People Analytics For Dummies

Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications  Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier. 

Table of Contents:
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

About the Author :
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.