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 Analysing 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 Analysing 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 Analysing 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: Analysing 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 Analysing 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.

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