Attrition Prediction & Retention
ML-powered prediction of voluntary turnover risk 60-90 days in advance with 70-80% accuracy, identifying flight risk factors, and triggering proactive retention interventions to reduce voluntary attrition by 20-40%.
Why This Matters
What It Is
ML-powered prediction of voluntary turnover risk 60-90 days in advance with 70-80% accuracy, identifying flight risk factors, and triggering proactive retention interventions to reduce voluntary attrition by 20-40%.
Current State vs Future State Comparison
Current State
(Traditional)1. Employee submits resignation: 2-week notice standard. 2. Manager surprised: had no warning employee was unhappy or job hunting. 3. HR conducts exit interview: asks why employee leaving (too late to retain). 4. HR analyzes attrition data quarterly: identifies trends 3-6 months after the fact. 5. Retention efforts reactive: only after key employees already resigned. 6. No prediction of which employees at risk of leaving.
Characteristics
- • SAP SuccessFactors
- • Oracle HCM
- • Workday
- • Excel
- • Tableau
- • Power BI
- • Qualtrics
- • SPSS
- • Python (pandas, scikit-learn)
Pain Points
- ⚠ Data silos across multiple systems complicate integration.
- ⚠ Heavy reliance on manual processes like Excel increases the risk of errors.
- ⚠ Limited real-time insights hinder proactive decision-making.
- ⚠ Basic models may not capture complex relationships in data.
- ⚠ Many organizations lack the skills or resources for advanced analytics.
- ⚠ Survey fatigue can lead to low response rates and unreliable data.
- ⚠ Actionability gap exists even with insights, due to budget or cultural constraints.
Future State
(Agentic)1. Attrition Prediction & Retention Agent uses ML model to predict flight risk 60-90 days before resignation with 70-80% accuracy. 2. Agent analyzes predictive factors: declining engagement scores, reduced collaboration (email/Slack activity), no recent promotion or raise, manager relationship issues, below-market compensation, LinkedIn profile updates. 3. Agent scores employees 0-100 for flight risk: flags high-risk employees (score >70) for proactive intervention. 4. Agent recommends retention actions by risk factor: compensation adjustment (if below market), promotion/career development (if stalled), manager coaching (if relationship issue), flexible work (if work-life balance concern). 5. Agent triggers stay conversations: manager or HR meets with employee before resignation to address concerns. 6. Agent tracks retention intervention effectiveness: which actions work, refine model over time. 7. Agent calculates retention ROI: cost of retention actions vs cost of replacement.
Characteristics
- • Employee engagement survey responses and trends
- • Performance review ratings and feedback
- • Compensation data and market benchmarks
- • Promotion and raise history
- • Manager relationship indicators (1:1 frequency, 360 feedback)
- • Collaboration activity (email, Slack, meeting attendance)
- • LinkedIn profile updates and activity
- • Historical attrition data (who left, when, why)
Benefits
- ✓ 70-80% attrition prediction accuracy: identify flight risk 60-90 days before resignation
- ✓ 20-40% voluntary turnover reduction through proactive retention interventions
- ✓ Retention intervention success: 60-70% vs 20-30% reactive counter-offers
- ✓ Cost savings: avoid 1.5-2x salary replacement costs (recruiting, training, productivity loss)
- ✓ Prioritized retention: focus on high-value employees vs scattershot approach
- ✓ Manager coaching: equip managers with flight risk alerts and retention playbooks
- ✓ Continuous learning: ML model improves over time, identifies new attrition patterns
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Moderate expected business value
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Attrition Prediction & Retention if:
- You're experiencing: Data silos across multiple systems complicate integration.
- You're experiencing: Heavy reliance on manual processes like Excel increases the risk of errors.
- You're experiencing: Limited real-time insights hinder proactive decision-making.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Churn Prediction & Prevention
Identifies at-risk customers with early warning enabling personalized interventions that significantly reduce churn.
What to Do Next
Related Functions
Metadata
- Function ID
- function-attrition-prediction-retention