Building predictive retention models using Prism Analytics in our R2 2023 environment. Want to identify flight risk employees 6-9 months before potential departure so we can proactively intervene. Key questions around predictive model data requirements - which HCM data points are actually predictive versus just correlated noise. Looking at performance ratings, compensation percentile, promotion velocity, manager tenure, and engagement survey results. Also need guidance on Prism Analytics configuration for model training and scoring. Finally, retention workflow integration is critical - how do we operationalize predictions into actionable retention workflows for managers without creating alert fatigue? What’s been your experience building and deploying retention prediction models?
Model validation is critical before production deployment. We split data into training (70%), validation (15%), and test (15%) sets with temporal separation - training on older data, testing on recent data to simulate real-world prediction scenarios. Track precision and recall separately - high precision minimizes false positives reducing alert fatigue, while high recall ensures you catch actual flight risks. Our production model targets 75% precision at 60% recall, meaning 3 out of 4 flagged employees are genuine risks and we catch 6 out of 10 employees who will actually leave.
Feature selection matters more than model complexity. We found compensation relative to market (not absolute salary) highly predictive, especially when combined with time since last increase. Manager relationship indicators like 1-on-1 frequency and skip-level meeting participation were stronger predictors than we expected. Avoid using protected characteristics directly but watch for proxy variables that could introduce bias.
Don’t overlook temporal features in your predictive model data requirements. We found change patterns more predictive than static values - declining performance trend, decreasing engagement scores over time, reduced learning activity compared to prior year. Also consider peer comparison features like compensation percentile within job family or promotion rate compared to peers hired same year. External data integration through Prism is powerful - we added industry-specific turnover rates and local unemployment data which improved model accuracy by 12%. Ensure your training dataset includes both voluntary terminations and current employees with sufficient tenure to avoid sampling bias.
Prism Analytics configuration requires careful dataset design. We created a unified retention dataset combining Core HCM data, Talent data, and external data sources like market compensation benchmarks. Key is the historical window - we used 36 months of data with monthly snapshots to capture trends rather than point-in-time values. Set up calculated fields for derived metrics like promotion velocity and compensation growth rate. Model refresh cadence is monthly with incremental loads to keep predictions current without full reprocessing.
Operationalizing predictions into workflows is where most implementations stumble. We created risk tiers (high/medium/low) rather than raw probability scores - easier for managers to understand and act on. High-risk employees trigger a business process assigning retention action to their manager with a 14-day due date. Action options include compensation review, development opportunity, role modification, or manager discussion. Tracking completion rates and outcome effectiveness helps refine the intervention strategies. Alert fatigue is real - we limit high-risk flags to top 5% of population to ensure managers take them seriously.