This document discusses HR analytics, including core concepts, stages of analytics, types of analytical models, analytics domains, tools and software, and case studies. It provides an overview of business intelligence and analytics, then discusses HR analytics in depth. Key areas covered for HR analytics include what is currently measured, what could be measured, predictive possibilities like turnover modeling, and examples of advanced predictive analyses. Real-world case studies are presented on companies that have successfully used analytics for outcomes like reducing attrition and identifying high performers. The importance of HR analytics as an organizational function is highlighted.

1. HR Analytics
- Akshay Raje

2. Agenda
1. Core concepts, terminologies & buzzwords
Business Intelligence, Analytics
Big Data, Cloud, SaaS
2. Analytics
Types, Domains, Tools…
3. HR Analytics
Why? What is measured?
How? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model

3. Core concepts and terminologies
Decision
Analytics
=
Business Intelligence

4. Core concepts and terminologies
“
Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
“
Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.
1. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, N.J: Wiley & Sons. ISBN 978-0-470-39240-9. 2. Beller, Michael J.; Alan Barnett (2009-06-18). "Next Generation Business Analytics". Lightship Partners LLC

5. History of BI and Analytics
•Decision support systems (DSS) began in the 1960s as computer-aided models created to assist with decision making and planning.
•From DSS, data warehouses, Executive Information Systems, OLAP…
•…and finally Business Intelligence came into focus beginning in the late 80s.
•Because of the rigidness of enterprise level BI tools, analytics started gaining traction in mid 00’
Logistics & Supply Chain Analytics
1980’s Financial & Budget Analytics
Integrated Supply Chain
Integrated ERP & Financial Analytics
Customer Analytics, CRM & Data Warehousing
Customer Segmentation and Shopping Basket
Web Behavior Analytics
Predictive Customer Behavior
Recruiting, Learning, Performance Management
Integrated Talent Workforce Planning
Business-driven Talent Analytics
Predictive Talent Models – HR Analytics
Early 1900s
1950s-60s
1970s-80s
Today
Industrial Economy
Financial Economy
Consumer & Web Economy
Talent Economy
Steel, Oil, Railroads
Conglomerates
Segmentation & Personalization
Globalization, Diversity, Skill & Leadership shortages
Source: Bersin & Associates

6. Buzzwords
#BigData
#Cloud
#SaaS

7. Agenda
1. Core concepts, terminologies & buzzwords
Business Intelligence, Analytics
Big Data, Cloud, SaaS
2. Analytics
Stages, Types, Domains, Tools…
3. HR Analytics
Why? What is measured?
How? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model

8. Stages of Analytics
Reporting
What happened?
Analysis & Monitoring
Why did it happen? What is happening now?
Predictive Analytics What can happen?
Complexity
Business value

9. Types of Analytical Models
Reporting
Analysis & Monitoring
Predictive Analytics
Past Data
Current Data
Future
PREDICTS
PREDICTS
PREDICTS
Drawing Conclusions or Inferences
Representation of Data and Summarizing
INFERENTIAL ANALYTICS
DESCRIPTIVE ANALYTICS
REPORT
PREDICTIVE ANALYTICS

10. Analytics Domains
Retail Sales Analytics
Financial Services Analytics
Risk & Credit Analytics
Talent / HR Analytics
Marketing Analytics
Consumer Behavior & Cohort Analytics
Collections Analytics
Fraud Analytics
Pricing Analytics
Telecom Analytics
Supply Chain Analytics
Web Analytics

11. Tools, Matrices, Software
Reporting
Metric Types
Description
Rate
Proportion of one or more parts to a whole of 100%
Ratio
One number relative to another, often expressed as a reduced fraction
Composition
Breakdown of a whole into its parts, showing the number or percentage allocated to each
Index
Weighted combination of disparate data into one number relative to a scale or anchor
Volume
Number of people or units with a characteristic, or occurrences of an event
Cost
Organizational expenses, revenues, profits, or value
Time
Process cycle time, volume of time invested, or timeliness of events
Quality
Performance of people, processes, or systems
Satisfaction
Participants’ subjective perceptions of a process, program, or experience
Typical tools / software:
•Microsoft Excel (max used)
•BI reporting tools
•ERP reporting tools, dashboards

12. Tools, Matrices, Software
Analysis & Monitoring
Typical tools / software:
•Microsoft Excel (limited usage)
•BI reporting tools
•Statistical software like SAS, R etc
Representation of Data:
•Frequency Distributions: Relative and Percent Frequency
•Graphs: Bar, Pie, Dot Plot, Histogram, Ogive
•Cumulative Distributions Measures of central tendency:
•Mathematical: Arithmetic / Geometric / Harmonic Mean
•Positional: Median, Mode Measures of dispersion: character of variability in data
•Absolute: Range, Quartile / Mean / Standard Deviation
•Relative: Coefficient of Range / QD / MD / variation Correlation: degree or extent to which two or more variables fluctuate with reference to one another
•Pearson Correlation: Correlation for Continuous data
•Spearman Correlation: Correlation for Ordinal Data
DESCRIPTIVE ANALYTICS

13. Tools, Matrices, Software
Analysis & Monitoring
Typical tools / software:
•Statistical software like SAS, R etc
•Survey tools
Sampling Types:
•Random
•Systematic Sampling
•Stratified
•Cluster Sampling Statistical inference: Inference about a population from a random sample drawn Confidence Intervals: Using standard error (SE) for applying confidence intervals to estimates Hypothesis Testing: Assertion regarding the statistical distribution of the population
INFERENTIAL ANALYTICS

14. Tools, Matrices, Software
Predictive Analytics
Typical tools / software:
•Statistical software like SAS, R etc
Regression:
•Linear Regression
•Non Linear Regression Factor Analysis Cluster Analysis

15. Over to Dilbert 

16. Agenda
1. Core concepts, terminologies & buzzwords
Business Intelligence, Analytics
Big Data, Cloud, SaaS
2. Analytics
Stages, Types, Domains, Tools…
3. HR Analytics
Why? What is measured?
What can be measured? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model

17. Why HR Analytics?
Measure & Manage
Return on Investment
Linkage of Business Objectives and People Strategies
Performance Improvement
“What gets measured, gets managed; What gets managed, gets executed” - Peter Drucker
“The business demands on HR are increasingly going to be on analysis just because people are so expensive“
- David Foster
“ To clearly demonstrate the interaction of business objectives and workforce strategies to determine a full picture of likely outcomes” HR Dashboards - SAP
“Global organizations with workforce analytics and workforce planning outperform all other organizations by 30% more sales per employee.” - CedarCrestone

18. Steps in HR Analytics
Hindsight
Insight
Foresight
Gather data by Reporting
Make sense of data by Analysis and Monitoring
Develop predictive models

19. What is generally measured/tracked today?
63%
52%
48%
45%
37%
31%
30%
27%
27%
Employee Engagement
Performance Ratings
Retention / Turnover
HIPOs & HIPO pipeline
% employees with dev plans
Readiness for job
Internal hire %age
Diversity of workforce
Level of expertise / competance
Source: Bersin & Associates 2012 – US research

20. What should/could be measured?
HR Matrices
Recruitment
Retention
Performance & Career Management
Training
Comp & Benefits
Workforce
Organization effectiveness

21. Recruitment
Recruitment
Internal Movement
Staffing Effectiveness
1.Employment Brand Strength
2.External Hire Rate
3.Net Hire Ratio
4.New Position Recruitment Rate
5.New Position Recruitment Ratio
6.Recruitment Source Breakdown
7.Recruitment Source Ratio
8.Rehire Rate
1.Career Path Ratio
2.Cross-Function Mobility
3.Internal Hire Rate
4.Internal Placement Rate
5.Lateral Mobility
6.Promotion Rate
7.Promotion Speed Ratio
8.Transfer Rate
9.Upward Mobility
1.Applicant Interview Rate
2.Applicant Ratio
3.Average Interviews per Hire
4.Average Sign-On Bonus Expense
5.Average Time to Fill
6.Average Time to Start
7.Interviewee Offer Rate
8.Interviewee Ratio
9.New Hire Failure Factor
10.New Hire Performance Satisfaction
11.New Hire Satisfaction Offer Acceptance Rate
12.On-Time Talent Delivery Factor
13.Recruitment Cost per Hire
14.Recruitment Expense Breakdown
15.Referral Conversion Rate
16.Referral Rate
17.Sign-On Bonus Rate

22. Retention
Turnover
Employee Engagement
Cost of Turnover
1.Involuntary Termination Rate
2.New Hire Turnover Contribution
3.Retention Rate
4.Termination Breakdown by Performance Rating
5.Termination Reason Breakdown
6.Voluntary Termination Rate
1.Employee Commitment Index
2.Employee Engagement Index
3.Employee Retention Index
4.Market Opportunity Index
5.Offer Fit Index
1.Average Termination Value
2.Average Voluntary Termination Value
3.Termination Value per FTE
4.Turnover Cost Rate—< 1-Year Tenure

23. Performance & Career Management
Performance Management
Career Management
1.Average Performance Appraisal Rating
2.Employee Turnaround Rate
3.Employee Upgrade Rate
4.High Performer Growth Rate
5.Peer Review Rate
6.Performance Appraisal Participation Rate
7.Performance Rating Distribution
8.Performance-Based Pay Differential
9.Performance Contingent Pay Prevalence
10.Self Review Rate
11.Upward Review Rate
1.Cross-Function Mobility— Managers
2.Employee Satisfaction with Leadership
3.LDP Prevalence Rate
4.Manager Instability Rate
5.Manager Quality Index
6.Positions Without Ready Candidates Rate
7.Successor Pool Coverage
8.Successor Pool Growth Rate

24. Training & Development
Training
Education & Development
1.Average Training Class Size
2.E-Learning Abandonment Rate
3.Employee Satisfaction with Training
4.Training Channel Delivery Mix
5.Training Course Content Breakdown
6.Training Expense per Employee
7.Training Hours per FTE
8.Training Hours per Occurrence
9.Training Penetration Rate
10.Training Quality
11.Training Staff Ratio
12.Training Total Compensation
13.Expense Rate
1.Development Program Penetration Rate
2.Educational Attainment Breakdown
3.Staffing Rate—Graduate Degree
4.Staffing Rate—High Potential
5.Tuition Reimbursement Request Rate

25. Compensation & Benefits
Compensation
Benefits
Equity
1.Average Cost Rate of Contractors
2.Average Hourly Rate
3.Bonus Actual to Potential Rate
4.Bonus Compensation Rate
5.Bonus Eligibility Rate
6.Bonus Receipt Rate
7.Compensation Satisfaction Index
8.Direct Comp Operating Expense Rate
9.Direct Compensation Breakdown
10.Direct Compensation Expense
11.per FTE
12.Market Compensation Ratio
13.Overtime Expense per FTE
14.Overtime Rate
15.Total Compensation Expense per FTE
16.Upward Salary Change Rate
1.Benefits Expense per FTE
2.Benefits Expense Type Breakdown
3.Benefits Operating Expense Rate
4.Benefits Satisfaction Index
5.Benefits Total Compensation Rate
1.Average Number of Options per Employee
2.Equity Incentive Value per Employee
3.Net Proceeds of Options per Employee Exercising
4.Number of Options Exercised per Employee
5.Stock Incentive Eligibility Rate

26. Workforce
Demographic
Structural
Tenure
1.Age Staffing Breakdown
2.Average Workforce Age
3.Ethnic Background Staffing Breakdown
4.Gender Staffing Breakdown
5.Staffing Rate—Disability
6.Staffing Rate—Female
7.Staffing Rate—Minority
8.Staffing Rate—Multilingual
1.Average Span of Control
2.Customer-Facing Time Rate
3.Employee Ownership Rate
4.Employment Level Staffing Breakdown
5.Function Staffing Breakdown
6.Staffing Rate—Corporate
7.Staffing Rate—Customer Facing
8.Staffing Rate—Managerial
9.Staffing Rate—Part Time
10.Staffing Rate—Revenue Generating
11.Staffing Rate—Temporary
12.Staffing Rate—Union Employees
1.Average Workforce Tenure
2.Organization Tenure Staffing
3.Breakdown Staffing Rate—< 1- Year Tenure

27. Organizational Effectiveness
Productivity
Structural
Innovation
1.Human Investment Ratio
2.Operating Expense per FTE
3.Operating Profit per FTE
4.Operating Revenue per FTE
5.Other Labor Rate
6.Return on Human Investment Ratio
7.Work Units per FTE
1.Corporate Expense Rate
2.Employee Stock Ownership Percentage
3.Intangible Asset Value per FTE
4.Market Capitalization per FTE
1.New Products & Services
2.Revenue per FTE R&D Expense Rate

28. Critical areas for HR Predictive analytics
1.Turnover modeling. Predicting future turnover in business units in specific functions, geographies by looking at factors such as commute time, time since last role change, and performance over time.
2.Targeted retention. Find out high risk of churn in the future and focus retention activities on critical few people
3.Risk Management. Profiling of candidates with higher risk of leaving prematurely or those performing below standard.
4.Talent Forecasting. To predict which new hires, based on their profile, are likely to be high fliers and then moving them in to fast track programs

29. Advanced Analysis & Predictive examples
Problem statement: An Indian MNC has a linear growth model. It wants to identify relationship between % revenue growth and % headcount growth. They have revenue and headcount details for past 10 years. Solution approach:
•Identify the correlation coefficient based on the type of data and plot a scatter plot.
•Given that revenue growth is estimated at X% for the next year, we can predict headcount growth
1
Problem statement: An HR manager identify 20 variables such as educational qualification, college, age, gender, nationality etc. that predicts the hiring effectiveness. He wants to identify mutually exclusive variables which affect hiring effectiveness. Solution approach:
•Using factor analysis , mutually exclusive factors can be identified
2

30. Advanced Analysis & Predictive examples
Problem statement: Campus hiring team is interested in how variables, such as entrance test score conducted by company, GPA (grade point average) and prestige of the institution, effect selection . The response variable, selected/not selected, is a binary variable Solution approach:
•Selection data is collected for past 5 years for the above parameters indicated.
•Here dependent variable is selected/not selected( Selected =1, Not Selected= 0) and independent variables are Test Score, GPA, Prestige of the institute.
•Using logistic regression a equation can be developed
3
Problem statement: A company conducted a employee engagement survey using a questionnaire developed by internal HR team. The questionnaire had 15 questions and responses were collected from 50 employees. As a HR manager, we want to identify mutually exclusive factors Solution approach:
•Using factor analysis , mutually exclusive factors can be identified
4

31. Over to Dilbert 

32. Agenda
1. Core concepts, terminologies & buzzwords
Business Intelligence, Analytics
Big Data, Cloud, SaaS
2. Analytics
Stages, Types, Domains, Tools…
3. HR Analytics
Why? What is measured?
What can be measured? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model

33. Real world case studies
Starbucks, Limited Brands, and Best Buy—can precisely identify the value of a 0.1% increase in employee engagement among employees at a particular store. At Best Buy, for example, that value is more than $100,000 in the store’s annual operating income.
Many companies favor job candidates with stellar academic records from prestigious schools—but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job.
Employee attrition can be less of a problem when managers see it coming. Sprint has identified the factors that best foretell which employees will leave after a relatively short time.
In 3 weeks Oracle was able to predict which top performers were predicted to leave the organization and why - this information is now driving global policy changes in retaining key performers and has provided the approved business case to expand the scope to predicting high performer flight .

34. Real world case studies
Dow Chemical has evolved its workforce planning over the past decade, mining historical data on its 40,000 employees to forecasts promotion rates, internal transfers, and overall labor availability. Dow uses a custom modeling tool to segment the workforce and calculates future head count by segment and level for each business unit. These detailed predictions are aggregated to yield a workforce projection for the entire company. Dow can engage in “what if” scenario planning, altering assumptions on internal variables such as staff promotions or external variables such as political and legal considerations.

35. Agenda
1. Core concepts, terminologies & buzzwords
Business Intelligence, Analytics
Big Data, Cloud, SaaS
2. Analytics
Stages, Types, Domains, Tools…
3. HR Analytics
Why? What is measured?
What can be measured? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model

36. Importance of HR Analytics as a function
20% Analysis
80% Data Capture
80% Analysis
20% Data Capture
Go out and measure & analyze the reasons for turnover of my sales people
Build a dashboard that continuously correlates retention with engagement, competency scores and other measures
Measurement as a Project
Measurement as a Process

37. How does Analytics fit in HR delivery model
Shared Services / Process administration
Center of Excellence
HR Business Partnering
HR Head
Zonal HR Head
Location 1 HR
Location 2 HR
Business Unit HR
BU 1 HR
BU 2 HR
Compensation & Benefits
Recruitment & Selection
Learning & Development
HR Analytics
Shared Services

38. Common mistakes to avoid
1.Keeping a metric live even when it has no clear business reason for being
2.Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
3.Insisting on 100% accurate data before an analysis is accepted— which amounts to never making a decision
4.Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success
5.Using analytics to hire lower-level people but not when assessing senior management
6.Analyzing HR efficiency metrics only, while failing to address the impact of talent management on business performance

39. Key to success in HR Analytics
1.Transparency of business and workforce information
2.Analytics as a journey, not an end
3.Develop culture of data-driven decision-making
4.Empower line leaders, not just HR and L&D
Build an HR Data Warehouse
Deliver Actionable Business Information

40. Thanks! Q&A time…
For one last time 
LinkedIn: in.linkedin.com/in/akshayraje/ Twitter: @akshayraje Email: akshay.raje@gmail.com