1100-1150
 Probability of enrollment does not increase very much from aid
between $5,000 - $6,000, and aid > $7,000.
*Scannell & Kurz
12
Table Analysis: Example 2
Financial Need
Need
$0
$15,000-$18,000
>$18,000
Grant Aid $
>$12,000
$9,000-12,000
55/100
55%
$6,000-9,000
20/80
25%
$3,000-6,000
8/40
20%
$1,001-3,000
0
•Grant aid for 220 accepted students with need of
$15,000 - $18,000
13
Cost-benefit Analysis
55* ($20,000 - $10,500)= $522,500
20* ($20,000 - $7,500)= $250,000
8* ($20,000 - $4,500)= $124,000
$896,500
14
Scannell & Kurz research 2007
Cost-benefit Analysis

What if all students in this need bracket
received the top financial aid award?
Projected enrollment:
220 * 55% = 121
 Projected net tuition revenue:
121 * ($20,000 - $10,500) = $1,149,500
 Gain in net tuition revenue:
$1,149,500 - $896,500 = $253,000

15
Benefits
$253,000 in additional net tuition revenue
 Critical mass
 Better residence hall utilization
(38 additional students at $4,000 =
$152,000)
 Improved per unit costs in dining
 Bigger enrollment base for future years

16
Predictive Modeling and Price
Sensitivity Analysis

Goals:
Permits multivariate analysis
 Identify factors important in the enrollment
decision
 Determine the impact of institutional grant
on the probability of enrollment
 Determine the revenue-maximizing levels
of grants
 Identify alternative financial aid packaging
strategies
 Suggest alternative admissions policies

17
Predictive Modeling and Price
Sensitivity Analysis

Basic Regression equation:


Probability of Enrolling a student = 
(Student Need, Total Grant, SAT Scores, High School
Grade Point Average, and “Other Student
Characteristics”)
Collapse Quality into groups:






Quality Group 1: SAT 1300+ and High School GPA 92+
Quality Group 2: SAT 1200+ and High School GPA 90+
Quality Group 3: SAT 1100+ and High School GPA 85+
Quality Group 4: SAT 1010+ and High School GPA 80+
Quality Group 5: SAT 900+ and High School GPA 80+
Quality Group 6: all others
18
Predictive Modeling and Price
Sensitivity Analysis

Regression analysis:
Variable
Marginal Effect
Calculation
Explanation
Total Grant
0.0294
For every $1,000 in grant, yield increases by 3%.
Need
-0.0061
For every $1,000 in need, yield decreases by <1%.
FAFSA Filer
0.223
Students filing FAFSA forms are 22% more likely to
enroll.
SAT
-0.00062
For every extra 100 SAT points yield declines by 6.2%.
High School GPA Avg.
-0.0098
For every 10 percentage point increase in HS average
(e.g. 80 to 90), yield decreases by 9.8%.
Apply after 3/31
0.086
Students applying after 3/31 are 8.6% more likely to
enroll.
Minority
-0.08
Minority students are 8% less likely to enroll than
majority students.
County A/County B
0.11
Students from Counties A and B are 11% more likely to
enroll.
Large City HS
-0.038
Students from large city high schools are 4% less likely
to enroll.
19
Price Sensitivity
Simulation Summary Table #1
Enrollment
NTR
Avg. NTR
Institutional Grant
Discount
Avg. SAT
H.S. Avg.
out-of-state
minority
Aid applicants
Predicted
Class
(Baseline) Simulation #1 Simulation #4
945
952
1,074
$16,055,830
$16,401,210
$17,253,490
$17,000
$17,230
$16,070
$5,887,610
$5,706,670
$7,691,520
27.0%
25.8%
30.8%
1007
1008
1001
84
84
84
144
144
162
233
233
289
826
833
955
22
Optimization: Summary

Creation of a data file is key
 Willingness to create new packaging
policies
 Is there slack in your system?
 May require a radical redefinition of how
you package
 Test market versus total roll-out
 Model needs to be updated/refined
annually!
23
Graduate Program Review
Overview
Key Questions
 Methodology
 Observations
 Price Sensitivity Analysis
 Recommendations
 Conclusion

25
Key Questions
1.
Is financial aid being used efficiently and
effectively in support of enrollment goals?
2.
Do recruitment processes ensure sufficient
representation of target populations in the
applicant pool?
3.
Are there appropriate linkages between the
programs and their campuses to ensure
effective service to doctoral students and a
“return on investment” to the University?
26
Methodology
Analysis of data file and departmental
records (3 years)
 Review of off-the-shelf materials
 In-person interviews and focus groups

27
Observations
Enrollment Goals
 Admissions Practices
 Financial Aid Practices
 Competition
 Integration with the Campus
 Summary

28
Enrollment Goals
16-18 new students admitted annually
 Faculty interest in increasing class
diversity
 Desire to improve yield on offers of
admission
 Priority to enroll students who will serve
“underserved” populations (C.W. Post)

29
Admissions Practices
Little to no active effort to recruit
students (primarily “word of mouth”)
 Inefficient use of Web as recruiting tool
 Lack of reports regarding Inquiries
 Little effort to recruit LIU undergrads

30
Financial Aid Practices
Financial Aid offer made after applicant
accepts offer of admission
 Nearly identical level of funding is offered
to all enrollees
 Discount rate declined at both campuses
from Fall 2004 to Fall 2006
 Yield also declined at both campuses
from Fall 2004 to Fall 2006

31
Financial Aid Practices (cont’d)
Brooklyn Campus
2004
2005
2006
Admit
28
30
33
Enroll
16
16
15
57%
53%
45%
Avg Tuition & Fees
$28,800
$30,530
$32,286
Avg Grant
$23,363
$22,138
$23,704
81.1%
72.5%
73.4%
$87,000
$134,280
$128,723
1274
1262
1271
18.8%
25.0%
13.3%
Yield
Discount Rate
Net Tuition Revenue
Avg GRE
% Minority
32
Financial Aid Practices (cont’d)
C.W. Post Campus
2004
2005
2006
Admit
40
46
51
Enroll
16
17
16
40%
37%
31%
Avg Tuition & Fees
$28,800
$30,530
$32,286
Avg Grant
$11,070
$13,586
$10,531
38.4%
44.5%
32.6%
$283,677
$288,050
$348,076
1192
1185
1211
37.5%
23.5%
12.5%
Yield
Discount Rate
Net Tuition Revenue
Avg GRE
% Minority
33
Competition
Limited competition between two
programs
 Both compete with some of the same
institutions in the region
 Some comparison and quality measures:

% of enrollees receiving tuition waivers or
assistantships
 Average GRE scores
 U.S. News rankings
 # of APA approved internship placements

34
Integration with the Campus

Brooklyn program




Highly integrated:
Students work in Psychological Services Center,
which serves campus students
Faculty teach at undergrad and master’s level
C.W. Post program



Largely disconnected
Clinic serves general public, not the campus
Faculty teach exclusively in the PsyD program
35
Summary

Both campuses:
Recruitment resources are not being used
strategically to meet stated goals
 Financial Aid resources are not being used
strategically to meet stated goals
 Price sensitivity must be investigated

36
Price Sensitivity Analysis
Brooklyn Campus
Year
2004
N
2005
Avg NTR
2006
N
Avg NTR
N
Avg NTR
GRE Total Score
1000-1090
-
-
1
$13,630
-
-
1100-1190
3
$733
2
$4,780
1
$6,471
1200-1290
7
$5,057
5
$7,030
7
$8,179
1300-1390
5
$11,560
4
$9,805
4
$10,381
1400+
1
$4,000
2
$5,130
2
$13,735
16
$5,437
14
$7,701
14
$9,480
All
Net Tuition Revenue by Total GRE Score (2004 to 2006)
37
Price Sensitivity Analysis (cont’d)
C.W. Post Campus
Year
2004
2005
2006
N
Avg NTR
N
Avg NTR
N
Avg NTR
no GRE
3
$19,066
2
$14,853
7
$20,428
<1000
1
$9,600
1
$30,530
-
-
1000-1090
1
$21,031
3
$16,960
-
-
1100-1190
4
$10,057
4
$17,070
5
$19,586
1200-1290
5
$20,383
4
$14,853
1
$21,286
1300-1390
2
$26,847
2
$14,853
3
$28,619
1400+
-
-
1
$19,530
-
-
16
$17,729
17
$16,944
16
GRE Total Score
All
$21,754
Net Tuition Revenue by Total GRE Score (2004 to 2006)
39
Recommendations
Use University’s new PeopleSoft system to
capture data beginning at the point of initial
inquiry.
 Follow up with applicants who choose not to
enroll in order to better understand the
competitive environment.
 Increase enrollment in each cohort by one or
two students.
 Use the Web more effectively to
communicate distinctive program features,
including lower cost of 4th year attendance.

41
Conclusion

Institutional investment in its programs is
significant.
 Recruitment efforts and decisions are not
always consistent with expressed goals.
 Institutional resources are not being used
strategically to attract and enroll the
populations of most interest.
42
Understanding and Responding to
Retention Trends
Three Key Questions to Answer
1. How can we identify at-risk students?
2.
How can we determine the impact of
existing policies and programs?
3.
Are there any “image versus reality”
issues, fit issues, or service gaps?
44
Question #1: How Can We Identify
At-Risk Students?
Analyze cohort retention and
graduation rates by subpopulation.
 Develop predictive models to isolate
the impact of specific variables.

45
Cohort Retention / Graduation Rates
by Subpopulation

Possible subpopulations to analyze:








Financial aid group (need, income)
Entry statistics (private HS vs. public HS)
Academic Characteristics (SAT/ACT; HS GPA)
Program area
Gender
GPA at institution
Ethnicity
Geography
46
Cohort Retention / Graduation Rates
by Subpopulation
Excerpt from Sample Retention Table
Freshman to Sophomore Retention of
Freshman Cohorts (2001-2006) by Need Level
Term 1
Term 3
No FAFSA
1141
673
59.0%
$0 (No need)
664
513
77.3%
$1-$10,000
514
391
76.1%
10,001-16,000
535
403
75.3%
$16,001-$22,000
694
503
72.5%
$22,001-$28,000
1112
803
72.2%
>$28,000
1002
714
71.3%
47
Predictive Modeling

If students with Term 1 GPAs below a
certain level are very unlikely to retain,
build two models:
One to predict who will have a low GPA
 One to understand the factors influencing
retention of achievers

48
Predictive Modeling – Excerpt
from Sample Predictive Model
Variable
Marginal Effects
Term 1 GPA
0.0226
Commuters
0.0434
Description
For every $1000 increase in total grant a person is
.8% more likely to retain to Term 3
For every $1000 increase in need a person is .3%
less likely to retain to Term 3
For every $1000 increase in on-campus earnings,
students are 5.4% more likely to retain to Term 3
For every additional GPA point (e.g. 2.5-3.5) a
student is 2.3% more likely to retain.
Commuters are 4.3% more likely to retain than
students who live on campus.
0.0712
State residents are 7.1% more likely to retain than
out-of-state students.
0.24663
Students in this program are 24.7% more likely to
retain.
0.10274
Funded athletes are 10.2% more likely to retain.
Total Grant
0.00834
Need
-0.00359
FWS
0.054
In-State
Program for
at-risk
students
Funded
Athletes
*For Full Time Freshmen Who Achieved at Least a 2.0 GPA in Term 1
49
Predictive Modeling

Possible Interventions Based on the
Model

Increase on-campus work opportunities

Review residential life programming

Expand program for at-risk students
50
Question #2: How Can We Determine the
Impact of Existing Policies and Programs?

Capture participation data and then
compare retention of participants and
non-participants.

Be aware of national research on
programs that have proven effective.

Conduct pilot programs.
51
Types of Participation Data to
Capture
 Athletic
involvement
 Student organization membership
 Honors participants
 First-year seminar participants
 Work-study participants
 Etc.
52
Question #3: Are There Any “Image Versus
Reality” Issues, Fit Issues, or Service Gaps?

Know the national trends (from ACT) and
trends among your competitors (from
IPEDS).
 Analyze student survey responses (NSSE,
CSI, SOS, SSI, CIRP, etc.).
 Ideally responses would be tied back to
student ID.
 Conduct focus groups.
 Use National Student Clearinghouse data.
Note: Feedback loops and measurable goals
are critical.
53
Discussion & Questions