Predict which employees are at risk of leaving before they resign, understand why they leave, and deploy targeted retention strategies powered by machine learning and people analytics.
Replacing an employee costs 50-200% of annual salary including recruiting, onboarding, training, and productivity loss. For a €60K employee, total replacement cost is €60K-€120K.
When experienced employees leave, they take critical knowledge, client relationships, and team expertise. Knowledge transfer takes 6-12 months, creating productivity gaps and project delays.
High turnover creates uncertainty and reduces engagement. Remaining employees take on extra work, question company stability, and consider leaving themselves - creating turnover cascades.
Without predictive analytics, retention efforts happen after resignation notice - too late. Exit interviews reveal problems but don't prevent departures. Proactive intervention requires early warning.
Our machine learning platform analyzes employee data, engagement patterns, and historical turnover to predict flight risk months before resignation, enabling proactive retention interventions.
ML models analyze hundreds of employee signals - performance reviews, compensation vs. market, tenure, promotion velocity, manager quality, commute distance, team turnover - to calculate resignation probability for each employee. Models update monthly, providing risk scores and expected time-to-departure predictions.
Beyond predicting who will leave, our platform explains why - which factors drive turnover risk for each employee. SHAP values and feature importance identify whether resignation risk stems from compensation, manager relationship, career growth, work-life balance, or team dynamics. Enables targeted interventions addressing actual concerns.
High-risk employees trigger personalized retention workflows: compensation-driven risk → market adjustment recommendations; growth-driven risk → development planning; manager-driven risk → skip-level 1:1s; burnout risk → workload rebalancing. Retention playbooks customized by employee segment, seniority, and turnover driver.
Ready to reduce regrettable turnover and retain top talent? Our platform predicts turnover with 82%+ accuracy and identifies retention interventions that work.
Real-time dashboards for HR and managers showing: team-level flight risk scores, turnover trends by department, retention intervention effectiveness, compensation competitiveness analysis, manager quality benchmarks. Enables data-driven retention decisions and budget allocation.
Models retrain monthly on latest turnover outcomes. Track which retention interventions worked vs. failed, learning what saves employees. A/B testing of retention strategies to optimize effectiveness. Feedback loop ensures predictions improve over time as more data accumulates.
Employee turnover costs include direct costs (recruiting, interviewing, onboarding, training) and indirect costs (productivity loss during vacancy, new hire ramp time, knowledge loss, remaining employee overtime). Total replacement cost estimates: Entry-level: 50-75% of salary, Mid-level: 100-150% of salary, Senior/specialized: 200%+ of salary. For technical roles, costs skew higher due to knowledge complexity and market competition.
Calculate your turnover cost: Company with 500 employees, 15% annual turnover (75 departures), €65K average salary. Conservative estimate at 100% replacement cost = 75 × €65K = €4.9M annual turnover cost. Even reducing turnover by 3 percentage points (15% to 12%, or 20% improvement) saves €975K annually. This is why retention prediction generates massive ROI - small turnover improvements create big savings.
Most common approach: predict whether employee will leave in next 6/12/24 months (yes/no). Train on historical HR data with features from HRIS, performance reviews, compensation. Algorithms: Random Forest (interpretable, handles missing data), XGBoost (highest accuracy, robust feature importance), Logistic Regression (simple baseline, good explainability). Output: resignation probability 0-100% for each employee. Typical accuracy: 75-85% AUC-ROC with quality data.
Predict not just if employee will leave but when. Cox proportional hazards, Kaplan-Meier estimators, or deep survival models. Advantages: handles right-censored data (current employees haven't left yet), provides time-to-departure estimates for prioritization, models how turnover risk changes over employee tenure. Use case: prioritize retention of critical employee leaving in 3 months over lower-risk employee potentially leaving in 18 months.
Different employee populations have different turnover drivers. Build separate models by: job function (engineering vs. sales vs. operations), seniority (IC vs. manager vs. executive), location (office vs. remote), tenure band (0-1 years, 1-3 years, 3+ years). This improves accuracy and enables targeted retention strategies per segment. Example: compensation dominates turnover for high performers, but work-life balance drives turnover for parents.
Turnover is socially contagious - employees are more likely to leave when teammates depart. Graph neural networks model organizational networks and detect turnover cascade risk. Identify critical employees whose departure would trigger team instability. Enables preemptive retention of network-central individuals and succession planning for single points of failure.
Feature engineering is crucial for turnover prediction. Essential feature categories:
Key metrics for turnover prediction: Precision (what % of predicted leavers actually leave - avoid false alarms), Recall (what % of actual leavers were predicted - don't miss critical departures), AUC-ROC (overall model discrimination - good models above 0.75), Early detection rate (% of turnover predicted 6+ months in advance). Business metrics: turnover reduction %, regrettable turnover rate, retention intervention success rate, cost savings from avoided turnover.
A 1,200-person consulting firm was experiencing 21% annual voluntary turnover (252 departures), costing an estimated €4.8M annually in replacement costs. High turnover created project continuity issues, client relationship disruption, and constant recruiting burden. Exit interviews revealed diverse turnover drivers - compensation, work-life balance, career growth, manager quality - but insights came too late to prevent resignations.
We built a turnover prediction platform integrating their HRIS (Workday), performance review data, compensation benchmarks (Radford), engagement surveys (Culture Amp), and project staffing system. An XGBoost model predicts 12-month turnover probability for each employee, updated quarterly. SHAP explanations identify specific turnover drivers per person. High-risk employees (over 55% turnover probability) trigger retention workflows: compensation risk → market adjustment recommendations for managers; growth risk → development planning and promotion discussions; manager risk → skip-level 1:1s with senior leaders; burnout risk → staffing rebalancing and time-off planning.
Results after 18 months: Annual voluntary turnover reduced from 21% to 13% (38% improvement), saving 100 departures annually. At €95K average replacement cost, this represents €9.5M avoided cost vs. €6.3M actual retention spend = €3.2M net savings. Model achieved 82% AUC-ROC with 68% precision and 71% recall. Regrettable turnover (top performers) reduced 47% as targeted retention focused on critical talent. Manager retention conversations increased 4x as flight risk data enabled proactive outreach. Employee engagement scores improved 14 points as retention efforts addressed systemic issues. The platform also identified three high-turnover managers requiring leadership development, reducing team turnover by 25% in those groups.
Minimum: 2-3 years of employee data with 100+ voluntary departures for training. Ideal: 4+ years with 300+ turnover events across different employee segments. For smaller companies with limited turnover history: (1) Start with engagement surveys and exit interview analysis to identify patterns, (2) Use industry benchmarks and research on turnover drivers, (3) Build simpler models using available data, (4) Implement data collection now for future ML models. Models improve continuously as more turnover data with outcomes accumulates. Even basic people analytics dashboards provide value before full predictive models.
Valid concerns include employee surveillance, punishing predicted flight risk, and privacy violations. Our approach: (1) Use only HR data employees already shared (HRIS, performance reviews, surveys), (2) Aggregate and anonymize data for model training, (3) Provide transparency - employees should know retention analytics exist, (4) Never penalize employees for predicted flight risk - use only for positive retention, (5) Ensure manager access controls - only show manager their own team data, (6) Comply with GDPR and local labor laws on employee data. When implemented ethically, turnover prediction improves employee experience by addressing retention issues proactively.
Retention interventions can backfire if poorly executed - heavy-handed retention conversations signal desperation, counteroffers often delay rather than prevent departure, retention bonuses create resentment among non-recipients. Best practices: (1) A/B test retention tactics to measure effectiveness vs. control group, (2) Train managers on authentic retention conversations focused on employee development, (3) Address systemic issues (compensation equity, manager quality, career paths) not just individual cases, (4) Time interventions early (6-9 months before predicted departure) when relationship-building works better, (5) Track outcomes - which interventions increased retention, which had no effect or negative effect. Effective retention feels like career development, not desperation.
Yes, but with adaptations. Industries like retail, hospitality, or call centers with 40-60% turnover need different approaches: (1) Focus on predicting early turnover (0-6 months) vs. long-term retention, (2) Segment by tenure - different models for new hires vs. tenured employees, (3) Identify characteristics of "stayers" to improve hiring for retention, (4) Use prediction to optimize retention investment - which employees are worth retaining vs. natural attrition, (5) Focus on reducing regrettable turnover (high performers, critical roles) vs. overall turnover. Even small regrettable turnover reductions create significant value in high-churn environments.
Timeline: Data integration and cleaning (3-4 weeks), model development and validation (4-6 weeks), manager training and rollout (2-3 weeks), total 10-13 weeks to first predictions. Results timeline: First retention interventions start immediately, measurable turnover reduction within 6-9 months (turnover is lagging indicator), full ROI typically within 12-18 months. Quick wins: Identify high-turnover managers and systemic issues in first month, enabling immediate organizational improvements. We typically pilot with one department or high-risk segment, validate effectiveness, then expand company-wide. Most clients see positive ROI within first year as avoided turnover costs exceed platform investment.
Predict employee turnover, understand why people leave, and deploy data-driven retention strategies. Our team will assess your turnover data and design a custom retention solution.
Based in Lund, Sweden • Serving businesses globally