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Predictive Analytics for Human Resources
Jac Fitz-enz, John R. Mattox II · 2014
In a sentence
A practical, step-by-step guide for HR professionals to move from descriptive metrics to predictive and prescriptive analytics that connect human capital investments to business outcomes.
Written by the 'father of human capital measurement' Jac Fitz-enz and researcher John Mattox, this book demystifies predictive analytics for human resources, showing how to turn the flood of HR data into actionable business intelligence. It teaches readers that analytics is first a mental framework of logical questioning and second a set of statistical operations, walking through how to start an analytics project, sell it to executives, gather and clean data, and apply correlation, regression, and structural equation modeling. Using a recurring case study of a 'Retain & Grow' talent initiative, the book ties efficiency, effectiveness, and outcome measures together through the lens of an optimization model, ultimately demonstrating how to predict workforce performance, profitability, and retention so HR can finally claim its seat at the table by adding measurable value.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
A path model in which organizational conditions (vision/brand/culture, executive sponsorship) and HR design levers (recruiting efficiency, development, assessment) influence psychological and behavioral states (competency, engagement, performance) which in turn drive outcome metrics (productivity, retention, profitability). Built on the Boudreau & Ramstad optimization framework and the book's three-tier measure structure.
Vision, Brand, and Culture (VBC)contextual condition
The three foundational elements of a company that set strategy and market position, providing the basis on which strategic plans, leadership requirements, and analytics purposes are defined.
Executive Sponsorship and Supportcontextual condition
The presence of a powerful sponsor, champion, or mandate from the C-level or CHRO that allocates resources, removes roadblocks, and legitimizes an analytics initiative as an organizational rather than HR-only project.
Recruiting and Hiring Efficiencydesign lever
HR design lever capturing the speed and cost of filling positions, including time to fill, cost to hire, average salary, open requisitions, and positions filled per month; reflects process efficiency of the talent acquisition function.
Development and Onboarding Investmentdesign lever
HR design lever covering the developmental experiences provided to employees—classroom training, e-learning, coaching, mentoring, on-the-job training, and onboarding programs—aimed at building competencies matched to assessed gaps.
Competency and Assessment Resultspsychological state
A behavioral/effectiveness state reflecting the degree to which an employee possesses the technical and business competencies required for their role, measured by competency assessment scores and identification of high potentials.
Speed to Competencybehavioral pattern
The time required (in days) for a new hire to demonstrate competence in job tasks, a behavioral effectiveness measure that strongly and negatively predicts productivity—faster speed indicates higher future performance.
Employee Engagementpsychological state
A psychological state reflecting the degree to which an employee feels they are thriving, valued, challenged, and contributing to the company's mission; a leading indicator of retention and an outcome in its own right.
Performance Ratingbehavioral pattern
A behavioral effectiveness measure of an employee's job performance, typically rated on a 9-box (1-9) potential/performance scale at 90 days and 365 days; the strongest correlate of actual productivity in the book's case study.
Employee Productivityoutcome metric
An outcome metric capturing the proportion of time an employee spends on billable work or value-producing output; in the case study it is measured as percentage of billable time and directly predicts profitability.
Retention and Turnoveroutcome metric
An outcome metric capturing whether employees stay or depart the organization (voluntarily or involuntarily) within defined windows (90 or 365 days), with associated replacement costs; high turnover among top performers signals systemic issues.
Profitabilityoutcome metric
The ultimate outcome metric representing the financial value generated per employee, computed from productivity and salary; in the case study, productivity is the single statistically significant predictor of profitability.
Front-End Questioning and Problem Framingdesign lever
The logical, structured inquiry process applied before statistical analysis to identify the true problem, reject biases and irrelevancies, and align stakeholders on a clear goal—the foundational and most important step of analytics.
How they connect
- front end questioning → influences recruiting efficiency
- executive sponsorship → moderates recruiting efficiency
- vision brand culture → moderates development investment
- recruiting efficiency → predicts productivity
- development investment → predicts assessment competency
- assessment competency − correlates speed to competency
- speed to competency − predicts productivity
- performance rating → predicts productivity
- employee engagement − predicts retention turnover
- productivity → predicts profitability
- retention turnover − influences profitability
- employee engagement → correlates productivity
A candidate measure
Predictive Analytics for Human Resources — derived measurement candidates
Vision, Brand, and Culture (VBC)
Competitive position scores across price/quality/service/operations/innovation; Degree of VBC consensus among leaders
self-report suitability: medium
Executive Sponsorship and Support
Budget dollars allocated; Initiative origin (top vs HR); Data access approvals granted
self-report suitability: medium
Recruiting and Hiring Efficiency
Time to fill (days); Cost to hire (currency); Positions filled per month; Open requisitions count
self-report suitability: low
Development and Onboarding Investment
Training hours; Course completion rate; Evaluation/satisfaction ratings
self-report suitability: medium
Competency and Assessment Results
Competency assessment score (1-5); Percentage of required competencies held; High potential flag
self-report suitability: medium
Speed to Competency
Days to competence; Competent before/after 90-day threshold
self-report suitability: low
Employee Engagement
Engagement survey top-two-box %; Year-over-year engagement change
self-report suitability: high
Performance Rating
90-day rating (1-9); 365-day rating (1-9)
self-report suitability: low
Employee Productivity
Percentage of billable/chargeable time; Widgets produced per hour
self-report suitability: low
Retention and Turnover
Turnover rate at 90/365 days; Cost of turnover (% of salary)
self-report suitability: none
Profitability
(Productivity % − 50%) × Salary; Profit per FTE
self-report suitability: none
Front-End Questioning and Problem Framing
Relevance/quality of research questions; Degree of stakeholder agreement on goal
self-report suitability: medium
The story
The reader An HR or human capital professional (or aspiring analyst/manager) who wants to add measurable value to their organization and earn credibility with business leaders.
External problem
HR data is fragmented, descriptive, and disconnected from business outcomes, making it impossible to demonstrate value or predict the impact of talent investments.
Internal problem
The reader feels overwhelmed, ignorant, powerless, and intimidated by Big Data, statistics, and the 'numerati' who speak about analytics.
Philosophical problem
It is simply wrong for organizations to keep swatting problems reactively and making talent decisions based on biased, out-of-date speculation when objective analysis is available.
The plan
- Clarify your organization's vision, brand, and culture and identify the true problem through front-end questioning.
- Decide your value path: solve a problem, form an analytics unit, or develop an analytics culture.
- Secure executive sponsorship and sell analytics by framing it in financial outcomes.
- Organize data into efficiency, effectiveness, and outcome measures using a logic/optimization model.
- Gather, clean, and structure data, then apply descriptive and inferential statistics (correlation, regression, SEM).
- Report results via statements and dashboards, then act on insights to optimize performance.
Success
- You connect human capital investments to revenue, profitability, and productivity.
- You predict future outcomes (like new-hire productivity and retention) and intervene early.
- You earn credibility and a 'seat at the table' as a value-adding business partner.
- Your organization makes data-driven decisions and optimizes workforce performance.
At stake
- You waste scarce resources repeatedly swatting the same problems.
- HR remains a record-keeping function whose only actionable data is for cutting staff and budget.
- You lose competitive advantage and market share by reacting instead of predicting.
- Your organization makes costly bad hires, runs ineffective programs, and loses top talent.
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