1. Predictive Modeling The Key to Enrollment Management GISEM Nancy G. McDuff October 22, 2006

2. What is Predictive Modeling Predicts the behavior of students –How many will enroll? –Who will enroll? –Who will retain? –How much it costs to attract/keep a student? –Who will graduate? –What they will study?

3. Predictive modeling: A short definition Statistical analysis of past behavior to simulate future results. For admitted students, the probability that a student will enroll can be determined by shared characteristics and behaviors of students who have enrolled in the past. From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

5. Why is it Important Planning –Space –Academic Service –Auxiliary services Budgeting — costs and revenue Setting and meeting goals

6. Basic Uses of Predictive Modeling How many offers of admission should be made to enroll a certain size class How many offers must be made to achieve certain characteristics of the class How many students will graduate How many students will attrite How much scholarship do you need to offer

7. How do you get Started What do you need to know (what questions are being asked) What do you know What do you wish you know What is predictable What predicts Data, Data, Data

8. Data Develop tools and techniques to manage information Decide what to collect –Don’t over/under collect Identify where to find it –Student app –College Board –State Determine where to store it Decide how to use it

9. From Data to Strategy Data are raw material Information is refined by variable analysis –Residency, demographics Refined information provides energy sources enabling knowledge –Trends, growth patterns, yields Knowledge makes it possible to create strategies –Marketing strategies, targeting, yield events

10. Start with what you know What characteristics predict well What do you have historically What are good correlates How comfortable are you with statistics

11. Tips and Secrets Be Conservative Three models surrounding the most likely case Define carefully Be Consistent Give them what you know, not always what they ask

12. Questions affecting the model What is the optimum tuition charge and enrollment mix How many seats will you need in a major/school How many students will live on campus How many students will drop classes Should you build a new residence hall

13. More Advanced Predictive Modeling ACES Validity Study Non Cognitive Variables in Admissions Predicting Demand for Majors LOGIT model for enrollment

14. What are good predictors History is usually a good predictor Sometimes there are unusual correlates Must start with archived data or beginning to develop history….but of what Numbers are good, but percentages are better

15. Enrollment Example Enrollment equals –Current enrollment –Less attrition –Less graduating students –Plus new students Predictive modeling is –Current + changes = New –Or inputs – outputs = Net loss/gain

16. How to Determine Enrollment Current Enrollment Less Attrition Less Graduates Plus New Students Equals New Enrollment

17. Predicting Enrollment Fall Enrollment - Attrition - Graduates + New = Returning (next year) Freshmen500 10% (50) 0 20 (4%) [500] Sophomore600 5% (30) 0 50 (8%) 470 Junior500 5% (25) 20 (4%) 620 Senior450 2% (9) 80% (360) 10 (2%) 470 91 Total2050 114 (5.6%) 385 (19% 100 (4.9%) 1650 + 500

18. Predicting the Freshman Class HistoryAssumptionsActual 52%, 56%, 63%, 60%, 67% yield 60% average yield, 1200 enrolled is target Make 2000 Offers Two years of history only, 65% and 85% 75% of offers send deposits 1600 deposits (80%) No History 90% of those with deposits attend orientation 1400 attended orientation (88%)

19. So How Many Will Enroll Did the averages work What other indicators are there –Housing Contracts –Financial Aid/Scholarships Accepts –Registrations –Meal Contracts

20. What Do You Predict

21. Looking at History Different Ways

22. Are you ready for the next generation of students? Between 1995 and 2015, 20% more students are projected to enroll in U.S. colleges and universities 80% of the increase in college-aged students between 1995 and 2015 will be under-represented students Business week (2004) 40% of the increase in the college age population will be in the bottom income quartile The South will have the largest growth at 18.7% by 2017-18 Georgia can expect between 26% and 45% growth in H.S. grads From: Noel-Levitz. “Doing More With Less: Building Efficiencies and Effectiveness into Your Enrollment Management Program”, WICHE “Knocking at the College Door”

23. What factors influence college choice/retention? Academic reputation Rankings/Selectivity Institution type Size Proximity to home Amenities Quality of student life Safety Personal touch/Relationships Class size & student to faculty ratio Academic programs (study- abroad, learning communities, Honors) Programs of study State and institutional financial assistance Receiving scholarships Campus visits Athletics/Campus Appearance

24. Challenges facing institutions Fluctuating economy Fewer students with the ability to pay for the increasing costs of higher education Strong scholarship, grant, and need-based aid programs to attract students are becoming more prevalent Static endowments and state support for higher education From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

25. Challenges Facing Institutions Cont. Operating in an increasingly competitive environment Changing demographics More aggressive marketing and recruiting by both public and private institutions More sophisticated marketplace…plans, systems, and advanced tools being developed From: Noel-Levitz. “Enrollment Strategies That Work in Attracting and Retaining Students”

26. Challenges of Predictive Modeling Can lead horse Models need to be developed over time – numerous years Models can alter by changes in policies –Financial aid –Tuition Models can be costly – time, accuracy, money Modeling usually is homogeneous (a model for freshmen recruiting usually would not fully apply to transfers.)

27. Challenges

28. Challenges

29. Challenges

30. Summary and Conclusions Modeling is only part of the puzzle. Use multiple modes of recruitment Predictive modeling provides a sense of the data pool accuracy – but inputs must be correct One can leverage enrollment by finances and characteristics

32. Resources and References https://ra.collegeboard.com/https://ra.collegeboard.com/ Enrollment Planning Services https://ra.collegeboard.com/ www.nslc.org/ www.nslc.org/ National Student Clearinghouse www.nslc.org/ http://www.amstat.org/index.cfm?fuseaction=mainhttp://www.amstat.org/index.cfm?fuseaction=main American Statistical Association http://www.amstat.org/index.cfm?fuseaction=main http://www.collegeresults.org/http://www.collegeresults.org/ The Education Trust http://www.collegeresults.org/ https://www.noellevitz.comhttps://www.noellevitz.com Noel-Levitz https://www.noellevitz.com http://www.airweb.org/http://www.airweb.org/ Association for Institutional Research http://www.airweb.org/ Hopkins, K. Noel-Levitz. (2003, July). “Building and Developing an Effective Enrollment Management Plan for Colleges and Universities.” National Conference on Student Retention. Topor & Associates. A Contemporary Approach to Marketing Higher Education.