Data-Driven Planning and Budgeting

for Net Tuition Revenue

Presenters:

Kathy Kurz, Vice President, Scannell & Kurz

Mike Frandsen, Vice President for Finance and

Administration at Oberlin College (formerly VP for Finance

at Albion College)

• Today’s Climate

• Analytical techniques for targeting aid to

increase NTR

• Trend-based financial aid budgeting

• Albion case study

Overview

The Demographic Climate

Source: WICHE

Michigan HS Graduates

• Students are applying to more schools and

competition for them is increasing.

• The number of students applying for aid has

increased.

• More families are appealing their first aid

offer.

• State aid programs are being reduced or

eliminated.

Today’s Climate

Today’s Climate

• Negative press is creating inaccurate

perceptions about educational costs and

financial aid. – Merit awards are seen as reducing need-based aid

when actually the vast majority of merit money also

meets need.

– Borrowing for educational expenses is increasingly

seen as a bad thing, making students loan averse.

– The ROI of a college education is increasingly being

called into question.

6

Today’s Climate

• Families are increasingly price sensitive

– Average family spent less on college in 2012-13 than in 2009-10. (Sallie Mae’s ―How America Pays for College‖ survey)

– In 2013 only 57% of students admitted to first choice institution chose to attend it. Cost and aid were significant influencers of that decision. (The American Freshman—

National Norms for 2013.)

• Many private institutions have seen their discount rates increase without corresponding increases in enrollment. – This has contributed to both Moody’s and S&P releasing negative outlooks

for higher education

What drives the discount rate?

• Market forces (i.e., competition)

• Changes in ability to pay

• Trends in family contributions

• Percentage of students applying for aid

• Changes in willingness to pay (lower yields on full pay students)

• Changes in the availability of government support

• Institutional goals (commitments to diversity, quality, etc.)

Today’s Climate

• Institutions are struggling to understand how to

adjust their aid policies in this environment.

• From NACUBO Tuition Discounting Survey:

• “We made an attempt to decrease our discount

rate through targeted enrollment strategies meant

to increase the yield of full-pay students. And no,

the strategies were not all that successful.”

• When another college tried to reduce its discount

rate: “Enrollment plunged”.

Today’s Climate

• Successful institutions:

• Focus on improving the value proposition and differentiating offerings, not just winning on affordability.

• Recognize that new strategies may be needed.

• Get the right people involved in understanding the trends and setting realistic budget targets.

• Focus on NTR not just class size or discount rate.

• Use data to understand price sensitivity and drive awarding strategies.

Today’s Climate

• Competitor Benchmarking

• Yield Analysis

• Predictive Modeling and Simulations

Analytical Techniques for

Targeting Aid to Increase NTR

• Benchmark with competitors, not peers or aspirants.

• Do not delete aid offers for non-enrolled students.

• Capture all aid offers, including those made by

• Admissions

• Athletics

• Departments

• Capture data on all appeals, whether or not additional aid was offered.

Avoiding Data Pitfalls

Competitor Benchmarking

College/University

Tuition

&

Fees

2014-15

Estimated

Net Tuition

& Fees

Freshman

Discount

Rate

2012-13*

Fall

2012

Accept

Rate

Fall

2012

SAT

25-75%

U.S. News

Ranking 2014

(America's Best

Colleges)

Institution A $32,776 $13,897 57.6% 69.5% 950 - 1170 NLAC below 150

Institution B $34,484 $19,449 43.6% 79.7% 950 - 1170 NLAC #100-150

Institution C $41,510 $27,231 34.4% 43.3% 1120 - 1340 NLAC #51-99

Institution D $43,270 $18,520 57.2% 70.2% 1065 - 1320 NLAC #51-99

Institution E $44,210 $27,101 38.7% 39.9% 1220 - 1370 NLAC top 50

Institution F $44,360 $29,588 33.3% 38.5% 1240 - 1390 NLAC top 50

Institution G $44,551 $25,439 42.9% 41.9% 1190 - 1370 NLAC top 50

Sources - College/University website, U.S. News & World Report and IPEDS

* Discount rate has been calculated using IPEDS data which, on occasion, have been found to be inaccurate.

• In general, when grants to a group of

admits are increased, yield increases but

the average NTR generated declines.

• Depending on how much yield increases,

increasing grant can either raise or lower

the total net tuition revenue generated by

that group of students.

Yield Analysis

Price Elastic Example

Grant Offer Admit Enroll Yield NTR Admit Enroll Yield NTR

$5,000 100 35 35% $700,000 220 77 35% $1,540,000

$3,000 120 24 20% $528,000 0 0 0 $0

Total 220 57 26% $1,228,000 220 77 35% $1,540,000

Current Grant Offer New Grant Offer

Tuition = $25,000; SAT = 1200+; Need = $10,000-$12,000

Projected Gain in NTR from increasing grant = $312,000

• Considers multiple variables at once

• Supports simulations of alternative

approaches

• Provides powerful tradeoff analysis

Predictive Modeling

Sample Model Factors

Price Elasticity

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Ex

pec

ted

Net

Tu

itio

n R

even

ue

Grant

Inelastic Elastic

Elasticity Tells You Which Side Of The Peak You Are

On.

Simulation of Alternative

Strategies

• Does the new policy really increase net

tuition revenue?

• What is the impact on total class size?

• What is its impact on geographic or ethnic

diversity?

• On the student quality profile?

Sample Simulation Summary Table:

Predicted

Class

(Baseline)

Add Need-

based Aid

Lower aid

for Lower Q

Larger Cuts

for Lower Q

Enrollment 572 595 558 524

Institutional Grant $10,247,214 $11,498,219 $9,355,848 $7,637,731

NTR $9,144,787 $8,687,059 $9,565,578 $10,139,259

Discount 52.8% 57.0% 49.4% 43.0%

High School GPA 3.40 3.40 3.40 3.42

Avg. SAT 1086 1086 1090 1098

Applied for Aid 84.5% 85.1% 83.0% 82.4%

% Female 64.4% 64.6% 64.2% 64.2%

% Catholic 56.2% 56.1% 55.9% 56.1%

% Honors 13.1% 12.8% 12.3% 13.1%

% Minority 51.1% 51.8% 50.4% 48.4%

% Pell Grant Recipients 35.9% 36.8% 33.1% 30.9%

% First Generation 42.0% 42.5% 40.3% 38.5%

% In-State 82.3% 82.0% 81.5% 81.1%

Data-Driven Budget Planning

• The simulations provide a basis for

discussions about tradeoffs and, ultimately,

for setting budget targets for incoming

students (NTR, discount rate, class size).

• To budget for returning students, use a by-

class year, trend-based approach.

22

Students Avg. Grant Students Avg. Grant Students Avg. Grant Students Avg. Grant

Freshmen 500 $13,700 500 $13,700 500 $13,700 500 $13,700

Sophomores 450 $10,000 375 $13,700 375 $13,700 375 $13,700

Juniors 425 $9,500 401 $10,000 334 $13,700 334 $13,700

Seniors 400 $9,300 404 $9,500 380 $10,000 317 $13,700

Total 1775 $10,765 1679 $11,808 1589 $12,814 1526 $13,700

Tuition $28,000 $28,840 $29,705 $30,596

FR Discount Rate 48.93% 47.50% 46.12% 44.78%

Total DR 38.45% 40.94% 43.14% 44.78%

Retention Assumptions

FR to So 0.75

So to JR 0.89

JR to SR 0.95

Tuition Incr. 1.03

2013 (Actual) 2014 (Actual) 2015 (Projected) 2016 (Projected)

Sample Cohort-Based Budget Model

Albion Case Study

24

• Kathy, thanks for reminding me…

– MI among the states with greatest projected

decline in HS graduates

– Albion enrollment ~90% Michiganders

– Local challenges along with the broader

challenges we all face

25

• Fall 2009 cohort

– 50 student drop from prior year

– 70+ drop from prior four year average

– Time to reassess

• Stamats

26

• Stamats recommended that Albion engage a

partner for predictive modeling of total

revenue based on financial aid strategies

and elasticity.

27

• Institutional tradeoffs

– Size of entering class

– Quality of entering class

– Revenue from entering class

– Retention of entering class

28

• Maximize total net revenue

• Maintain quality

29

• Year #1 – Late start with S&K, many decisions already made

– Partial implementation of recommendations

– RESULTS

• Increase in net revenue per first-year student for the first time

in 5 years

• One more first-year student

• BUT, terrible retention

30

• Year #2 – More timely information from Albion to S&K and from

S&K back to Albion

• Still some information we did not have

– Implemented the majority of S&K’s recommendations

– RESULT

• Net revenue per first-year student up double digits

• Smaller class, more revenue

• Retention improved by 10%

31

• Year #3 – New Enrollment Management leadership

– Implemented some of S&K’s recommendations

– RESULT

• Net revenue per first-year student down slightly (but still up by

double digits from two years prior)

• Smaller class

• Retention up again

32

• So What?

– Using data, and trusting data, led to better

results

– The more we trusted the data, the better our

results

33

• So What?

– Having data led to:

• More discussions about tradeoffs

• Better understanding of issues by the Board

• Better planning for returning student revenue

• Better thinking about our value proposition and how

it was being communicated

34

• So What?

– But did not lead to:

• Data-based planning for new student revenue; still

planned for what we hoped

35

• A Caution

– The market is changing so fast that one needs to

be careful with too much reliance on the past

36