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TRANSCRIPT
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