HR analytics is essential for measuring and managing workforce performance, linking business objectives with employee data, and improving return on investment. It involves collecting and analyzing various data points like attrition rates and recruitment metrics to provide insights for strategic decisions in hiring, retention, and talent management. Key applications include workforce planning, targeted retention strategies, risk management, and predictive analytics to enhance organizational effectiveness.

1. HR Analytics
What is it good for?

3. Why HR Analytics?
• Measure & Manage
• Link Business and People
• Improve Performance
• Increase Return on investment
• Because, now we can!
Caretaker Partner Thought Leader

4. Understanding HR Analytics
Data
• Information
•Attrition Rate is
15%
Metrics
• Interpretation
• Attrition
increased by
3% over last
year
Analytics
• Insights
• Decline in sales
team members
earning incentives
contributed to
2.5% increase in
attrition
Analytics =/= Analysis
Better metrics = Better Analytics
Logic > Quantity of Data
Business Intelligence – From What to Why!

5. Elementary Model for HR Analytics
Collect
Employee Data
Hiring Data
Engagement Surveys
Attrition Data
Exit Interviews
Rewards data
External Benchmarks
Performance ratings
Training Inputs…
Connect
Multivariate Analyses
Regression /
Correlation
SWOT
Six Sigma / Lean
Risk Analysis
Correct
Trend
Decide
Prioritize
Control
Monitor / Track
Dashboards
Continuous
improvement
Ref: StratEval Analytics LLC
Hindsight Insight Foresight

6. Availability of metrics…
• External Hire Rate
• New Position Recruitment
Ratio
• Recruitment Source
Breakdown
• Rehire Rate
• Career Step Ratio
• Cross-Function Mobility
• Promotion Rate
• Promotion Speed Ratio
• Upward Mobility
• Applicant Ratio
• Average Interviews per Hire
• Average Sign-On Bonus
Expense
• Average Time to Fill
• Interviewee Offer Rate
• New Hire Failure Factor
• On-Time Talent Delivery Factor
• Recruitment Cost per Hire
• Referral Conversion Rate
• Referral Rate
• Involuntary Termination Rate
• New Hire Turnover Contribution
• Retention Rate
• Exits by Performance Rating
• Termination Reason Breakdown
• Voluntary Termination Rate
• Employee Engagement Index
• Market Opportunity Index
• Turnover Cost Rate—< 1-Year
Tenure
• Average Performance Appraisal
• Employee Upgrade Rate
• Performance Rating Distribution
• Performance-Based Pay
Differential
• Employee Satisfaction with
Leadership
• Manager Quality Index
• Positions Without Ready
Candidates Rate
• Average Training Class Size
• E-Learning Abandonment
Rate
• Training Expense per.
Employee
• Training Hours per FTE 8
• 3. Bonus Actual to Potential
Rate
• Bonus Eligibility
• Compensation Satisfaction
• Benefits Satisfaction
• Number of Options
Exercised per Employee
• Workforce Demographic
Structural Tenure
• Average Workforce Age
• Ethnic Background
Breakdown
• Diversity Demographics
• Average Span of Control
• Tooth to tail ratio
• Staffing Rate—Part
• Staffing Rate—Temporary
• Average Workforce Tenure
• 1. Human Investment Ratio
• Operating Expense per FTE
• Operating Profit per FTE
• Return on Human
Investment Ratio 7
• Market Capitalization per
FTE
• Revenue per FTE

7. Analytics don’t happen by themselves

8. Key remains in linking the data

9. Applications in Practice
Manpower
Planning
HR
Analytics
Hiring
HR Ops
Learning &
Devpt
Performance
Attrition
Succession
Planning
Compensation
& Rewards

10. Critical areas for HR Predictive analytics
Planning & Hiring
• Where Should we
hire from
• What is my ideal
candidate profile
• Are there
redundancies in
the hiring process
• Should factors
such as commute
time influence
hiring decisions?
Targeted retention
• Why did people
leave in the past?
• Can causes of
attrition be
eliminated before
they cause harm?
• How to focus
retention
activities on
critical few
people?
Risk Management
• Quantifying
operational risks
• Profiling of
candidates to
forecast right
fitment
Talent Forecasting
• Predict potential
amongst high
performers and
move them in to
fast track
programs

11. Pitfalls of HR Analytics

12. Some Pointers to Remember
• Start with the problem, not the data
• Explore the opportunities. Avoid Analysis paralysis
• Data cleaning and consistency continues to be a concern
• Computer science cannot replace curiosity

14. Thank You