Title Line

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Harnessing the Power of Data

to Enrol l the Class of Your Dreams

Moderator

• Greg Perfetto, Executive Director, Product Research, The

College Board, TN

Presenters

• Yvonne Romero Da Silva, Vice Dean of Admissions; Director

of Strategic Planning, University of Pennsylvania, PA

• Emily Coleman, Assistant Vice President, Enrollment

Management, Syracuse University, NY

• Thomas Bear, Executive Director of Student Financial

Strategies, University of Notre Dame, IN

Segment Analysis Service and

Geodemography

in 10 Minutes

Understanding Geodemographics…

• A long history of use in consumer marketing

• The basic idea of geodemography is that people with similar cultural backgrounds, incomes, and perspectives naturally gravitate toward one another and form relatively homogeneous communities; in other words—birds of a feather flock together.

• When they are living in a community, people emulate their neighbors, adopt similar social values, tastes and expectations, and—most importantly for consumer marketers—share similar patterns of consumer behavior toward products, services, media, and promotions.

Segment Analysis Service

– Geodemography -- Birds of feather flock together

• Consumer Models

– Consumer Focused

– Based on limited set of metrics -- SES, credit card data, and

purchasing behaviors

– Targeted at adult population

– Useful for consumer marketing (e.g., Prizm)

• Segment Analysis Service (formerly Descriptor Plus)

– Student Focused

– Based on over 300 data elements that broadly characterize college

going students background, preparation and aspirations

– Targets educationally relevant behaviors

– A dual lens on High Schools and Neighborhoods

Student Focused Geodemography

– Students

• Live in Neighborhoods

• Attend High Schools

– Neighborhoods and High Schools

• Have unique characteristics

• Characteristics can be summarized, “scored” and compared

• Based on the pattern of scores they can be grouped into

similar types

Segment Analysis Service

Although Segment Analysis Service is based on statistical analysis it is not

itself “Research”

Think of it as a data tool that can be used in research!

29 High School

Types or Clusters

33 Neighborhood

Types or Clusters

A given cluster describes

many similar High Schools

and Neighborhoods

Each Neighborhood

and High School is

placed into one and

only one cluster

• Factors (or scaled) scores categorize and summarize the general attributes of students in a specific High School or Neighborhood

• Key indicators are unscaled or “raw” average values of common metrics (test scores, number of scores sends, percent minority, average income…)

• Both scaled factor scores and unscaled raw scores and can be used to compare individual Neighborhoods and High Schools and predict the behavior of their students

Academic Indicators standardized testing, student grades, academic ability, content area strengths/weaknesses Religious Affiliation Christian culture, Catholic culture, Jewish culture College Application Focus public/private, sectarian/non-sectarian, shotgun/focused Desired College Characteristics national selective, local technical, public/private, coed/single gender, sectarian Racial/Ethnic Makeup Hispanic/Mexican, African- American, Asian, …

Curriculum Participation strength & breadth, college prep curriculum & culture, AP/Honors, religious curriculum Personal Achievements academic, community, work/vocational, leadership, athletics, arts Residential Characteristics stability/mobility, density, family category Socio-Economic Indicators work (professional/working Class), income, level of education, class

Segment Analysis Service

Example NH Cluster 59

An Institution Starting Out

Thinking though maximizing the opportunity for geodemographic tagging

13

Recruitment Evaluation and Selection Awareness &

Perception

Office Culture and Values Partnerships

Setting the Office Vision and Strategy Pen Undergraduate Admissions Strategy Project

Engage in an eight-month long strategy project to develop a five-year strategic plan that will outline the strategic vision, goals and implementation plan for the office of undergraduate admissions

Create a new organizational model for highly selective college admissions at Penn to recruit, enroll and retain the most accomplished and promising student-scholars and leaders

Method

Vision

Infrastructure

and Processes

Understanding our Core Segments

Four Schools

•College of Arts and Sciences

•Wharton School of Business

•School of Engineering and Applied Sciences

•School of Nursing

Five Coordinated Dual Degree Programs

Academics

Macro-Regions

•Midwest

•Northeast

•South

•West

•International

Geography

Latino

African-American

Native American

Low-Income & first generation

LGBT

Equity and Excellence

Prepping for Success

• Capturing the geotags

• Targeted recruitment

• Differentiated marketing

Using the College Board Segment Analysis Service Data in Yield Modeling

Emily Coleman

Assistant VP of Enrollment Management

Syracuse University

Introduction

Syracuse University uses logistic

regression models to predict the yield

and discount rate of each incoming

class

Many factors are considered

Factors that Predict Yield at SU Academic indicators (SAT, GPA)

Interest indicators (e.g., campus visits, interviews)

Program of interest

Demographics (gender, ethnicity)

Alumni relative

Admission to first choice program

Net cost

Financial aid application filed

FAFSA indicators

Family income / need level

Aid Filers vs. Non-Aid Filers Tried combining aid filers and non-filers in one model:

Requires assumption about need-level

Model did not work

Non-aid filer contains two types of students:

No need

Need, but low interest

Aid filer model works very well

Yield of non-aid filers has been more difficult to

predict

Non-Aid Filers Non-aid filers have lower yield than aid filers (filing is

an interest indicator)

Predicting yield of this group is very important for both

enrollment target and discount rate

Needed a way to distinguish between no need admits

and low interest admits who may have need

Before SA, tried median income by zip from census

Looked to SA factors to provide needed distinction

Neighborhood factor ACS1: Measure of Career type / Affluence

Anchored at “Professional / Affluent” on the high end

and “Working Class” on the low end

Correlates with actual income of aid filers at SU (r =

0.3)

Divided admits into five equally-sized groups for

exploration

ASC1 Groups N

Group 5 100

Group 4 100

Group 3 100

Group 2 100

Group 1 100

Professional and Affluent

Working Class

Comparison of Actual Mean Income by ACS1 Group to Overall Mean Income (aid filers)

Group 1, ($70,000)

Group 2, ($43,000)

Group 3, ($7,000)

Group 4, +$21,000

Group 5, +$65,000

Professional and Affluent

Working Class

Below average income

Above average income

Yield by ACS1 Group for Non-aid Filers Comparison to Average

Yield

Group 5 +4 %pts

Group 4 +3 %pt

Group 3 -1 %pt

Group 2 -1 %pts

Group 1 -3 %pts

7 %pt difference in yield in between Groups 1 and 5!

Implications for yield model ASC1:

Provides the much-needed distinction between low

need and low interest admits in the non-filer pool

Increases the accuracy of the yield model

Could be used to target messaging during the yield

phase

Send messages about financial aid and affordability to low

ASC1 admits in the non-filer pool

Conclusions Focus here has been on just one SA factor, but others

are significant predictors of yield as well

Average test scores at High School also works in non-filer model Correlates with income

Several SA factors are significant when it comes to predicting yield of aid filers Test scores at High School

Percent at HS with parent education of HS or less (neg)

These factors do not improve accuracy of model, but do tell us about our population

Use of Segment Analysis

in awarding

Institutional Financial Assistance

Thomas Bear

Executive Director of Student Financial Strategies

Need-Blind and Need-Based

Admission to Notre Dame is “need-blind.” Students are admitted to the

University on the basis of their academic and personal records of

achievement, not their financial circumstances. Notre Dame is

committed to offering a financial aid package that is designed to

meet the demonstrated financial need of a student through our need-

based aid programs.

In the 2013-2014 academic year, the University gave approximately

$115 million in need-based scholarships to undergraduate students.

Over 48% of all undergraduates receive some form of gift aid from the

University. Of the freshmen who enrolled in the fall of 2013 who

demonstrated financial need, the average amount of University

scholarship awarded was approximately $30,000.

Enrollment Division Goals

• Operate with a strategic planning mindset

• Relate to our constituents with highly personalized

service, anticipating needs and questions

• Provide the opportunity to every high-achieving Catholic

and other high priority cohorts of students to know about

how ND may benefit them

• Attract the best class available, with increased diversity

broadly understood

• Strategic administration of our financial aid resources

• Evaluate and evolve the division and its processes and

tasks in the continuous process improvement way

Improve Overall Freshman Class Profile

• Grow inquiries and applications

• Decrease admits

• Maintain freshman class enrollment

• Increase yield rate

Specific Characteristics

• Grow academic profile

• Maintain Catholicity and legacy percentages

• Increase diversity (domestic and international)

Target Populations:

• Academically most competitive

• Catholic

• Diversity (domestic and international)

• Target populations established by the Provost’s

Office with the input and support of the Board of

Trustees

• These populations create a natural stress in

attaining goal numbers and percentages

Segment Analysis and other related tools

Introduce tools to:

• Institute and measure effectiveness of financial aid

awarding

• Facilitate growth of academic profile and diversity

• Provide Office of General Counsel assurance of good

practice in offering financial assistance

Strategies Employed

• Cell analysis of yield and financial aid awarding

• Segment Analysis

• Yield probability

Provost Scholarship – used to reduce students’ portion of

self-help including:

• Student loans

• College employment

Students were grouped by academic profile and

estimated family contribution (EFC) in order to

analyze:

• Population characteristics

• Academic profile

• Diversity

• Special populations (i.e., legacy, athletes, merit,

international)

• Yield rates

• Above and below mean

• Need-based aid offered

• Use of institutional methodology

• Self-help assistance

• Percentage of $10,200 offered in Provost Scholarship

The neighborhood and high school probabilities of

diversity linked to each student were employed to

identify African-American, Hispanic/Latino, and Asian

target populations.

• Probabilities determined the reduction of students’ self-help

through awarding of Provost Scholarships

• Targeted probabilities varied by cells

• All three populations were tested

• Probability awarding of Provost Scholarships provided

residual benefits to freshman class diversity

• Limited pool of financial resources for Provost Scholarship

were available

Yield probabilities linked to each student were

matched against cells in which individuals were

plotted

• Final audits identified students who were ten percentage

points or lower than peers in cell comparisons identifying

underrepresented populations

• Students with lower projected yield probabilities were targeted

with Provost Scholarship

• Targeted percentages of 10% or greater were consistent

across all cells

• Limited pool of financial resources for Provost Scholarship

available

Several models were created and tested before final

implementation of awarding in March and April 2013 of

University need-based assistance

• Modeled the prior year’s data to ensure consistency and

effectiveness in awarding and enrolling students

• Several indicators were considered during testing

process to maximize model’s effectiveness

• Once developed, weekly reports were generated to

monitor financial aid awards and students’ enrollment

decisions/patterns

Academic Profile

Standardized Test Score

25% to 75% 1380 to 1510

Median 1434

Demographics

Diversity of Students 31%

Domestic 26%

International 5%

Catholicity 82%

Legacy 24%

First Generation 9%

Fall 2013 Freshman Class Profile

Highlights