Every 1-percentage-point attrition reduction equals $5M savings at Nielsen scale. Managers learn about departures via resignation letters; predictive models surface risk early enough to intervene. The trick is intervening without creating self-fulfilling prophecies.
ML models score individual flight risk using features including tenure, compensation percentile, performance, manager span, distance from office, time since promotion, and engagement. High-risk employees route to trained manager interventions: career conversations, role changes, comp adjustments. Models retrain quarterly to handle drift.
ML modeling platform + feature engineering pipeline + bias monitoring framework + manager intervention workflow + privacy and GDPR compliance layer.
A clean, unified employee master data system serving as the single source of truth for every other HR capability.
HRIS provides the employee feature set (tenure, position, org) for model training.
A continuous performance cycle replacing annual reviews with quarterly goals, ongoing feedback, and career development conversations.
Performance data is a primary predictive feature for attrition models.
Standardized compensation bands by job level paired with continuous pay equity monitoring across gender, race, and other protected characteristics.
Compensation percentile is among the strongest attrition predictors.
Nothing downstream yet.
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