Cogentix, a global organization with 10,000+ employees, faces critical challenges in talent retention, promotion fairness, and compensation equity. This report leverages advanced data analytics and machine learning to identify at-risk segments, predict promotion readiness, and flag compensation inequities, providing a data-driven roadmap for HR leadership.
Analysis of bottom-quartile engagement scores reveals significant demographic variation. Mid-career employees (Age 40-44) exhibit the highest risk for low engagement (27.0%), while gender and location show no statistically significant impact (p > 0.05).
Strategic Takeaway: Retention efforts should shift from broad demographic targeting to role-specific and age-cohort interventions, focusing on manager quality and career progression for the 40-44 age bracket.
Using a leakage-free LightGBM model, we achieved the ability to identify the Top 50 high-potential candidates with a focus on engagement and commitment.
Core Drivers of Promotion:
- Engagement Score: Primary indicator of readiness.
- Investment in Learning: Training hours highly correlate with promotion potential.
- Tenure: Years at company indicate organizational stability.
Performance (Rating ≥ 4) is driven more by investment in skills than by department or role. While location-specific variations are significant (p=0.0093), identifying top performers requires looking at individual growth metrics.
Figure 1: Spearman correlations showing training hours and engagement as positive performance drivers.
Figure 2: Performance remains remarkably consistent across all 10 departments, suggesting a unified organizational standard.
Figure 3: Heatmap identifying the "Sweet Spot" where training hours maximize performance for mid-tenure employees.
Our residual-based compensation model identified High-Performing Underpaid employees. By predicting expected salary based on role, performance, and tenure, we flagged individuals whose actual pay falls below the 20th percentile of expectation.
Action Item: Review flagged employees in the outputs/csv_reports/underpaid_high_performers.csv to preemptively address turnover risk among top talent.
Statistically significant low engagement was detected in 27 manager-led teams. These teams show average performance ratings lower than the company average, indicating a direct link between leadership quality and workforce output.
| Highlighted Metric | Value |
|---|---|
| Managers Flagged | 27 |
| Highest Risk Team Size | 22 |
| Engagement p-value | < 0.001 |
- Salary Adjustments: Address gaps for flagged underpaid high performers.
- Leadership Coaching: Intervene with the 27 flagged managers.
- Promotion Reviews: Evaluate the Top 50 candidates identified by the predictive model.
- Training Expansion: Incentivize high-impact training hours (60+ hours/year).
- Age-Cohort Programs: Launch engagement initiatives for the 40-44 age group.
- Predictive Integration: Incorporate promotion models into standard HR reviews.
- Market Benchmarking: Review external competitiveness for niche roles identified in equity analysis.
- Stack: Python (Pandas/NumPy), LightGBM, Scikit-learn, Plotly, Streamlit.
- Rigors: Chi-Square, Kruskal-Wallis, Spearman Correlation, Wilson Confidence Intervals.
- Project Structure: Organized into
data/,notebooks/,app/, andoutputs/.
Last Updated: February 2026 | Analysis by VRMithun