Based on the recruitment data, provide the market segmentation and scholarship optimization model.

1. DSO 573
USC Dworak-Peck School of Social Work : Recruitment Case
By:
Musab Alafaliq, Nikhil Gupta, Evie Wang, Fan Yang

2. What’s happening
2
No. 11
No. 12
No. 13
No. 14
No. 15
No. 16
No. 17
No. 18
‘14 ‘15 ‘16 ‘17 ‘18
Graduate School of Social Work Ranking Decreased
Boston College
University of Pennsylvania
University of Pittsburgh
University of Wisconsin
UCLA
Ohio State University
USC - PECK
University of Denver
Column: # students that school gave the offer
Line: Conversion rate = # Accepted offers / # School offers
# Total Admitted Students & Conversion % Decreased

3. What we found
3
Admitted
Students
Enrolled
Students
Difference
11.87 11.47 0.40
Cohort Quality Score
Increased # of
students who
rejected school
offersDecreased
Conversion
Rate
Decreased # of
applications
Rejected students
tend to have higher
quality

4. What we are going to do
4
Student Quantity Student Quality
• Attract more students to apply
• Increase student offer acceptance rate
• Acquire better qualified students
• Prevent high quality students from going
to competitors

5. Strategy overview
55
currentfuture future
Prospective Students
Applicants
Admits
Confirms
Enrolls
Expand the number of applicants by
acquiring higher quality, more committed
prospective students with targeted
marketing approaches.
Increase student confirmation rate and
prevent offer rejections from high
quality students.

6. Solutions
66
Location-based acquisition marketing Scholarship offering optimization
Enhance acquisition marketing efficiency by targeting
high performance locations with tailored promotion
content:
o Low-hanging fruit: Identify and market to high-
performing locations with low application quantities.
o Tailored contents: Provide tailored marketing
messages based on student location & demographics.
o Expand marketing target locations: Market to new
locations similar to current high performance
locations.
Improve high quality student offer acceptance rate while
balancing fairness and diversity:
o Quality scoring: Develop metrics to score candidates’
quality.
o Acceptance prediction: Predict the likelihood of
student offer acceptance.
o Continuous quality improvement: Provide
scholarships to students with higher quality but lower
likelihood to join while balancing students’ financial
and diversity status at the same time.

7. Location-based acquisition marketing - model intro
7

8. Location-based acquisition marketing - target cluster
attributes
8
Results - cluster attributes
8
Clusters Segments Offer acceptance
Reviewer
score avg.
Applicants
size
Education
resource
Economic
indicators
Locations
1 Elite
performers
3rd highest acceptance
prediction (0.534)
Highest quality
score
3rd largest (844) Medium resource and
local competition
Highest income
level, medium
unemployment rate
CA,FL, WI,
NJ, CO, OR
2 Chance seekers Highest acceptance
prediction (0.563)
Lowest quality
score
Lowest (272) Rich resource and
highest local
competition
Medium income
level, low
unemployment rate
IL, TX, WA,
Columbia
3 Mid-class
strivers
2nd highest
acceptance prediction
(0.562)
2nd highest
quality score 5th (383)
2nd competitive local
education resources
Medium income
level, lowest
unemployment rate
South CA, NJ
4 Affluent Calis 4th highest acceptance
prediction (0.323)
3rd highest
quality score
2nd largest
(1302)
Medium resource and
local competition
High income level,
medium
unemployment rate
CA
5 Neighborhood
triers
Low acceptance
prediction (0.241)
5th highest
quality score
Largest (1967) Few resource and low
competition
Low income, high
unemployment
South CA
6 Outsiders Lowest acceptance
prediction (0.216)
4th highest
quality score
4th (518) Few resource and low
competition
Low income, high
unemployment
South CA
X X
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9. Location-based acquisition marketing - geo-targeting
9
Targeted clusters (1,3,4) location distribution with applicant size

10. Location-based acquisition marketing - promotion
content customization
10
Elite performers Mid-class strivers Affluent Calis
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Customized email campaign titles
and rich content based on cluster
demographic content.

11. Scholarship offering optimization - 1
11
Objective:
Increase quality of students entering the
program through efficient utilization of
scholarship offers
Methodology:
•Score applicants as per their probability of
not joining the program given admits
•Used Logistic Regression
•Dependent variable: ‘Response’
•Use output to redirect the efforts of
school administration in scholarship offers
Compare before and after metric
Negatively related to
conversion
Positively related to
conversion
Category of Variable
Age Distance LT 150 Individual
Time between admit &
response
Individual
Undergraduate GPA Academic
First Reviewer Score Academic
Salaries & Wages Socio-economic
Educator Rate Socio-economic
Returns with Student
Loan
Socio-economic

12. Scholarship offering optimization - 2
12
Withdrawn students have a higher CQS than the admitted student CQS
If scholarships are offered based on our model recommendations, we expect an
increase in the CQS for the accepted student population
Cohort Quality Score
This is the mean reviewer
scores for population of interest

13. Constraints and next steps
13

14. THANK YOU