Mission, Margin, or Mission, Margin, or Both?Both?

How Data Analysis and Modeling Can How Data Analysis and Modeling Can Help You Find the BalanceHelp You Find the Balance

Middle States Regional ForumFebruary 2008

Goals of Enrollment Goals of Enrollment ManagementManagement•Understand factors that influence enrollment behaviors

• Positively influence enrollment decisions, then retain and graduate those who enroll

•Optimize size & composition of student body, consistent with mission

• Control expenditures

Trends Leading to Strategic Trends Leading to Strategic Use Use of Financial Aidof Financial AidSimilar to those which prompted

enrollment

management models:• Intense competition

• Competing pressures• Enrolling desired numbers, quality, mix

• Achieving revenue goals

• Maintaining commitment to mission & values

All-consuming focus on revenue

Plenty of Pressures - Little Plenty of Pressures - Little ControlControl•The need for a data-supported approach to managing enrollment and resources has never been more important.

•Sophisticated models and tools are needed to fully understand the trade-offs and impacts of various strategies.

Data Analysis ProgressionData Analysis Progression

•Table analysis•Aggregate data analysis•Segmented data analysis

•subgroup or individual data

•Regression analysis•individual data & modeling

Typical Enrollment Yield TableTypical Enrollment Yield Table

•Males enrolled at a rate of 30% and had an average GPA of 3.0.

• Females enrolled at a rate of 20% and had an average GPA of 3.4.

Male Female

Yield Rate

30% 20%

Mean GPA

3.0 3.4

Limitations of Table AnalysisLimitations of Table Analysis

Is the difference in enrollment rates by gender because men are more inclined to come to the institution or because people with lower GPAs are more likely to enroll?

•If all admits had the same GPA, how much would gender affect the likelihood of enrollment?

•If gender were a constant, how much difference would GPA make?

A table alone does not provide the answer.

Modeling Modeling && Regression Regression AnalysisAnalysisProbability models make projections

about the

future based on evidence from the past.

Regression analysis controls

for the influence of multiple

variables – it holds other things

constant while testing the

impact of various factors

What is a Predictive Model?What is a Predictive Model?

An equation that explains variation in

something – such as the probability that a

student will enroll.

•A financial aid model helps you understand the relationship of students’ characteristics to their ability and willingness to pay.

Example EquationExample Equation

Enrollment = f (SAT, GPA, state, ethnicity, gender, expected major, need, grant)

Translation: The probability of enrolling is a function of the

following combination of factors (SAT, GPA, etc.).

Benefits of ModelingBenefits of Modeling

•Pinpoints opportunities to use aid resources thoughtfully and effectively to achieve enrollment and fiscal goals and support institutional values.

•Makes clear the consequences and choices that result from new awarding policies.

•Where we’ve been

•Where we are

•How we got here

•How to deal with success

•How research has supported the effort

““A Classic Turnaround Story”A Classic Turnaround Story”

• New president arrived spring 1999 – Inspired by Benjamin Rush to transform Dickinson

• Strategic plan

• Operational strategies

• Key performance indicators

• Budget – in the black & endowment spending controlled

• Enrollment – doubling of demand for Dickinson

• Improved academic quality

Transformation in Four PartsTransformation in Four Parts

• Power of leadership narrative

• Strategic planning

• Key to transformation

• Three levels

• Execution

• Results

Three Levels of Strategic PlanThree Levels of Strategic Plan

• Level 1: Story, History, Mission, Vision, Environmental Analysis

• Level 2: Articulate Defining Characteristics, Identify Enabling Conditions, Develop Strategic Objectives & Measurable Goals

• Level 3: Execution, Measurement – Key Performance Indicators, Assessment

Success by the NumbersSuccess by the Numbers

•Quality of student – improvedimproved

• Financial stability – attainedattained

•Mission, Vision, Goals , objectives – established established && defines defines our “brand”our “brand”

““What have you done for me What have you done for me lately?”lately?” •How to deal with success – tough to maintain momentum and not “burn out”

•New group of aspirant schools = new goals

•Understanding new financial situation

• Preparing for leadership change (President, senior executives)

Role of Institutional ResearchRole of Institutional Research

•Define the problem or area needing attention

• Establish timeline & objectives

• Importance of intermediate objectives

• Input from ‘statistically savvy’ & assistance to ‘statistically challenged’

Role of Institutional ResearchRole of Institutional Research

• Remove “black box” results

• Confidence in model required so results will be believed

• Put technical analysis into common terms

• Leverage expertise of subject matter experts, (financial aid, admission, enrollment exec)

• Facilitate decision-making

Role of Institutional ResearchRole of Institutional Research

• “Keeper of Data” in conjunction with others

• Ensure a “common voice” • Frozen data sets

• Keep tabs on the various definitions

• Funnel all data requests (internal & external) through IR

• Peer group comparisons & benchmarking• Difficult to ensure apples are compared to apples

• LOTS of different groups; admissions overlap, President’s list, Dean’s list, other aspirant lists.

Strategic Indicators and Strategic Indicators and GoalsGoals ~ AdmissionsAdmissions

20020011

20020033

20020055

20020077

2010 2010 GoalGoal

Total Applicants 3820 4633 4784 5844 6000

Acceptance Rate 64% 52% 43% 42% 40%

Yield 25% 26% 27% 25% 25%

Total Freshmen 611 624 648 621 600

Top 10% Class Rank

47% 50% 52% 48% 60%

Strategic Indicators and Strategic Indicators and Goals ~ Goals ~ First-Year Financial AidFirst-Year Financial Aid

20012001 20032003 20052005 20072007 20102010GoalGoal

% Aided (w/ Dickinson

Grant)61% 55% 57% 48% 53%

% Aided Need-Based 52% 47% 48% 43% 45%

% Aided Non-Need-Based

11% 9% 10% 5% 8%

Average Grant$14,1

60$16,6

68$19, 203

$21,163

Average Grant as % of Comp Fee

44% 47% 48% 48%

Tuition Discount Rate

34% 32% 34% 28% 35%

Strategic Indicators and Strategic Indicators and Goals ~ Goals ~ Student BodyStudent Body

20020011

20020033

20020055

20020077

2010 2010 GoalGoal

Total MatriculantEnrollment

2159

2235 2301 2349 2250

% Male 42% 44% 44% 45% 45%

% International 1% 2% 5% 6% 7%

% Minority 6% 8% 13% 14% 18%

% Out-of-State 59% 65% 72% 75% 75%

Modeling Starts with Key Modeling Starts with Key QuestionsQuestions

•What questions are you trying to answer?

•What data do you have available to answer these questions?

•How do you get started?

What are your Target Groups What are your Target Groups and Objectives?and Objectives?

Number, quality and mix of students:

• Underrepresented populations

• Academically gifted, special talents

• In-state/out-of-state

• Specific majors

• On campus vs. off campus

Containing expenditures

Potential Data ElementsPotential Data Elements

•Admission fields: application type and status, quality measures

• Financial aid fields: grant, need, awards

•Demographic fields: ethnicity, gender, state of residence

•Enrollment indicator

Influences on Enrollment Influences on Enrollment DecisionsDecisions Academics Reputation

Location

STUDENT CHARACTERISTICS

PRICE

Net price is the single, easiest factor for the

institution to control

Ability and Willingness to PayAbility and Willingness to Pay

•Ability to pay based on need

•Willingness to pay

… Perceived value of enrolling

… Commitment to institution – students will pay more to attend their first choice

… Even if you are their first choice, the price must be perceived as affordable

Policy ImplicationsPolicy Implications• Evaluating yields of students with different combinations of academic ability and need can help in understanding the relationship between willingness and ability to pay.

• Institutions can respond to this information through tuition discounting, but should they?

• Implications for institutional missions and values, fairness, and families’ perceptions of aid practices.

Competing PrioritiesCompeting Priorities

VALUES If more funds are directed at middle- and high-income students, low-income students may end up with more unmet need or self-help.

REVENUE

To focus solely on equity could quickly deplete resources and therefore be fiscally inefficient.

BALANCE

Focusing on the efficient use of funds may increase the opportunities for the use of aid funds in the pursuit of equity.

Competing PrioritiesCompeting Priorities

• “If there aren’t enough students bringing tuition dollars to campus, there will be inadequate resources for delivering a quality education and inadequate resources for need-based financial aid.

• Understanding the mutually reinforcing aspects of equity & efficiency lead to the suggestion that equity goals and strategic planning must enter into financial allocation decisions.”

Sandy Baum

Data ConsiderationsData Considerations

• Garbage in, garbage out – not all offices treat data with the same respect. Everyone must be committed to data quality.

• Data is incomplete or inaccurateNo aid offers for non-enrolling studentsMissing quality indicators (GPA, test scores) or

demographic information on a significant segment of the admitted pool

• External factors not included in the model (what is happening with your primary competitors… their recruiting strategies, their awarding policies?)

Managing ExpectationsManaging Expectations

•Modeling is not a crystal ball.

• Predictive modeling is based on the past.

• The future may look different.

It’s Only DataIt’s Only Data

Data and information from modeling

+ Lessons learned by experienced practitioners

+ Intuition

+ Institutional context and values

= Well-founded & informed policy decisions

Strategic Use of Financial AidStrategic Use of Financial Aid

Resulting aid policies should:

• Be congruent with mission & values

• Optimize enrollment of desired mix and number of students

• Encourage retention & degree completion

• Blend principles of meeting need with awareness of market realities

• Seek to balance ideal with practical

ConclusionsConclusions

• We will not be able to maintain or increase equity and access in higher education if we do not control costs and use funds efficiently.

• Without careful analysis of data, any change in policy or practice is equally likely to succeed or fail.

Risk can be minimized and mission can be accomplished through data analysis & modeling.

PresentersPresenters

•Mike JohnsonDirector, Institutional ResearchDickinson Collegejohnsomi@dickinson.edu(717) 245-1019

•Anne SturtevantDirector, Financial Aid SolutionsCollege Boardasturtevant@collegeboard.org(571) 262-5991