People Analytics For Dummies

Introduction  

About This Book  

Foolish Assumptions  

Icons Used in This Book  

How This Book is Organized  

Part 1: Getting Started with People Analytics  

Part 2: Elevating Your Perspective

Part 3: Quantifying the Employee Journey

Part 4: Improving Your Game Plan with Science and Statistics

Part 5: The Part of Tens

Beyond the Book

Where to Go from Here

Part 1: Getting Started With People Analytics

Chapter 1: Introducing People Analytics

Defining People Analytics

Solving business problems by asking questions

Using people data in business analysis

Applying statistics to people management

Combining people strategy, science, statistics, and systems

Blazing a New Trail for Executive Influence and Business Impact

Moving from old HR to new HR

Using data for continuous improvement

Accounting for people in business results

Competing in the New Management Frontier

Chapter 2: Making the Business Case for People Analytics

Getting Executives to Buy into People Analytics

Getting started with the ABCs

Creating clarity is essential

Business case dreams are made of problems, needs, goals

Tailoring to the decision maker

Peeling the onion

Identifying people problems

Taking feelings seriously

Saving time and money

Leading the field (analytically)

People Analytics as a Decision Support Tool

Formalizing the Business Case

Presenting the Business Case

Chapter 3: Contrasting People Analytics Approaches

Figuring Out What You Are After: Efficiency or Insight

Efficiency

Insight

Having your cake and eating it too

Deciding on a Method of Planning

Waterfall project management

Agile project management

Choosing a Mode of Operation

Centralized

Distributed

Part 2: Elevating Your Perspective

Chapter 4: Segmenting for Perspective

Segmenting Based on Basic Employee Facts

"Just the facts, ma'am"

The brave new world of segmentation is psychographic and social

Visualizing Headcount by Segment

Analyzing Metrics by Segment

Understanding Segmentation Hierarchies

Creating Calculated Segments

Company tenure

More calculated segment examples

Cross-Tabbing for Insight

Setting up a dataset for cross-tabs

Getting started with cross-tabs

Good Advice for Segmenting

Chapter 5: Finding Useful Insight in Differences

Defining Strategy

Focusing on product differentiators

Identifying key jobs

Identifying the characteristics of key talent

Measuring If Your Company is Concentrating Its Resources

Concentrating spending on key jobs

Concentrating spending on highest performers

Finding Differences Worth Creating

Chapter 6: Estimating Lifetime Value

Introducing Employee Lifetime Value

Understanding Why ELV Is Important

Applying ELV

Calculating Lifetime Value

Estimating human capital ROI

Estimating average annual compensation cost per segment

Estimating average lifetime tenure per segment

Calculating the simple ELV per segment by multiplying

Refining the simple ELV calculation

Identifying the highest-value-producing employee segments

Making Better Time-and-Resource Decisions with ELV

Drawing Some Bottom Lines

Chapter 7: Activating Value

Introducing Activated Value

The Origin and Purpose of Activated Value

The imitation trap

The need to streamline your efforts

Measuring Activation

The calculation nitty-gritty

Combining Lifetime Value and Activation with Net Activated Value (NAV)

Using Activation for Business Impact

Gaining business buy-in on the people analytics research plan

Analyzing problems and designing solutions

Supporting managers

Supporting organizational change

Taking Stock

Part 3: Quantifying the Employee Journey

Chapter 8: Mapping the Employee Journey

Standing on the Shoulders of Customer Journey Maps

Why an Employee Journey Map?

Creating Your Own Employee Journey Map

Mapping your map

Getting data

Using Surveys to Get a Handle on the Employee Journey

Pre-Recruiting Market Research Survey

Pre-Onsite-Interview survey

Post-Onsite-Interview survey

Post-Hire Reverse Exit Interview survey

14-Day On-Board survey

90-Day On-Board Survey

Once-Per-Quarter Check-In survey

Once-Per-Year Check-In survey

Key Talent Exit Survey

Making the Employee Journey Map More Useful

Using the Feedback You Get to Increase

Employee Lifetime Value

Chapter 9: Attraction: Quantifying the Talent Acquisition Phase

Introducing Talent Acquisition

Making the case for talent acquisition analytics

Seeing what can be measured

Getting Things Moving with Process Metrics

Answering the volume question

Answering the efficiency question

Answering the speed question

Answering the cost question

Answering the quality question

Using critical-incident technique

Chapter 10: Activation: Identifying the ABCs of a Productive Worker

Analyzing Antecedents, Behaviors, and Consequences

Looking at the ABC framework in action

Extrapolating from observed behavior

Introducing Models

Business models

Scientific models

Mathematical/statistical models

Data models

System models

Evaluating the Benefits and Limitations of Models

Using Models Effectively

Getting Started with General People Models

Activating employee performance

Using models to clarify fuzzy ideas about people

The Culture Congruence model

Climate

Engagement

Chapter 11: Attrition: Analyzing Employee Commitment and Attrition

Getting Beyond the Common Misconceptions about Attrition

Measuring Employee Attrition

Calculating the exit rate

Calculating the annualized exit rate

Refining exit rate by type classification

Calculating exit rate by any exit type

Segmenting for Insight

Measuring Retention Rate

Measuring Commitment

Commitment Index scoring

Commitment types

Calculating intent to stay

Understanding Why People Leave

Creating a better exit survey

Part 4: Improving Your Game Plan with Science and Statistics

Chapter 12: Measuring Your Fuzzy Ideas with Surveys

Discovering the Wisdom of Crowds through Surveys

O, the Things We Can Measure Together

Surveying the many types of survey measures

Looking at survey instruments

Getting Started with Survey Research

Designing Surveys

Working with models

Conceptualizing fuzzy ideas

Operationalizing concepts into measurements

Designing indexes (scales)

Testing validity and reliability

Managing the Survey Process

Getting confidential: Third-party confidentiality

Ensuring a good response rate

Planning for effective survey communications

Comparing Survey Data

Chapter 13: Prioritizing Where to Focus

Dealing with the Data Firehose

Introducing a Two-Pronged Approach to Survey Design and Analysis

Going with KPIs

Taking the KDA route

Evaluating Survey Data with Key Driver Analysis (KDA)

Having a Look at KDA Output

Outlining Key Driver Analysis

Learning the Ins and Outs of Correlation

Visualizing associations

Quantifying the strength of a relationship

Computing correlation in Excel

Interpreting the strength of a correlation

Making associations between binary variables

Regressing to conclusions with least squares

Cautions

Improving Your Key Driver Analysis Chops

Chapter 14: Modeling HR Data with Multiple Regression Analysis

Taking Baby Steps with Linear Regression

Mastering Multiple Regression Analysis: The Bird's-Eye View

Doing a Multiple Regression in Excel

Interpreting the Summary Output of a Multiple Regression

Regression statistics

Multiple R

R-Square

Adjusted R-square

Standard Error

Analysis of variance (ANOVA)

Significance F

Coefficients Table

Moving from Excel to a Statistics Application

Doing a Binary Logistic Regression in SPSS

Chapter 15: Making Better Predictions

Predicting in the Real World

Introducing the Key Concepts

Independent and dependent variables

Deterministic and probabilistic methods

Statistics versus data science

Putting the Key Concepts to Use

Understanding Your Data Just in Time

Predicting exits from time series data

Dealing with exponential (nonlinear) growth

Checking your work with training and validation periods

Dealing with short-term trends, seasonality, and noise

Dealing with long-term trends

Improving Your Predictions with Multiple Regression

Looking at the nuts-and-bolts of multiple regression analysis

Refining your multiple regression analysis strategy

Interpreting the Variables in the Equation

(SPSS Variable Summary Table)

Applying Learning from Logistic Regression

Output Summary Back to Individual Data

Chapter 16: Learning with Experiments

Introducing Experimental Design

Analytics for description

Analytics for insight

Breaking down theories into hypotheses and experiments

Paying attention to practical and ethical considerations

Designing Experiments

Using independent and dependent variables

Relying on pre-measurements and post-measurements

Working with experimental and control groups

Selecting Random Samples for Experiments

Introducing probability sampling

Randomizing samples

Matching or producing samples that meet the needs of a quota

Analyzing Data from Experiments

Graphing sample data with error bars

Using t-tests to determine statistically significant differences between means

Performing a t-test in Excel

Part 5: The Part of Tens

Chapter 17: Ten Myths of People Analytics

Myth 1: Slowing Down for People Analytics Will Slow You Down

Myth 2: Systems Are the First Step

Myth 3: More Data Is Better

Myth 4: Data Must Be Perfect

Myth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team

Myth 6: Artificial Intelligence Can Do People Analytics Automatically

Myth 7: People Analytics Is Just for the Nerds

Myth 8: There are Permanent HR Insights and HR Solutions

Myth 9: The More Complex the Analysis, the Better the Analyst

Myth 10: Financial Measures are the Holy Grail

Chapter 18: Ten People Analytics Pitfalls

Pitfall 1: Changing People is Hard

Pitfall 2: Missing the People Strategy Part of the People Analytics Intersection

Measuring everything that is easy to measure

Measuring everything everyone else is measuring

Pitfall 3: Missing the Statistics Part of the People Analytics intersection

Pitfall 4: Missing the Science Part of the People Analytics Intersection

Pitfall 5: Missing the System Part of the People Analytics Intersection

Pitfall 6: Not Involving Other People in the Right Ways

Pitfall 7: Underfunding People Analytics

Pitfall 8: Garbage In, Garbage Out

Pitfall 9: Skimping on New Data Development

Pitfall 10: Not Getting Started at All

Index