Turn workforce data into focused decisions.

Model Framework & Evidence

This People Intelligence Engine uses a rule-based model to analyze workforce data and identify patterns in performance, attrition risk, and hiring effectiveness.

How the Model Works

• Identifies top performers using a composite index (TPI)
• Estimates attrition risk using multiple signals (ARI)
• Highlights gaps and recommends targeted interventions
• Translates success patterns into hiring strategies

Key Signals & Rationale

Engagement

Lower engagement is associated with higher attrition risk and lower productivity. Research from Gallup shows engaged employees drive stronger business outcomes and significantly lower turnover.

Learning & Development

Learning engagement is associated with higher performance and retention. Development opportunities are a key factor in employee experience and long-term engagement.

Manager Feedback

Manager relationships strongly influence engagement and retention outcomes. Declines in manager engagement are linked to broader engagement drops globally.

Compensation

Below-market compensation is associated with higher likelihood of external job movement, especially in competitive labor markets.

Tenure

Early-tenure employees may show higher variability in engagement and retention depending on onboarding quality and role fit. This model treats tenure as contextual, not causal.

How to Interpret Outputs

• "High Risk" indicates pattern-based signals, not certainty
• Insights are based on relationships observed in the dataset
• Outputs should guide decisions, not replace HR judgment

Limitations

• Dependent on data quality and consistency
• Uses rule-based logic, not predictive machine learning
• Correlation does not imply causation
• Small datasets may produce less reliable insights