MODEL
Nordic Compass Analytics
Illustration Recruitment Analysis
Recruitment operations for three different groups, viz. R&D, IT and Sales have been assigned to an HR Leader. The objective of his assignment is to ensure successful closing of job positions within set deadlines. Any position that closes past the deadline needs to be studied in order to avoid omissions in future. For this purpose, a recruitment analytics model is to be set up so that a regular report of everyday operations is made available for senior management.
GOAL
PURPOSE
SCOPE
X-FACTOR
LOGIC
MODEL
TEST
SIGN OFF
ILLUSTRATION
GOAL : Analytics for Recruitment Operations
PURPOSE : To illustrate performance, identify recruitment delays - causes and solutions
STORY : Recruitment story by Objective, Performance and Process Life Cycle
Objective :
To post job openings and close them within set deadline
Performance :
No. of Positions closed within deadline / No. of positions opened
Process Life Cycle :
SELECTION PROCESS
DEADLINE
1
2
POSITIONS OPENED
3
POSITIONS CLOSED
4
5
POSITIONS PENDING
6
POSITIONS CLOSED
X-FACTOR : 1) Positions Opened, 2) Positions Closed On-Time (Within Deadline)
3) Positions Pending, 4) Positions Closed Late (After Deadline)
LOGIC : Closed On-Time = Positions Opened - Missed Deadline
Missed Deadline = [Closed Late + Pending]
MODEL :
METRICS : KPI or Performance as defined by Goal and Purpose
Performance => Close Rate => [ Closed On-Time / No. of Positions ]
ANALYSIS : Logical Construct of "Close Rate" as defined by Story, X-Factors and Logic
TEST ANALYSIS
PART I
[Metrics]
PART II
[Analysis]
PART III
[Insights]
SCENARIO
INSIGHTS : In-depth Analysis of "Missed Deadline" with findings and recommendations
|Close Rate|Score|Closed On-Time|No. of Positions|Missed Deadline|Closed Late|Pending|
[A/B] % [A/B] A B [C+D] C D
|Missed Deadline| Closed Late - Why? | Pending - Why? | Findings and Recommendations
TEST : Testing of Model using imaginary data to create a sample report of Recruitment Analysis
SIGN OFF : Model verified against "Scope" and "Purpose" on sample data
Model vs Data
