Utilizing Predictive Analytics to Advance Data Driven Decision Making Presented by Michael Laracy, CEO of Rapid Insight

1. Utilizing Predictive Analytics to Advance
Data Driven Decision Making
Mike Laracy, Founder and CEO
Rapid Insight Inc.

2. • What is a predictive model?
• Types of models being used in higher ed and their
use cases
• What data is needed?
Agenda

3. “essentially, all models are wrong,
but some are useful”
-George E.P. Box

4. What is a predictive model?

5. Predictive Modeling
Using history to PREDICT the future
FUTUREHISTORY

6. MODELS ARE MATHEMATICAL EQUATIONS
Y=  + 1X1 + 2X2 + 3X3 + ... nXn
Yis the variable to be predicted
X’s represent variables on your students or prospective students
’sare coefficients that are statistically estimated

7. How does a model actually work?
=SLOPE of the line

8. What if there's more than one variable?

9. Model Output Example
Applicant ID Enrollment Probability
1822342 21.9%
1822432 7.4%
1761241 77.8%
1767771 81.8%
1766512 2.2%
1775511 88.8%
1845544 1.3%
1833355 1.9%
1775488 55.5%
1775575 12.2%

10. Model Decile Analysis

11. Some examples of models across industries
Direct Marketing:
What is each customer’s probability of purchasing?
How much will each prospect purchase?
Healthcare:
What is each patient’s probability of readmitting within 30 days of discharge?
What is each patient’s risk of Sepsis?
How long will a patient be in the hospital?
Financial:
Which customers will default on their loan?
Insurance:
What is each person’s risk of an accident?
What is each customer’s probability of churning?

12. Examples of models used in Higher Education

13. Prospect/Inquiry Modeling
What is each prospect’s (or inquiry’s) probability of applying?
How can these models be used?
• Buying optimal search names
• Strategically coordinate recruiting efforts by estimating expected
yield
• Prioritize staff and resources
• Mail vs. call vs. email

14. Applicant Enrollment Modeling
• What is each applicant’s probability of enrolling?
How can these models be used?
• Prioritize resources based on each applicant’s enrollment probability
• Help shape your incoming class
• Forecast enrollment and financial outlay based on admit pool

15. Student Retention Modeling
• Which students are at risk of attrition?
How can these models be used?
• Put programs in place to focus resources on at-risk students
• Increase retention and likelihood of student success

16. Alumni Donor Modeling
• Who is going to donate to the annual fund?
How can these models be used?
• Prioritize Advancement efforts based on predicted giving scores.

17. Other models
• Deposit Melt
• First term GPA

18. What Data?
• Inquiry Source
• Geographic location
• Gender
• Race/Ethnicity
• Distance from school
• EFC
• HS GPA
• SAT Scores
• Campus Visits
• Legacy
• Financial Aid
• Application date
• National Student
Clearinghouse

19. Q & A
www.rapidinsightinc.com