Download - HR / Talent Analytics

HR Analytics - Akshay Raje

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

Core concepts and terminologies

Decision

Analytics = Business Intelligence

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

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

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Buzzwords

#BigData

#Cloud

#SaaS



2. Analytics Stages, Types, Domains, Tools…

3. HR Analytics Why? What is measured? How? Predictive possibilities…

4. Case studies


Stages of Analytics

Reporting What happened?

Analysis & Monitoring Why did it happen? What is

happening now?

Predictive Analytics What can happen?

Complexity

Bu

sin

ess

valu

e

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

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

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

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

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

Tools, Matrices, Software Predictive Analytics

Typical tools / software: • Statistical software

like SAS, R etc

Regression: • Linear Regression • Non Linear Regression Factor Analysis

Cluster Analysis

Over to Dilbert




3. HR Analytics Why? What is measured? What can be measured? Predictive possibilities…

4. Case studies


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

Steps in HR Analytics

Hindsight Insight Foresight

Gather data by Reporting

Make sense of data by Analysis and Monitoring

Develop predictive models

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

What should/could be measured?

HR Matrices

Recruitment

Retention

Performance & Career

Management

Training Comp & Benefits

Workforce

Organization effectiveness

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

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

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

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

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

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

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

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

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

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

Over to Dilbert





4. Case studies


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 .

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.





4. Case studies


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

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

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

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

Thanks! Q&A time…

For one last time

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