People Analytics For Dummies

Introduction 1

About This Book 1

Foolish Assumptions 2

Icons Used in This Book 3

How This Book is Organized 3

Part 1: Getting Started with People Analytics 3

Part 2: Elevating Your Perspective 4

Part 3: Quantifying the Employee Journey 4

Part 4: Improving Your Game Plan with Science and Statistics 5

Part 5: The Part of Tens 5

Beyond the Book 5

Where to Go from Here 7

Part 1: Getting Started With People Analytics 9

Chapter 1: Introducing People Analytics 11

Defining People Analytics 12

Solving business problems by asking questions 14

Using people data in business analysis 19

Applying statistics to people management 20

Combining people strategy, science, statistics, and systems 21

Blazing a New Trail for Executive Influence and Business Impact 22

Moving from old HR to new HR 22

Using data for continuous improvement 24

Accounting for people in business results 24

Competing in the New Management Frontier 25

Chapter 2: Making the Business Case for People Analytics 27

Getting Executives to Buy into People Analytics 29

Getting started with the ABCs 29

Creating clarity is essential 30

Business case dreams are made of problems, needs, goals 30

Tailoring to the decision maker 31

Peeling the onion 32

Identifying people problems 34

Taking feelings seriously 35

Saving time and money 36

Leading the field (analytically) 37

People Analytics as a Decision Support Tool 38

Formalizing the Business Case 40

Presenting the Business Case 41

Chapter 3: Contrasting People Analytics Approaches 43

Figuring Out What You Are After: Efficiency or Insight 44

Efficiency 44

Insight 45

Having your cake and eating it too 46

Deciding on a Method of Planning 47

Waterfall project management 47

Agile project management 47

Choosing a Mode of Operation 50

Centralized 51

Distributed 52

Part 2: Elevating Your Perspective 55

Chapter 4: Segmenting for Perspective 57

Segmenting Based on Basic Employee Facts 58

"Just the facts, ma'am" 58

The brave new world of segmentation is psychographic and social 62

Visualizing Headcount by Segment 62

Analyzing Metrics by Segment 63

Understanding Segmentation Hierarchies 65

Creating Calculated Segments 68

Company tenure 68

More calculated segment examples 72

Cross-Tabbing for Insight 74

Setting up a dataset for cross-tabs 74

Getting started with cross-tabs 75

Good Advice for Segmenting 78

Chapter 5: Finding Useful Insight in Differences 79

Defining Strategy 80

Focusing on product differentiators 83

Identifying key jobs 85

Identifying the characteristics of key talent 86

Measuring If Your Company is Concentrating Its Resources 87

Concentrating spending on key jobs 88

Concentrating spending on highest performers 88

Finding Differences Worth Creating 93

Chapter 6: Estimating Lifetime Value 95

Introducing Employee Lifetime Value 96

Understanding Why ELV Is Important 97

Applying ELV 99

Calculating Lifetime Value 101

Estimating human capital ROI 102

Estimating average annual compensation cost per segment 103

Estimating average lifetime tenure per segment 103

Calculating the simple ELV per segment by multiplying 104

Refining the simple ELV calculation 106

Identifying the highest-value-producing employee segments 107

Making Better Time-and-Resource Decisions with ELV 108

Drawing Some Bottom Lines 109

Chapter 7: Activating Value 111

Introducing Activated Value 113

The Origin and Purpose of Activated Value 114

The imitation trap 114

The need to streamline your efforts 116

Measuring Activation 118

The calculation nitty-gritty 121

Combining Lifetime Value and Activation with Net Activated Value (NAV) 126

Using Activation for Business Impact 128

Gaining business buy-in on the people analytics research plan 128

Analyzing problems and designing solutions 129

Supporting managers 130

Supporting organizational change 130

Taking Stock 130

Part 3: Quantifying the Employee Journey 131

Chapter 8: Mapping the Employee Journey 133

Standing on the Shoulders of Customer Journey Maps 135

Why an Employee Journey Map? 141

Creating Your Own Employee Journey Map 143

Mapping your map 143

Getting data 144

Using Surveys to Get a Handle on the Employee Journey 145

Pre-Recruiting Market Research Survey 145

Pre-Onsite-Interview survey 148

Post-Onsite-Interview survey 148

Post-Hire Reverse Exit Interview survey 149

14-Day On-Board survey 150

90-Day On-Board Survey 151

Once-Per-Quarter Check-In survey 152

Once-Per-Year Check-In survey 153

Key Talent Exit Survey 155

Making the Employee Journey Map More Useful 157

Using the Feedback You Get to Increase

Employee Lifetime Value 158

Chapter 9: Attraction: Quantifying the Talent Acquisition Phase 159

Introducing Talent Acquisition 160

Making the case for talent acquisition analytics 161

Seeing what can be measured 162

Getting Things Moving with Process Metrics 163

Answering the volume question 164

Answering the efficiency question 172

Answering the speed question 177

Answering the cost question 182

Answering the quality question 184

Using critical-incident technique 185

Chapter 10: Activation: Identifying the ABCs of a Productive Worker 193

Analyzing Antecedents, Behaviors, and Consequences 194

Looking at the ABC framework in action 195

Extrapolating from observed behavior 196

Introducing Models 198

Business models 199

Scientific models 200

Mathematical/statistical models 200

Data models 201

System models 203

Evaluating the Benefits and Limitations of Models 204

Using Models Effectively 206

Getting Started with General People Models 209

Activating employee performance 209

Using models to clarify fuzzy ideas about people 215

The Culture Congruence model 216

Climate 218

Engagement 221

Chapter 11: Attrition: Analyzing Employee Commitment and Attrition 225

Getting Beyond the Common Misconceptions about Attrition 226

Measuring Employee Attrition 230

Calculating the exit rate 231

Calculating the annualized exit rate 233

Refining exit rate by type classification 233

Calculating exit rate by any exit type 236

Segmenting for Insight 236

Measuring Retention Rate 238

Measuring Commitment 239

Commitment Index scoring 240

Commitment types 241

Calculating intent to stay 241

Understanding Why People Leave 243

Creating a better exit survey 243

Part 4: Improving Your Game Plan with Science and Statistics 249

Chapter 12: Measuring Your Fuzzy Ideas with Surveys 251

Discovering the Wisdom of Crowds through Surveys 252

O, the Things We Can Measure Together 253

Surveying the many types of survey measures 254

Looking at survey instruments 256

Getting Started with Survey Research 257

Designing Surveys 258

Working with models 259

Conceptualizing fuzzy ideas 260

Operationalizing concepts into measurements 260

Designing indexes (scales) 261

Testing validity and reliability 263

Managing the Survey Process 266

Getting confidential: Third-party confidentiality 266

Ensuring a good response rate 267

Planning for effective survey communications 270

Comparing Survey Data 272

Chapter 13: Prioritizing Where to Focus 275

Dealing with the Data Firehose 276

Introducing a Two-Pronged Approach to Survey Design and Analysis 278

Going with KPIs 278

Taking the KDA route 278

Evaluating Survey Data with Key Driver Analysis (KDA) 279

Having a Look at KDA Output 286

Outlining Key Driver Analysis 287

Learning the Ins and Outs of Correlation 288

Visualizing associations 288

Quantifying the strength of a relationship 290

Computing correlation in Excel 291

Interpreting the strength of a correlation 292

Making associations between binary variables 293

Regressing to conclusions with least squares 296

Cautions 299

Improving Your Key Driver Analysis Chops 299

Chapter 14: Modeling HR Data with Multiple Regression Analysis 303

Taking Baby Steps with Linear Regression 304

Mastering Multiple Regression Analysis: The Bird's-Eye View 307

Doing a Multiple Regression in Excel 309

Interpreting the Summary Output of a Multiple Regression 312

Regression statistics 313

Multiple R 313

R-Square 314

Adjusted R-square 314

Standard Error 315

Analysis of variance (ANOVA) 315

Significance F 316

Coefficients Table 317

Moving from Excel to a Statistics Application 320

Doing a Binary Logistic Regression in SPSS 321

Chapter 15: Making Better Predictions 331

Predicting in the Real World 333

Introducing the Key Concepts 334

Independent and dependent variables 335

Deterministic and probabilistic methods 335

Statistics versus data science 337

Putting the Key Concepts to Use 337

Understanding Your Data Just in Time 339

Predicting exits from time series data 340

Dealing with exponential (nonlinear) growth 344

Checking your work with training and validation periods 345

Dealing with short-term trends, seasonality, and noise 347

Dealing with long-term trends 350

Improving Your Predictions with Multiple Regression 354

Looking at the nuts-and-bolts of multiple regression analysis 356

Refining your multiple regression analysis strategy 358

Interpreting the Variables in the Equation

(SPSS Variable Summary Table) 361

Applying Learning from Logistic Regression

Output Summary Back to Individual Data 364

Chapter 16: Learning with Experiments 369

Introducing Experimental Design 370

Analytics for description 371

Analytics for insight 371

Breaking down theories into hypotheses and experiments 372

Paying attention to practical and ethical considerations 374

Designing Experiments 375

Using independent and dependent variables 375

Relying on pre-measurements and post-measurements 376

Working with experimental and control groups 377

Selecting Random Samples for Experiments 378

Introducing probability sampling 379

Randomizing samples 380

Matching or producing samples that meet the needs of a quota 383

Analyzing Data from Experiments 384

Graphing sample data with error bars 385

Using t-tests to determine statistically significant differences between means 389

Performing a t-test in Excel 390

Part 5: The Part of Tens 395

Chapter 17: Ten Myths of People Analytics 397

Myth 1: Slowing Down for People Analytics Will Slow You Down 398

Myth 2: Systems Are the First Step 399

Myth 3: More Data Is Better 400

Myth 4: Data Must Be Perfect 401

Myth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team 402

Myth 6: Artificial Intelligence Can Do People Analytics Automatically 403

Myth 7: People Analytics Is Just for the Nerds 404

Myth 8: There are Permanent HR Insights and HR Solutions 405

Myth 9: The More Complex the Analysis, the Better the Analyst 405

Myth 10: Financial Measures are the Holy Grail 407

Chapter 18: Ten People Analytics Pitfalls 409

Pitfall 1: Changing People is Hard 409

Pitfall 2: Missing the People Strategy Part of the People Analytics Intersection 411

Measuring everything that is easy to measure 412

Measuring everything everyone else is measuring 412

Pitfall 3: Missing the Statistics Part of the People Analytics intersection 413

Pitfall 4: Missing the Science Part of the People Analytics Intersection 413

Pitfall 5: Missing the System Part of the People Analytics Intersection 414

Pitfall 6: Not Involving Other People in the Right Ways 416

Pitfall 7: Underfunding People Analytics 417

Pitfall 8: Garbage In, Garbage Out 419

Pitfall 9: Skimping on New Data Development 420

Pitfall 10: Not Getting Started at All 422

Index 423