1. Using Predictive Modeling To Manage and Shape Your Enrollments Kevin Crockett President and CEO February 21, 2008

2. According to the 2008 Institutional Fact Finders submitted in preparation for this conference… 14% of institutional respondents reported using predictive modeling in their marketing and recruitment programs 36% reported that they systematically contact inquiries to code their level of interest 29% reported that they use data analysis to predict dropout proneness

3. What is predictive modeling and how can it support your enrollment management efforts

4. Resource scarcity requires enrollment managers to effectively understand and manage student propensity to enroll/re-enroll

5. Means of qualifying student interest in and commitment to your institution Research/data analysis Tracking student contacts/behavior Telecommunications Personal contact Reply mechanisms in all correspondence Predictive modeling

6. Predictive modeling (pri*dik*tiv mod*el*ing) Statistical analysis of past student behavior to simulate future results

7. Why is funnel qualification important? Focuses scarce time and resources on those students with the greatest propensity to enroll/re- enroll Facilitates better relationship-building Enables university staff and advocates to follow-up with students that are genuinely interested in your school Provides cost-savings by not communicating equally with every student Enables greater personalization with students Increases the precision of enrollment forecasting

8. Nationally…enrollment funnel dynamics are changing Source: Noel-Levitz 2006 Admissions Funnel Report

9. Predictive modeling has become more important as the distinction between stages has become blurred

10. The ultimate goal is to build a critical mass of “good fit” students throughout the enrollment funnel

11. How are predictive models built and how well do they work?

12. Models can be built from each stage of the enrollment funnel but they should ultimately predict enrollment or re-enrollment Pre-prospect model Prospect model Inquiry model Applicant/admit model Retention/progression models

13. Modeling converts each trait or behavior into a statistical value Sample inquiry model

14. Sample admitted student model

15. The “Hold” and “Main” Files Models should be built using one half of your historical file so that they can be tested against the other half of your file This ensures that you understand the performance of your model before you ever use it to prioritize your follow-up with prospective students

16. Sample model performance chart 60% of non-enrollers scored <.30 while less than 4% of enrollers had these scores

17. A model’s output ENROLLED 1 ENROLLED Kate Black.99 Highly Likely Mike Miller.85 Highly Likely Dave Hamilton.72 Likely Jerrica Zwick.68 Likely Angie Mabeus.46 Somewhat Likely Audrey Keppler.41 Somewhat Likely Brian Schuler.21 Less Likely Jordan Clouser.17 Less Likely NOT ENROLLED 0 NOT ENROLLED

18. Sample predictive model performance At.90 or greater, 11% of the inquiry pool produced 67% of the applications and 78% of the enrolled students.

19. Fall 2007 average client model performance 83% of the deposited students came from the highest scoring 45% of the inquiry pool. Score RangeInquiryApplicantAdmit Gross Deposit Applicant / Inquiry Admit/ Inquiry Gross Deposit/ Inquiry Applicant Lift Admit Lift Gross Deposit Lift 0.00-0.09345210143102.9%1.2%0.3%0.210.130.09 0.10-0.1925455710304792.8%1.2%0.3%0.200.120.10 0.20-0.29101900380122024663.7%2.2%0.5%0.270.220.15 0.30-0.3920578311685777017825.7%3.8%0.9%0.420.380.28 0.40-0.49216739181091248230908.4%5.8%1.4%0.610.580.46 0.50-0.591537861981314017389112.9%9.1%2.5%0.940.920.81 0.60-0.691194242149615641459318.0%13.1%3.8%1.32 1.24 0.70-0.79864532244216463532726.0%19.0%6.2%1.901.921.98 0.80-0.89582642203516804592737.8%28.8%10.2%2.772.913.27 0.90-1.00301701658213422599755.0%44.5%19.9%4.024.496.39 Total/Average1001426136774991483116213.7%9.9%3.1%1.00 7% of the deposited students came from the lowest scoring 34% of the inquiry pool

20. Applying predictive modeling technology to your marketing and recruitment program

21. Increase the size of your inquiry pool through more effective mining of your prospect pool (pre-prospect and prospect models)

22. Assign communication channels based on propensity to enroll Strategically created groups Lowest interest Most interested Web site E-mail E-newsletters/ communications Direct mail Student calls Professional staff Alumni Faculty

23. Shape enrollment through targeted communication campaigns

24. Focus admissions travel

25. Applying predictive modeling to your retention efforts

26. We have found that blending a predictive model with data gleaned from a motivation/attitudinal survey produces a powerful data combination

27. The predictive model provides OBSERVED risk factors

28. While the motivation survey produces ACKOWLEDGED risk factors

29. Risk categories can be used to design both programmatic and student-specific interventions

30. It is critical in this approach that you blend the observed and acknowledged risk factors to create an agenda for action

31. Implementation of this combined approach improved retention rates across entry terms and campuses for this institution

32. Some concluding thoughts

33. Apply modeling to the regions of your funnel that hold the greatest promise for improving your enrollment management outcomes Pre-prospect model Prospect model Inquiry model Applicant/admit model Retention/progression models

34. Identify a resource to develop your institution- specific models and score your current files

35. Establish project goals and aggressively measure your results…remember the goal is to beat the model!

36. Use the modeling process to improve data collection and data management protocols on your campus….

37. …while most schools have reasonably good data on student characteristics, the weakness tends to be in tracking student behavior

38. Observations and questions