The document discusses the challenges of declining college enrollment in the U.S., particularly among community colleges and for-profit institutions, and presents a predictive analytics workflow to enhance enrollment strategies. It details the importance of data understanding, modeling, evaluation, and deployment in enrollment management, alongside industry case studies and outcomes related to increased student retention and targeted marketing efforts. The document emphasizes the need for realistic expectations, effective data use, and continuous improvement in recruitment practices.

1. BECKER COLLEGE
Enhancing the Quality of Predic4ve
Modeling on College Enrollment
Feyzi R. Bagirov

2. Acknowledgements
• Yun Xiang, Director of Ins4tu4onal Research
and Assessment at University of New
Hampshire

3. Agenda
• Enrollment in the US
• Background
• Predic4ve Analy4cs Workflow
– Business Understanding
– Data Understanding
– Data Prepara4on
– Modeling
– Evalua4on
– Deployment
• Next Steps
• Take Away Messages
• Q&A

4. • Founding Director of a Bachelor of Science in
Data Science program at Becker College
• Faculty of Analy4cs at Harrisburg University of
Science and Technology
• CBO at 529 LLC (Educa4onal infrastructure
and analy4cs)
• Data Science Advisor at Metadata.io (ABM,
B2B Demand Genera4on)

5. Enrollment in the US
The two types of colleges with the biggest declines in enrollment are:
community colleges and for-profit universi4es. Those schools draw heavily
from low-income and minority households.
Source: h]p://money.cnn.com/2016/05/20/news/economy/college-enrollment-down/

6. Enrollment in the US
• The 10-year average for college closures is five annually.
• The main struggle for many small colleges is declining enrollment
• Moody’s report predicts that inability of small colleges to increase revenue
will result in triple the number of closures and double the number of mergers
in the coming years.

7. Enrollment in the US

8. Background
A Private, 4-year College
Enrollment: 2,000 undergraduates
Loca4on: Central Massachuse]s
Expecta4on:
§ 1st year—Build the data structure
§ 2nd year—Run the predic4ve models

9. “Skunk” Team
• Vice President of Enrollment Management
• Chief Informa4on Officer
• Director of Ins4tu4onal Research
• Dean of Admissions
• Director of Financial Aid
• Director of Data Science
• Director of Enterprise Applica4ons
• Data Engineer

10. Predic4ve Analy4c Work Flow
The Cross-Industry Standard Process Model of Data
Mining (CRISP-DM)

11. Step One—Business
Understanding

12. “U4lize Big Data to increase the enrollment
Ini4al Requirement

13. Start with a high-level idea
• Who is my customer?
• What is making my customer complain so much?

14. Enrollment Management &
Admission Funnel
Defini&on: Enrollment Management
is the organiza4onal integra4on of
func4ons such as academic advising,
admissions, financial aid, and
orienta4on into a comprehensive
ins4tu4onal approach designed to
enable college and university
administrators to exert greater
influence over the factors that shape
their enrollments (Hossler &
Bontrager, 2015, p. 7-8).
Rules of Progression
Response %
Conversion %
Comple3on %
Acceptance %
Confirma3on %
Capture %
Persistence %
Gradua3on %

15. Enrollment Management Process
Process:
• Prospect/Inquiry Genera4on
• Applicant Management
• Deposit/Confirma4on Management
• Event Management
• Marke4ng
• Registrar
Goals:
• Maintain or increase class size
• Increase ethnic diversity
• Improve academic profile
• Increase net tui4on revenue
• Lower the tui4on discount rate
• Strengthen weak academic
programs
• Maximize the return of strong
academic programs
• Support athle4c or other
specialized programs on campus
Hossler, D., & Bontrager, B. (2015). Handbook of strategic enrollment management. San Francisco, CA: Jossey-Bass.

16. Industry use cases

17. Industry use cases

18. Industry use cases

19. Use cases studied prior to design
• A major university in the south of the US
• Started experimen4ng with analy4cs in 2003
• Used all available data in modeling
• Leveraged Clearing House data to see where
they were losing students; this allowed to
have a be]er idea of true compe4tors. This
led to a completely restructured
understanding of their compe44on.
• Used ACT datasets for profiling

20. Use cases studied prior to design
• Game-changers and take-away messages:
– Visits to campus made a difference in predic4ng
which students would enroll
– Freshmen were required to live on campus and
that made students persist be]er
– Targe4ng of the out-of-state students based on
geographies where they had a large popula4ons
already. By visualizing large popula4ons with
maps it showed concentra4ons by the area

21. Use cases studied prior to design
• Determining target markets, the Admission’s business model
changed dras4cally:
– Hiring regional recruiters in areas of high prospect concentra4on
– Changing target prospects (high-probability prospects require less
4me, more efforts on 60-80% probability prospects)
– Changed the targe4ng process. Customize targe4ng messages.
– Changed the types of recruiters they’ve hired – the ones that be]er
understand data and use that data to bring in the prospects
– Hired a campaign director to track the success of the campaigns
– Used in-house callers to spend a lot of 4me on calling campaigns
talking to students
– Overlaid prospec4ve students with alumni data sets and reached out
to alumni to host lunches in areas outside of the home state to engage
prospects.

22. Use cases studied prior to design
• Outcomes:
– Launched predic4ve models in 2003. Most
informa4on was in Excel. Over4me, acquired a CRM
system and leveraged that for consolida4on and
accrual of applicant and event data.
– Redundant and irrelevant data in CRM was removed
over 4me (for example, all students wanted a
scholarship, so they did not include that in their
model)
– Enrollment increase from 20k to 35k over a targeted
period, predominantly due to out-of-state students

23. Be Realis4c, Expect Changes!
Goal: Increase
freshmen yield rate
Ques&on: What admi]ed students
are more likely to deposit?
Reality

24. Step Two—Data Understanding

25. Organiza4onal Silos

26. Ini4al Ques4ons for Becker
• Determining data sources:
– ACT, what data elements are included and can be
collected
– SAT, what data elements are included and can be
collected
– Clearing House, how long will it take to perform
valida4on of students’ enrollment to Becker
– Accessing popula4on data to be used in targe4ng
areas
– Do we know who are our true compe4tors?
– What are we doing to target students differently?

27. Life Cycle of an Inquiry in Recruiter

28. Building In-house Data –
IR & IT Work Together, Happily
Miller, W. (2016). Conflict Resolu4on.
The process
took 75% of
time of the
whole
project!

29. Student Profile
Non-
White
29%
White
71%
Ethnicity 2014/15
Out-
of-
State
46%
In-
State
54%
Non-
First
Gen
48%
First
Gen
52%
Femal
e
56%
Male
44%
Gender 2014/15
Female
64%
Male
36%
Gender 2016
Non-
White
26%
White
74%
Ethnicity 2016
Non-
FirstGe
n
38%
First
Gen
62%
Out-of-
State
44%
In-
State
56%
FirstGen vs. Non
FirstGen 2014/15
FirstGen vs. Non
FirstGen 2016
In-State vs. Out-of-
State 2014/15
In-State vs. Out-of-
State 2016

30. Step Three—Data Prepara4on

31. Be Realis4c, Expect Changes!
Plan

32. Step Four—Modeling

33. Predic4ve Modeling Details
• Two years of historical data
• Descrip4ve: Characteris4cs and demographics (gender, race/ethnicity, geography, first-
gen, Pell grant receiver, etc.)
• Financial aid: Need-based, merit-based, other grant, loan, work-study, etc.
• Behavioral: Applica4on date; deposited date; Admission ac4vi4es (phone call, email,
campus visits, etc.)
• Logis4c regression
• Predict “deposit” probability for each student (values from 0 to 1)
• Test each predictor separately first vs putng everything in the model
• R/RStudio
• Learning curve, but it’s worth
Data
Model
Tools

34. • Descrip4ve (Characteris4cs and demographics)
– Age
– gender
– race/ethnicity
– Geography (regional – New England/Non-New
England, MA/Non-MA, Worcester/Non-Worcester)
– first genera4on (parental educa4on)
– Pell grant receiver
– Sports ac4vi4es count
– External ac4vi4es count
Data

35. • Financial aid:
– need-based
– merit-based
– other grant
– loan
– work-study
– family contribu4on
Data

36. • Behavioral:
– Applica4on date/applied term
– deposited date
– Admission ac4vi4es
• phone call count
• email
• campus visits (Acceptance Student Day, campus tours,
etc.)
Data
Admi]ed Accepted Deposited

37. • Over 800 lines of code
• Majority of code is data transforma4on and
cleanup
• Supervised modeling
• Logis4c regression
Model

38. Why R?
• Open-source (free to use, improve on, and redistribute)
• Runs on most standard OS
• Released frequently
• Graphics capabili4es are be]er than in the most other analy4cal packages
• Huge and very ac4ve user community
• It provides analysts more control over what changes to make and
what assump4ons to test.
Tools

39. • Excel
• RDBMS database
• CRM
Tools

40. Why In-house?
• Why in-house?
• Most data cleaning can only be done in-house.
• Colleges have a be]er control of the modeling
process.
• The process can be repeated once the codes are
wri]en.
• More transparency and evalua4on can be done in
house.

41. Step Five—Evalua4on

42. Evalua4on step 1 – score models
• Is model good enough?
– Likelihood Ra4o test
– Area under the ROC curve
• Are predic4ons accurate?
– Confusion matrix
• Which Predictors are most important?

43. Evalua4on Ques4on –
Are predic4ons accurate?

44. Evalua4on step 2 – review the model
• Did we miss anything?
• Any assump4ons violated?

45. Evalua4on step 3 – next step
• Deploy vs. recreate model

46. Step Six—Deployment

47. Actually using your model!
• Automa4on
• Getng feedback from model’s output users
• Experimen4ng and feeding the new learning
back to the model to improve its accuracy
• Monitoring outcomes of the model use

48. Deploy Results—
Be Responsive & Improve the Admissions
Prac4ce
1. The admissions
office will
receive a file
every two
weeks.
2. The file will be
uploaded to
Recruiter.
3. The VP of
Enrollment will
take ac4ons.

49. Challenges in Using Predic4ve Analy4cs
• Obstacles in management
No champion for the work
• Obstacles with data
Data need à Data infrastructure à Data consistency
• Obstacles with modeling
Analyst too zealous or too ambi4ous
Model is too complex (overfitng – significant
rela4onshpis are just noise)

51. Next Steps
Build more analy4c work
• What areas or school districts should we spend more resources on
(visits, calls, marke4ng campaign)?
• How do we improve the academic profile of incoming students?
• How can we create new aid models to leverage our intui4onal aid
to achieve higher yield?
• Reten4on - which students are most likely to drop out/transfer?
• U4lizing unstructured data to gain addi4onal insights
Actually USING the results
• What ac4ons need to take place based on scores generated from
predic4ve models?
• How do we assess the effec4veness of our upliuing marke4ng
campaigns (including customized emails, number of phone calls,
and other marke4ng ac4vi4es)?

52. How else can data analy4cs help my
school?
• Increasing students’ reten4on
• Increasing students’ gradua4on
• Increasing students’/teachers’ performance
• Reducing students’ absence

53. Take Away Messages
• Be realis4c to narrow the scope of the predica4ve
modeling work at the ini4al stage
• Evalua4on of models should be in the process
• Building data structure is more important than
methodology

54. Predic4ve Modeling Steps and Details
in the Context of College Enrollment
Details in strategic
enrollment
management process
but very general
guideline on modeling
(Page 223-227).
Prac4cal sugges4ons on
predic4ve analy4cs but focus
on the economic field of fraud
detec4on & customer
sa4sfac4on.
Link the two sides
Build predic&ve analy&cs
in the context of college
enrollment

55. References
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professional data analyst (17). Indianapolis, IN: Wiley.
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analyst (19). Indianapolis, IN: Wiley.
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answer
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57. Thank you!
• Email: yagirov@harrisburgu.edu
• Twi]er: @FeyziBagirov