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Modeling and Money:The Two DO Mix
TAIRFebruary 1, 2006
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Baylor UniversityLocated in Waco TexasAffiliated with Baptist General
Convention of TexasBachelors/Masters/Doctoral degreesSeminary Mdiv and DminLawFall Enrollment approximately 14,000
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Nuggets
— Herb Edelstein
“If you’ve got terabytes of data, and
you’re relying on
data mining to find
interesting things
in there for you,
you’ve lost before
you’ve even begun.”
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Predictive Modeling at BU
Enrollment ManagementInquiry to Net Deposit Accept to Enroll– Applications of model
• Moving from one stage to another• Classification of students-new freshmen,
new transfers, graduate, etc.• Texas and non-Texas students
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Enrollment Management Stages
Inquiry AppliedAcceptedDepositNet DepositEnrollRetentionGraduation
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Student Retention– Applications --
• Fall to Spring Retention• Fall to Fall Retention• Enroll to Graduation
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Donor Management– Annual Gift– Major Donor– Planned Gift – Retention/Upgrade– New Donors
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Business Questions How can we identify potential major
donors? How can we predict propensity of a
donor to make an annual gift? How can we identify potential
planned giving donors? How can we identify current donors
that can move to next level of giving?
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How can we identify non-donor constituents with characteristics of a donor?
How can we predict expected value of a gift?
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Required Expertise
– Domain
– Data
– Analytical Methods
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Project TeamRepresentatives from University
DevelopmentRepresentatives from Institutional
ResearchSAS Consultants
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Process/Steps Explore Development data Build datasets for descriptive models Validate datasets Create profiles for analysis Build datasets for predictive modeling/mining Mine the data Create predictive models Apply the models Test the models
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Data ExplorationNew database for IR
–Learn and learn more!–Edit reports and data cleansing
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ProfilesDonorNon-donorAlumnae donorHispanic donorAfrican-American donor
– More data cleansing!
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Indicator Score
Creation of indicator variables with yes/no (1/0) values For Single households
-- 18 indicators For Two-person households
-- 25 indicators (7 indicators could be duplicated)
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Indicator Variables DOB_50_ind – over 50 years of age? Married-Widowed_ind - married or
widowed? Children_ind – any info on children? Alumni_ind – an alumni? Contact_ind – any contact info for
donor? Executive_ind – executive job code?
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Leader_ind –Baylor relationship? gift count – has donor made 15 gifts over
lifetime? gift_5k – total cum gifts >= $5,000? gift_25k – total cum gifts >= $25,000? gift_100k – total cum gifts >= $100,000? year5_ind – has donor made $250 gift in
EACH of last 5 years?
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year2_ind – has donor made ANY gift in EITHER of last 2 years?
Rating_ind – does donor have Echelon rating?
Athletic_gift_ind – has donor made gift to Athletic Department?
Alumn_assoc_ind – has donor made gift to Alumni Association?
Spouse_alum_ind - is spouse coded an alum?
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Indicator Score Distribution
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Average Cumulative Gift
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Donor Household Profile
64,000+ Households72% One donor in household50% Alumni60% Males57% Married19% indicate Baptist religion58% indicate Texas residences
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Non-Donor Household Profile
77,000+ HouseholdsMost data fields have a large
percent of missing values
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Donor Model for 2004
Use donors for previous 10 years Create target variable Identify predictor variablesBuild modelApply to 2005 donors
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Categories of PredictorsBiographical/demographic - 20Contact information - 12Degree data – 9Activities - 15Gift information - 31External rating information - 5Research data - 4
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Building ModelTarget variable – gift in 2004
– 1 for household with 2004 donation– 0 for household with no donation in
2004Predictors constructed from donors
in 1994-2003 time period Tools -- SAS Enterprise Miner
– Used to build, validate, and score
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ROC Model Comparison
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Lift Chart
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Distribution of Scores
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Model ComparisonsROC curves and Lift charts indicate
all models are performing wellMisclassification rates for the
models are all close to 16%Very little difference between
average profit for the modelsLogistic regression was chosen as
the model to employ
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Model ApplicationAnalyze 2004 donors at the end of
June 2005Determine those who have not
made a donationUse probability scores to target
those most likely to make a gift
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Future WorkApplication of general model
– Annual gifts– Major gifts– Planned gifts
Non-donor modelGift amount modelLife time value model
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Thanks!
Questions or Comments