The document discusses a case competition project focused on optimizing university tier assignments for improved recruiter resource allocation and increased acceptance rates of qualified candidates. Key findings suggest that tier 3 should cover more universities while tiers 1 and 2 maintain current coverage, with recommendations to optimize resource allocation using a fine-tuned lead scoring model. The analysis indicates that proper tier assignments can enhance the number of students hired and improve acceptance rates across various tiers.

1. Team members: Yifei Tang
Donglin Jia
Mohan Wang
Wharton People Analytics Conference
Case Competition

2. Project Objective
Project Methodology
Key Findings
Recommendations
Q&A
Overview
2

3. Measure university performance based upon tier
adjustments
Find the optimal tier and the best tier assignment for
each university
Optimize recruiter resource allocation and maximize
the number of qualified candidates
Project Objective
3

4. Tier 3 should cover more universities and the number of
uncovered universities should be reduced
Tier 1 and Tier 2 should keep their current coverage
We suggest:
Tier 1 : Tier 2 : Tier 3 = 83 : 81 : 208
Recommendations
4

5. Project Methodology
Transfer variables from student level to university level, to
be used as indicators to measure productive universities
Cluster universities using Performance-Potential Matrix
Perform Lead Scoring Analysis to calculate expected
number of qualified applicants
Leverage Large-scale Linear Programming to find optimal
solution for recruiter resource allocation
5

6. 2016 only 2017
only
126 279 12
Our Focus
86 417 23
Target Universities
6
0
20
40
60
Number of schools
0
10
20
<5 5--10 10--15
Number of applicants
We only focus 417 universities that add value
Inactive universities: 86
Unknown universities: 23

7. 7
 In 2017, accepted rate in tier 2 & 3 were roughly same to that
in 2016, which could be further improved
Fine Tuned Tier Policy Is On The Right Track
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Tier1 Tier 2 Tier 3
Accepted rate for each tier
20172016 20162017 2017

8. Evaluation Of Tier Policy Change
High Performance
High Potential
Low Potential
Low Performance
8
Improperly upgrade resulted in waste of recruiter resource
Improperly downgrade led to loss of potential candidates

9. Lead Scoring Model
Purpose: how many students can be hired from
each university if it was NOT in its current tier?
Based upon the predicted number of students,
optimize the allocation of recruiters
Example:
University of Husky was in Tier-2 in 2016 and in Tier-1 in 2017.
How many students can be hired from University of Husky if it
was in Tier-2 in 2017? What if in Tier-3?
9

10. Segment Universities
Tier-2
Tier-2
Tier-3
None
Tier-1
2016 2017
#3
Similar features
&increase rate
Predict which segment
these universities belong to
based on their features
Conversion Probability: If these universities were in Tier-1 in 2017, how likely
the number of students hired would increase by a certain rate
10

11. Sample Coefficient Table
University Tier 1 Tier 2 Tier 3 No Policy
# 1 43.00 29.83 25.83 0.00
# 2 5.00 3.82 2.49 0.00
# 3 12.00 4.19 1.43 0.00
# 4 6.61 4.08 3.00 0.00
… … … … …
 Generally speaking, the higher the tier, the more students can be
hired
 However, with limited recruiter resource and varied sensitivity to
tiers of each university, we have to optimize the tier allocation to
maximize the number of students can be hired
11

12. Tier 1
79
Tier 2
118Tier 3
94
No Policy
126
Large-scale Linear Optimization
Tier 1
83
Tier 2
81Tier 3
208
No Policy
45
Optimized Allocation: Current Allocation:
 Strategy #1: Tier 3 should cover more universities
 Strategy #2: Tier 1 & Tier 2 should keep their current coverage
 Number of students who accepted the offer is predicted to
increase by 28.1%, reaching 3059
12

13. Questions & Discussion
13