Analytical Tools for Improving Access, Retention and Net Tuition Revenue

College Board Middle States Regional Forum 2008

Daniel J. Rodas, Long Island UniversityHeather Gibbs, Long Island University Jim Scannell, Scannell & Kurz, Inc.

Thursday, February 14, 2008

Overview

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Key Objectives

Increase student accessImprove undergraduate retentionIncrease net tuition revenueAchieve key enrollment objectivesMission attainment

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Long Island UniversityFounded in 1926 as a private, co-

educational, non-sectarian institution Mission of “Access and Excellence”18,600 degree-seeking students600 degree and certificate programs$360 million operating budget$100 million endowment$1 billion replacement value/physical

assets

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Long Island University

Two residential campuses: BrooklynC.W. Post

Four regional campusesBrentwoodRiverheadRocklandWestchester

653 full-time faculty162,000 living alumni

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Core Methodologies

Quantitative modeling Table analysis Regression

Qualitative research Document analysis Interviews Focus groups

Competitive analysis Benchmarking of best practices

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Case Studies

Undergraduate Pricing and Net TuitionGraduate Program ReviewRetention and PersistenceDiscussion

Discounting Policies & Practices

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Types

Historical/IncrementalTable AnalysisPredictive modeling & regressionOptimization

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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.)

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Optimization ModelsStrategic approach to allocating financial aidComprises quantitative techniques to

understand the relationship between grant and student characteristics on the probability of enrollment

Tools include:Table analysisPredictive analysisPrice sensitivity analysis

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Table Analysis: Example 1

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

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

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Table Analysis: Example 2Financial 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

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•Grant aid for 220 accepted students with need of $15,000 - $18,000

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Cost-benefit Analysis

Scannell & Kurz research 2007

55* ($20,000 - $10,500)= $522,500

20* ($20,000 - $7,500)= $250,000

8* ($20,000 - $4,500)= $124,000

$896,500

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Cost-benefit Analysis

What if all students in this need bracket received the top financial aid award?Projected enrollment:

220 * 55% = 121Projected net tuition revenue:

121 * ($20,000 - $10,500) = $1,149,500Gain in net tuition revenue:

$1,149,500 - $896,500 = $253,000

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Benefits

$253,000 in additional net tuition revenueCritical massBetter residence hall utilization

(38 additional students at $4,000 = $152,000)

Improved per unit costs in diningBigger enrollment base for future years

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Goals:Permits multivariate analysis Identify factors important in the enrollment

decisionDetermine the impact of institutional grant

on the probability of enrollmentDetermine the revenue-maximizing levels

of grants Identify alternative financial aid packaging

strategiesSuggest alternative admissions policies

Predictive Modeling and Price Sensitivity Analysis

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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

Predictive Modeling and Price Sensitivity Analysis

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Regression analysis:

Predictive Modeling and Price Sensitivity 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.

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Price SensitivitySimulation Summary Table #1

Predicted Class

(Baseline) Simulation #1 Simulation #4

Enrollment 945 952 1,074NTR $16,055,830 $16,401,210 $17,253,490Avg. NTR $17,000 $17,230 $16,070Institutional Grant $5,887,610 $5,706,670 $7,691,520Discount 27.0% 25.8% 30.8%Avg. SAT 1007 1008 1001H.S. Avg. 84 84 84out-of-state 144 144 162minority 233 233 289Aid applicants 826 833 955

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Optimization: SummaryCreation of a data file is keyWillingness to create new packaging

policiesIs there slack in your system?May require a radical redefinition of how

you packageTest market versus total roll-outModel needs to be updated/refined

annually!

Graduate Program Review

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Overview

Key QuestionsMethodologyObservationsPrice Sensitivity AnalysisRecommendationsConclusion

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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?

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Methodology

Analysis of data file and departmental records (3 years)

Review of off-the-shelf materialsIn-person interviews and focus groups

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Observations

Enrollment GoalsAdmissions PracticesFinancial Aid PracticesCompetitionIntegration with the CampusSummary

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Enrollment Goals

16-18 new students admitted annuallyFaculty interest in increasing class

diversityDesire to improve yield on offers of

admissionPriority to enroll students who will serve

“underserved” populations (C.W. Post)

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Admissions Practices

Little to no active effort to recruit students (primarily “word of mouth”)

Inefficient use of Web as recruiting toolLack of reports regarding InquiriesLittle effort to recruit LIU undergrads

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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

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Financial Aid Practices (cont’d)

Brooklyn Campus2004 2005 2006

Admit 28 30 33

Enroll 16 16 15

Yield 57% 53% 45%

Avg Tuition & Fees $28,800 $30,530 $32,286

Avg Grant $23,363 $22,138 $23,704

Discount Rate 81.1% 72.5% 73.4%

Net Tuition Revenue $87,000 $134,280 $128,723

Avg GRE 1274 1262 1271

% Minority 18.8% 25.0% 13.3%

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Financial Aid Practices (cont’d)

C.W. Post Campus2004 2005 2006

Admit 40 46 51

Enroll 16 17 16

Yield 40% 37% 31%

Avg Tuition & Fees $28,800 $30,530 $32,286

Avg Grant $11,070 $13,586 $10,531

Discount Rate 38.4% 44.5% 32.6%

Net Tuition Revenue $283,677 $288,050 $348,076

Avg GRE 1192 1185 1211

% Minority 37.5% 23.5% 12.5%

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CompetitionLimited competition between two programsBoth compete with some of the same

institutions in the regionSome comparison and quality measures:

% of enrollees receiving tuition waivers or assistantships

Average GRE scoresU.S. News rankings# of APA approved internship placements

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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

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Summary

Both campuses:Recruitment resources are not being used

strategically to meet stated goalsFinancial Aid resources are not being used

strategically to meet stated goalsPrice sensitivity must be investigated

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Price Sensitivity AnalysisBrooklyn Campus

Net Tuition Revenue by Total GRE Score (2004 to 2006)

Year

2004 2005 2006

N Avg NTR 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

All 16 $5,437 14 $7,701 14 $9,480

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Price Sensitivity Analysis (cont’d)C.W. Post Campus

Net Tuition Revenue by Total GRE Score (2004 to 2006)

Year

2004 2005 2006

N Avg NTR N Avg NTR N Avg NTR

GRE Total Score

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 - -

All 16 $17,729 17 $16,944 16 $21,754

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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.

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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.

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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?

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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.

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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

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Cohort Retention / Graduation Rates by Subpopulation

Excerpt from Sample Retention Table

Term 1No 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%

Term 3

Freshman to Sophomore Retention of Freshman Cohorts (2001-2006) by Need Level

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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

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Predictive Modeling – Excerptfrom Sample Predictive Model

Variable Marginal Effects Description

Total Grant 0.00834For every $1000 increase in total grant a person is .8% more likely to retain to Term 3

Need -0.00359For every $1000 increase in need a person is .3% less likely to retain to Term 3

FWS 0.054For every $1000 increase in on-campus earnings, students are 5.4% more likely to retain to Term 3

Term 1 GPA 0.0226For every additional GPA point (e.g. 2.5-3.5) a student is 2.3% more likely to retain.

Commuters 0.0434Commuters are 4.3% more likely to retain than students who live on campus.

In-State 0.0712State residents are 7.1% more likely to retain than out-of-state students.

Program for at-risk students 0.24663

Students in this program are 24.7% more likely to retain.

Funded Athletes 0.10274 Funded athletes are 10.2% more likely to retain.

*For Full Time Freshmen Who Achieved at Least a 2.0 GPA in Term 1

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Possible Interventions Based on the Model

Increase on-campus work opportunities

Review residential life programming

Expand program for at-risk students

Predictive Modeling

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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.

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Types of Participation Data to Capture

Athletic involvementStudent organization membershipHonors participantsFirst-year seminar participantsWork-study participantsEtc.

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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.

Discussion & Questions