hr-predictive-analytics

Predictive people analytics

Build, validate, and responsibly interpret predictive models for HR outcomes — attrition risk, performance trajectory, and hiring success — so people decisions can be informed by evidence rather than intuition alone.

Supported tasks

• Framing an HR question as a predictive modeling problem
• Identifying relevant predictors for attrition, performance, or hiring success models
• Selecting appropriate features from HRIS, engagement, and performance data
• Interpreting model outputs (risk scores, feature importance) for HR audiences
• Validating predictive models for accuracy and stability over time
• Assessing predictive models for bias and disparate impact before use
• Translating model outputs into actionable manager and HR interventions
• Designing attrition risk flagging processes that avoid over-reliance on scores
• Communicating predictive analytics findings without overstating certainty
• Governing appropriate use and access of predictive model outputs
• Comparing predictive model performance against simpler baseline approaches
• Retiring or retraining models as underlying workforce dynamics shift

Key prompts

Framing and building

1. "Frame [HR question, e.g. 'which employees are likely to leave in the next 6 months'] as a predictive modeling problem — what data and approach would this need?"
2. "What predictors are commonly associated with attrition risk, and which of these could we responsibly use given our available data?"
3. "What features would be relevant for a model predicting hiring success for [role type], and how would we validate them against actual performance outcomes?"
4. "What data quality issues in our HRIS would we need to fix before a predictive model on [outcome] would be trustworthy?"

Interpreting and validating

1. "Explain this model's feature importance output in plain language for an HR business partner audience."
2. "How should we validate whether this attrition prediction model is actually accurate and stable over time, not just fitted to historical data?"
3. "Assess this predictive model for potential bias or disparate impact against protected groups before we put it into use."
4. "How do we explain a false positive or false negative from this model to an employee or manager who questions the result?"