In today's tuition-driven budget environments, measuring Return on Investment (ROI) has become a critical imperative. Join us for a live demonstration of a working model that enables colleagues to assign values to both quantitative and qualitative variables related to the entire International Enrollment Management cycle.

1. Measuring Return on Investment
in International Student
Recruitment: A Working Model
Advanced Session at the NAFSA Annual Conference in Denver, 31 May 2016

2. Co-Presenters:
Cheryl DarrupBoychuck, USjournal of Academics
cheryl@USjournal.com
George Kacenga, University of Colorado Denver
George.Kacenga@UCDenver.edu
Angeliki Rigos, Merrimack College
rigosa@merrimack.edu

3. Learning Objectives:
1. Define ROI metrics, as they pertain to your
campus environment
2. Explore “quantifiably-elusive” factors,
balancing ethical issues and practical realities
3. Play with the model, to evaluate your
institutional ROI as it changes over time

4. Brief Overview

5. Brief Overview

6. Brief Overview

7. Managing Expectations
* ROI metrics are complicated

10. Managing Expectations
* ROI metrics are complicated
* Risk: Oversimplifying complex
scenarios

11. Managing Expectations
* ROI metrics are complicated
* Risk: Oversimplifying complex
scenarios
* Each user = Master of the outcome

13. Managing Expectations
* ROI metrics are complicated
* Risk: Oversimplifying complex
scenarios
* Each user = Master of the outcome
* Strive for simplicity

15. Two-Asset Portfolio

16. Portfolio Strategy

17. ROI – Definitions and Usefulness

18. Quantifiably Elusive Variables (QEVs)
Both Input and
Output Variables
Currency fluctuations
Visa policy fluctuations
Sufficient budget
Input Variables that
can be quantified
Input Variables
Top-level support
Well-defined strategy
Word-of-mouth referrals
Academic relevance
Faculty involvement
Efficiency of operations
Diversification of students
Alumni engagement
Prestige factor
Partnerships

19. A few QEVs can be quantified…
Exchange Rates over time

20. QEVs within the model
Quantifiably-Elusive Variables Importance Type
1 to 13 China Gulf States Brazil
1 Top-level support 13 input 5 7 3
2 Sufficient budget 11 input 5 3 -5
3 Well-defined strategy 10 input 5 5 3
4 Prestige factor 5 both 5 5 5
5 Word-of-mouth referrals 7 input 5 8 3
6 Currency fluctuations 2 input -1 0 -3
7 Visa policy fluctuations 1 input 5 3 5
8 Academic program relevance 8 input 7 7 7
9 Faculty involvement 3 input 3 3 3
10 Partnerships 9 both 8 5 0
11 Alumni relations 4 both 0 3 0
12 Efficiency of operations 12 input 7 7 7
13 Diversification of student body 6 both -1 3 8
14 Other n/a n/a
Performance: -10 to +10

21. Qualitative ROI
* Useful for comparing markets
- Based on the performance of key variables
Exchange Rates
 Visa Fluctuations
Strength of Partnerships
* Most useful for developing market-specific
strategies

22. Quantifiably-Elusive Variables Importance Type
1 to 13 China Gulf States Brazil
1 Top-level support 13 input 5 7 3
2 Sufficient budget 11 input 5 3 -5
3 Well-defined strategy 10 input 5 5 3
4 Prestige factor 5 both 5 5 5
5 Word-of-mouth referrals 7 input 5 8 3
6 Currency fluctuations 2 input -1 0 -3
7 Visa policy fluctuations 1 input 5 3 5
8 Academic program relevance 8 input 7 7 7
9 Faculty involvement 3 input 3 3 3
10 Partnerships 9 both 8 5 0
11 Alumni relations 4 both 0 3 0
12 Efficiency of operations 12 input 7 7 7
13 Diversification of student body 6 both -1 3 8
14 Other n/a n/a
Performance: -10 to +10

23. Quantifiably-Elusive Variables Importance Type
1 to 13 China Gulf States Brazil
1 Top-level support 13 input 5 7 3
2 Sufficient budget 11 input 5 3 -5
3 Well-defined strategy 10 input 5 5 3
4 Prestige factor 5 both 5 5 5
5 Word-of-mouth referrals 7 input 5 8 3
6 Currency fluctuations 2 input -1 0 -3
7 Visa policy fluctuations 1 input 5 3 5
8 Academic program relevance 8 input 7 7 7
9 Faculty involvement 3 input 3 3 3
10 Partnerships 9 both 8 5 0
11 Alumni relations 4 both 0 3 0
12 Efficiency of operations 12 input 7 7 7
13 Diversification of student body 6 both -1 3 8
14 Other n/a n/a
Performance: -10 to +10

24. Model Development
* More data => Better model
- Multi-year data is critical for partnerships…
* Differentiate time and $$
- According to markets and types
Partnerships
Sponsors
Other / Nothing

25. Questions and Answers
Cheryl DarrupBoychuck, USjournal of Academics
cheryl@USjournal.com
George Kacenga, University of Colorado Denver
George.Kacenga@UCDenver.edu
Angeliki Rigos, Merrimack College
rigosa@merrimack.edu