People Analytics For Dummies

Mike West was a founding member of the first people analytics teams at Merck, PetSmart, Google, and Children's Health Dallas before starting his own firm, PeopleAnalyst, LLC. He has helped companies large and small design people analytics applications and start their own people analytics teams. Mike brings a unique perspective about how to use data to create winning companies and great places to work.

• Introduction 1About This Book 1Foolish Assumptions 2Icons Used in This Book 3How This Book is Organized 3Part 1: Getting Started with People Analytics 3Part 2: Elevating Your Perspective 4Part 3: Quantifying the Employee Journey 4Part 4: Improving Your Game Plan with Science and Statistics 5Part 5: The Part of Tens 5Beyond the Book 5Where to Go from Here 7Part 1: Getting Started With People Analytics 9Chapter 1: Introducing People Analytics 11Defining People Analytics 12Solving business problems by asking questions 14Using people data in business analysis 19Applying statistics to people management 20Combining people strategy, science, statistics, and systems 21Blazing a New Trail for Executive Influence and Business Impact 22Moving from old HR to new HR 22Using data for continuous improvement 24Accounting for people in business results 24Competing in the New Management Frontier 25Chapter 2: Making the Business Case for People Analytics 27Getting Executives to Buy into People Analytics 29Getting started with the ABCs 29Creating clarity is essential 30Business case dreams are made of problems, needs, goals 30Tailoring to the decision maker 31Peeling the onion 32Identifying people problems 34Taking feelings seriously 35Saving time and money 36Leading the field (analytically) 37People Analytics as a Decision Support Tool 38Formalizing the Business Case 40Presenting the Business Case 41Chapter 3: Contrasting People Analytics Approaches 43Figuring Out What You Are After: Efficiency or Insight 44Efficiency 44Insight 45Having your cake and eating it too 46Deciding on a Method of Planning 47Waterfall project management 47Agile project management 47Choosing a Mode of Operation 50Centralized 51Distributed 52Part 2: Elevating Your Perspective 55Chapter 4: Segmenting for Perspective 57Segmenting Based on Basic Employee Facts 58“Just the facts, ma’am” 58The brave new world of segmentation is psychographic and social 62Visualizing Headcount by Segment 62Analyzing Metrics by Segment 63Understanding Segmentation Hierarchies 65Creating Calculated Segments 68Company tenure 68More calculated segment examples 72Cross-Tabbing for Insight 74Setting up a dataset for cross-tabs 74Getting started with cross-tabs 75Good Advice for Segmenting 78Chapter 5: Finding Useful Insight in Differences 79Defining Strategy 80Focusing on product differentiators 83Identifying key jobs 85Identifying the characteristics of key talent 86Measuring If Your Company is Concentrating Its Resources 87Concentrating spending on key jobs 88Concentrating spending on highest performers 88Finding Differences Worth Creating 93Chapter 6: Estimating Lifetime Value 95Introducing Employee Lifetime Value 96Understanding Why ELV Is Important 97Applying ELV 99Calculating Lifetime Value 101Estimating human capital ROI 102Estimating average annual compensation cost per segment 103Estimating average lifetime tenure per segment 103Calculating the simple ELV per segment by multiplying 104Refining the simple ELV calculation 106Identifying the highest-value-producing employee segments 107Making Better Time-and-Resource Decisions with ELV 108Drawing Some Bottom Lines 109Chapter 7: Activating Value 111Introducing Activated Value 113The Origin and Purpose of Activated Value 114The imitation trap 114The need to streamline your efforts 116Measuring Activation 118The calculation nitty-gritty 121Combining Lifetime Value and Activation with Net Activated Value (NAV) 126Using Activation for Business Impact 128Gaining business buy-in on the people analytics research plan 128Analyzing problems and designing solutions 129Supporting managers 130Supporting organizational change 130Taking Stock 130Part 3: Quantifying the Employee Journey 131Chapter 8: Mapping the Employee Journey 133Standing on the Shoulders of Customer Journey Maps 135Why an Employee Journey Map? 141Creating Your Own Employee Journey Map 143Mapping your map 143Getting data 144Using Surveys to Get a Handle on the Employee Journey 145Pre-Recruiting Market Research Survey 145Pre-Onsite-Interview survey 148Post-Onsite-Interview survey 148Post-Hire Reverse Exit Interview survey 14914-Day On-Board survey 15090-Day On-Board Survey 151Once-Per-Quarter Check-In survey 152Once-Per-Year Check-In survey 153Key Talent Exit Survey 155Making the Employee Journey Map More Useful 157Using the Feedback You Get to IncreaseEmployee Lifetime Value 158Chapter 9: Attraction: Quantifying the Talent Acquisition Phase 159Introducing Talent Acquisition 160Making the case for talent acquisition analytics 161Seeing what can be measured 162Getting Things Moving with Process Metrics 163Answering the volume question 164Answering the efficiency question 172Answering the speed question 177Answering the cost question 182Answering the quality question 184Using critical-incident technique 185Chapter 10: Activation: Identifying the ABCs of a Productive Worker 193Analyzing Antecedents, Behaviors, and Consequences 194Looking at the ABC framework in action 195Extrapolating from observed behavior 196Introducing Models 198Business models 199Scientific models 200Mathematical/statistical models 200Data models 201System models 203Evaluating the Benefits and Limitations of Models 204Using Models Effectively 206Getting Started with General People Models 209Activating employee performance 209Using models to clarify fuzzy ideas about people 215The Culture Congruence model 216Climate 218Engagement 221Chapter 11: Attrition: Analyzing Employee Commitment and Attrition 225Getting Beyond the Common Misconceptions about Attrition 226Measuring Employee Attrition 230Calculating the exit rate 231Calculating the annualized exit rate 233Refining exit rate by type classification 233Calculating exit rate by any exit type 236Segmenting for Insight 236Measuring Retention Rate 238Measuring Commitment 239Commitment Index scoring 240Commitment types 241Calculating intent to stay 241Understanding Why People Leave 243Creating a better exit survey 243Part 4: Improving Your Game Plan with Science and Statistics 249Chapter 12: Measuring Your Fuzzy Ideas with Surveys 251Discovering the Wisdom of Crowds through Surveys 252O, the Things We Can Measure Together 253Surveying the many types of survey measures 254Looking at survey instruments 256Getting Started with Survey Research 257Designing Surveys 258Working with models 259Conceptualizing fuzzy ideas 260Operationalizing concepts into measurements 260Designing indexes (scales) 261Testing validity and reliability 263Managing the Survey Process 266Getting confidential: Third-party confidentiality 266Ensuring a good response rate 267Planning for effective survey communications 270Comparing Survey Data 272Chapter 13: Prioritizing Where to Focus 275Dealing with the Data Firehose 276Introducing a Two-Pronged Approach to Survey Design and Analysis 278Going with KPIs 278Taking the KDA route 278Evaluating Survey Data with Key Driver Analysis (KDA) 279Having a Look at KDA Output 286Outlining Key Driver Analysis 287Learning the Ins and Outs of Correlation 288Visualizing associations 288Quantifying the strength of a relationship 290Computing correlation in Excel 291Interpreting the strength of a correlation 292Making associations between binary variables 293Regressing to conclusions with least squares 296Cautions 299Improving Your Key Driver Analysis Chops 299Chapter 14: Modeling HR Data with Multiple Regression Analysis 303Taking Baby Steps with Linear Regression 304Mastering Multiple Regression Analysis: The Bird’s-Eye View 307Doing a Multiple Regression in Excel 309Interpreting the Summary Output of a Multiple Regression 312Regression statistics 313Multiple R 313R-Square 314Adjusted R-square 314Standard Error 315Analysis of variance (ANOVA) 315Significance F 316Coefficients Table 317Moving from Excel to a Statistics Application 320Doing a Binary Logistic Regression in SPSS 321Chapter 15: Making Better Predictions 331Predicting in the Real World 333Introducing the Key Concepts 334Independent and dependent variables 335Deterministic and probabilistic methods 335Statistics versus data science 337Putting the Key Concepts to Use 337Understanding Your Data Just in Time 339Predicting exits from time series data 340Dealing with exponential (nonlinear) growth 344Checking your work with training and validation periods 345Dealing with short-term trends, seasonality, and noise 347Dealing with long-term trends 350Improving Your Predictions with Multiple Regression 354Looking at the nuts-and-bolts of multiple regression analysis 356Refining your multiple regression analysis strategy 358Interpreting the Variables in the Equation(SPSS Variable Summary Table) 361Applying Learning from Logistic RegressionOutput Summary Back to Individual Data 364Chapter 16: Learning with Experiments 369Introducing Experimental Design 370Analytics for description 371Analytics for insight 371Breaking down theories into hypotheses and experiments 372Paying attention to practical and ethical considerations 374Designing Experiments 375Using independent and dependent variables 375Relying on pre-measurements and post-measurements 376Working with experimental and control groups 377Selecting Random Samples for Experiments 378Introducing probability sampling 379Randomizing samples 380Matching or producing samples that meet the needs of a quota 383Analyzing Data from Experiments 384Graphing sample data with error bars 385Using t-tests to determine statistically significant differences between means 389Performing a t-test in Excel 390Part 5: The Part of Tens 395Chapter 17: Ten Myths of People Analytics 397Myth 1: Slowing Down for People Analytics Will Slow You Down 398Myth 2: Systems Are the First Step 399Myth 3: More Data Is Better 400Myth 4: Data Must Be Perfect 401Myth 5: People Analytics Responsibility Can be Performed by the IT or HRIT Team 402Myth 6: Artificial Intelligence Can Do People Analytics Automatically 403Myth 7: People Analytics Is Just for the Nerds 404Myth 8: There are Permanent HR Insights and HR Solutions 405Myth 9: The More Complex the Analysis, the Better the Analyst 405Myth 10: Financial Measures are the Holy Grail 407Chapter 18: Ten People Analytics Pitfalls 409Pitfall 1: Changing People is Hard 409Pitfall 2: Missing the People Strategy Part of the People Analytics Intersection 411Measuring everything that is easy to measure 412Measuring everything everyone else is measuring 412Pitfall 3: Missing the Statistics Part of the People Analytics intersection 413Pitfall 4: Missing the Science Part of the People Analytics Intersection 413Pitfall 5: Missing the System Part of the People Analytics Intersection 414Pitfall 6: Not Involving Other People in the Right Ways 416Pitfall 7: Underfunding People Analytics 417Pitfall 8: Garbage In, Garbage Out 419Pitfall 9: Skimping on New Data Development 420Pitfall 10: Not Getting Started at All 422Index 423