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
4. 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
12. Managing Expectations
* ROI metrics are complicated
* Risk: Oversimplifying complex scenarios
* Each user = Master of the outcome
13.
14. Managing Expectations
* ROI metrics are complicated
* Risk: Oversimplifying complex scenarios
* Each user = Master of the outcome
* Be consistent over time
19. Quantifiably ElusiveVariables (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
20. A few QEVs can be quantified…
Exchange Rates over time
21. 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
22. 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
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. 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
25. Model Development
* More data => Better model
- Multi-year data is critical for partnerships…
* Differentiate time and money
- According to markets and types
Partnerships
Sponsors
Other / Nothing
26. 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
