This document discusses the basics of HR analytics. It explains that analytics can be divided into three levels: descriptive, predictive, and prescriptive. Descriptive analytics reveal relationships in current and historical data using metrics, dashboards, and reports. Predictive analytics use statistical techniques to predict future probabilities and impacts. Prescriptive analytics provide decision options and show alternative business impacts. The document outlines the typical applications of analytics like assessing turnover patterns and reasons for employee disengagement. It also describes the steps involved in predictive analytics, which include determining key performance indicators, analyzing and reporting data, and interpreting results to enable predictions.

1. HR Analytics
Unit - 3
Prof. Vijay K S Bapuji B-Schools, Davangere

2. Basics of HR Analytics
Prof. Vijay K S Bapuji B-Schools, Davangere

3. Nature of Analytics?
Analytics
Arts Science
Logic Design Statistics Procedure
Evaluation
Strategic
Operational
Futures
Prof. Vijay K S Bapuji B-Schools, Davangere

4. ANALYTICS?
“Analytics is first a mental framework, a logical progression
and second a set of statistical operations”
Prof. Vijay K S Bapuji B-Schools, Davangere

5. HR Analytics / Human Capital Analytics
- Basically communicative device
- It brings together data from disparate sources, such as
Surveys, records, and Operations, to paint a cohesive,
actionable picture of current conditions and likely future.
Prof. Vijay K S Bapuji B-Schools, Davangere

6. Analytics is divided into three levels
- Descriptive
- Predictive
- Prescriptive
Prof. Vijay K S Bapuji B-Schools, Davangere

7. Analytics is divided into three levels
- Descriptive
- They are the traditional HR Metrics and are largely efficiency metrics
(Turnover rate, Time to fill, cost of hire, number hired and trained, etc.)
- The primary focus was on cost reduction and process improvement.
- It reveal and describe the relationships and current and historical data
patterns.
- It includes Dashboards, Scorecards, Data mining for basic patterns and
periodic reports
Prof. Vijay K S Bapuji B-Schools, Davangere

8. Analytics is divided into three levels
- Predictive
- It covers variety of techniques viz. Statistics, modelling, data mining that
use current and historical facts to make predictions about the future.
- Its about the probabilities and potential impact.
- Examples: Models used for increasing the probability of selecting the right
people to hire, train and promote.
Prof. Vijay K S Bapuji B-Schools, Davangere

9. Analytics is divided into three levels
- Prescriptive
- This goes behind the predictions and outline the decision options and
workforce optimization.
- It is used to analyse complex data to predict outcomes, Provide decision
options and show alternative business impacts.
- Example: Models used for understanding how alternative learning
investments impact the bottom line.
Prof. Vijay K S Bapuji B-Schools, Davangere

10. Evolution of HR Analytics
Anecdotes / Reports
Scorecards & Dash Boards
Benchmarks
Correlations
Regression and Causation
Optimization
Descriptive
Diagnostic
Predictive
Prescriptive
Prof. Vijay K S Bapuji B-Schools, Davangere

11. Two Values – HR Analytics
Financial Economic
Structured Unstructured
Descriptive Predictive Prescriptive
Prof. Vijay K S Bapuji B-Schools, Davangere

12. Analytic Value Chain
Strategic Business
Plan
Innovate
Produce, Sell
Service
Customer: Analysed
Needs
Price
Quality
Service
Operations:
Planning targets:
Process Times
Product Quality
Output volume
Talent Management:
Workforce Planning – Hiring
– Deploying-compensating-
developing-engaging-
sustaining
HR Services:
Workforce and succession plan-
hire cost-time to fill – quality pay
ad benefits cost
L&D Spend-engagement program
Operational Outputs:
Unit cost – Cycle time
Quantity: Output/input
Quality: Error rates, shrink,
rework
Customer responses:
Number contacted and
number responding
Conversion Rate and Spend
Satisfaction level
Return Rate
Financial Outcomes:
Income Statement
- Revenue
- Expenses
Balance Sheet
- Assets and Liabilities
Strategy Execution
Prof. Vijay K S Bapuji B-Schools, Davangere

13. Analytic Model
Level 2: Display
Show data by category looking for apparent connections and trends (Not Predictive)
Feature: Dashboards and reports shows possible efficiencies in cost, time and amount
Benefits: Basis for predictive and prescriptive analyses
Level 1: Organize
Collect Data into a database and validate accuracy
Feature: Static data on transactions waiting to be applied
Benefits: Solves the basic problem of analytics; data availability
Level 5: Evaluate
Apply statistical or other methodology to validate the predictive model’s validity and utility
Feature: Records economic and financial values obtained
Benefits: Shows top line and bottom line changes that increase all shareholders’ value
Level 4: Model
Design predictive experiment to connect people, Policies, processes and performance
Feature: Describe expected pattern of relationships to uncover correlations and causation
Benefits: Testable hypothesis for understanding complex interactions & interdependencies
Level 3: Relate
Look for impactful external and internal forces affecting the organization
Feature: Shows effects of interacting among human, structural, and relational data
Benefits: Points to opportunities for simple performance improvements
V
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Descriptive
Predictive
Prescriptive
Prof. Vijay K S Bapuji B-Schools, Davangere

14. Typical Application
• Turnover or Attrition – Very common – But few attempts on to
connect turnover or retention changes with business outcomes.
• Assess the patterns within the job groups
• Assessing the disengagement – When the four fundamental human
needs have not met –
• TRUST
• HOPE
• SENSE OF WORTH and
• FEELING COMPETENT.
(Leigh Branham on retention research)
Prof. Vijay K S Bapuji B-Schools, Davangere

15. Typical Application
• Preventable Reasons Why Employee Disengage
• Job and workplace was not what was expected
• Mismatch between job and person
• Too tittle coaching or feedback
• Not feeling valued or recognised
• Stress from overwork and work/life balance
• Loss of trust and confidence in senior leaders
• Exit interviews can give lots of insight on Tenure, Reason and Position
• The organizations are highly complex, So one should
• Truly know what is happening (descriptive analytics)
• Why it is happening and where it is likely to lead (Predictive analytics)
• What to do about it (Prescriptive Analytics)
Prof. Vijay K S Bapuji B-Schools, Davangere

16. Predictive Analytics
Link different data sets and apply predictive analytics to create information for stakeholders
Prof. Vijay K S Bapuji B-Schools, Davangere

17. Steps involved in Predictive Analytics
• Determine key performance indicator,
• Analyse and report data,
• Interpreting the results and
• Predicting the future
Prof. Vijay K S Bapuji B-Schools, Davangere

18. Steps involved in Predictive Analytics
1. Determine key performance indicator (KPIs)
• Through conversation with the VP, segment the data into three types of
measures
• Efficiency
• Effectiveness
• Outcomes
• Then gathering of data
• Communication to gather data
• Formatting the data for analysis
- In which department does the data reside?
- Who is the gate keeper?
- Is it sensitive information?
- What approvals?
- Type of data
- Format of data
- Standard process for requesting data
- Standard turnaround time for the request
- Procedure / Request rising
- Approvals
- About Confidentiality
- Separated by rows and column
- Vertical style
- Cross tab structure is required
- Where each column is a variable
Prof. Vijay K S Bapuji B-Schools, Davangere

19. Steps involved in Predictive Analytics
2. Analyse and report data
- Descriptive Statistics
- Mean
- Standard Deviation
- Frequency distribution
- This analysis is to understand the data
- Inferential Statistics
- Relationship amongst the variables
- Correlation and Regression
- T-Test
- Analysis of Variance
Predictive Analytics is an extension of inferential statistics
Prof. Vijay K S Bapuji B-Schools, Davangere

20. Ask some questions?
April May June
A 15 18 12
B 10 15 13
C 5 5 7
0
2
4
6
8
10
12
14
16
18
20
Open positions by Business Unit in Quarter
A B CProf. Vijay K S Bapuji B-Schools, Davangere

21. Ask some questions?
April May June
A 40 43 48
B 18 22 23
C 12 16 15
40 43
48
18
22 23
12
16 15
0
10
20
30
40
50
60
Average Number of Days to fill a position
A B C
Prof. Vijay K S Bapuji B-Schools, Davangere

22. Thank you
Prof. Vijay K S Bapuji B-Schools, Davangere