Flight Risk Score Calculator

About the Flight Risk Score Calculator

A flight risk score is a composite metric that estimates the likelihood an employee will leave the organization voluntarily within a defined period. By weighting factors such as time since last promotion, compensation relative to market, engagement survey scores, manager relationship quality, tenure inflection points, and external market demand, organizations can identify at-risk employees before they start looking elsewhere.

This Flight Risk Score Calculator provides a simplified but practical model that combines six key risk factors into a weighted score from 0 (lowest risk) to 100 (highest risk). Each factor is rated on a 1–10 scale, and customizable weights let you adjust the model to reflect your organization's specific drivers of voluntary turnover.

Proactive retention starts with identifying who is most likely to leave. Armed with flight risk scores, managers and HR business partners can prioritize stay interviews, compensation adjustments, career development conversations, and other retention interventions for the employees whose departure would be most impactful to the business.

When This Page Helps

Reactive retention—scrambling to counter-offer after an employee has already decided to leave—is expensive and often unsuccessful. Flight risk scoring enables proactive retention by identifying at-risk employees early, allowing targeted interventions that are more effective and less costly than reactive approaches.

How to Use the Inputs

1. Rate each factor on a scale of 1 (low risk) to 10 (high risk).
2. Tenure risk: higher if in the 1–2 year or 5–7 year "itchy" windows.
3. Compensation risk: higher if paid below market rate.
4. Promotion risk: higher if not promoted in 3+ years.
5. Engagement risk: higher if recent survey scores are low.
6. Manager risk: higher if relationship with manager is poor.
7. Market risk: higher if hot job market for their skills.
8. Adjust weights to reflect your organization's specific turnover drivers.
9. Review the composite flight risk score and priority level.

Example Calculation

Tips & Best Practices

• Calibrate your weights using historical data—analyze which factors predicted actual departures in the past.
• scores above 70 warrant immediate intervention (stay interview, comp review, development plan).
• Update scores quarterly as factors change—a promotion or raise can significantly decrease risk.
• Combine quantitative scores with manager judgment for the most accurate assessment.
• High-performer flight risk should be escalated—the cost of losing top talent is disproportionate.
• Use flight risk data to prioritize limited retention budget across the workforce.

The Science of Predicting Turnover

Employee flight risk prediction combines organizational psychology with data analytics. The underlying principle is that employees make departure decisions based on two forces: "push" factors (dissatisfaction with current role, pay, manager, culture) and "pull" factors (attractive alternatives in the market). A comprehensive risk model captures both.

Building a More Sophisticated Model

It gives a manual, judgment-based scoring approach. Organizations with mature HR analytics capabilities can build predictive models using historical data, incorporating variables like resume update frequency, LinkedIn activity, performance trajectory, compensation ratio to market, time since last role change, and commute distance changes.

Acting on Flight Risk Intelligence

The value of flight risk scoring is entirely in the action it drives. Create a structured response protocol: low-risk employees get standard engagement; moderate-risk employees get enhanced check-ins and development conversations; high-risk employees get personalized retention plans with specific commitments from the organization.

Frequently Asked Questions

• What factors predict employee flight risk?

  Research shows the top predictors are: time since last promotion, pay relative to market, engagement/satisfaction scores, manager relationship quality, tenure milestones (year 1–2 and 5–7 are peak risk), and external job market conditions for their skills. The relative importance varies by role and industry.

• How accurate are flight risk models?

  Simple weighted models like this one provide directional guidance—they identify higher-risk individuals but aren't precise predictions. Advanced machine learning models using behavioral data (badge swipes, email patterns, LinkedIn activity) can achieve 70–85% accuracy for 6-month prediction windows.

• What score indicates high risk?

  On a 0–100 scale: 0–30 is low risk, 30–60 is moderate risk, and 60–100 is high risk. However, calibrate these thresholds against your actual turnover data to set meaningful boundaries for your organization.

• How often should I update flight risk scores?

  Quarterly updates capture changes from engagement surveys, compensation reviews, and promotion cycles. However, trigger events (manager change, reorganization, missed promotion, competitor poaching) should prompt immediate re-assessment of affected employees.

• What should I do with high flight risk scores?

  For high-risk employees you want to keep: conduct a stay interview, review compensation against market data, discuss career aspirations and create a development plan, address any manager relationship issues, and consider retention incentives like bonuses or accelerated equity vesting. Taking this into account leads to more reliable planning and reduces the risk of unexpected costs or issues.

• Is it ethical to use flight risk scores?

  Flight risk scoring is standard HR practice when used to improve employee experience and retention. However, scores should not be used to withhold opportunities, pre-emptively terminate, or discriminate. The goal is to invest more in keeping valued employees, not to punish at-risk workers.

• Should I share flight risk scores with employees?

  Generally no. Flight risk scores are internal tools for managers and HR to prioritize retention efforts. Sharing scores could create anxiety, resentment, or gaming behavior. Instead, use the insights to drive conversations about career development, compensation, and engagement.

• How do I weight the factors?

  Start with equal weights, then adjust based on your exit interview data and historical patterns. If compensation is the #1 reason people leave your organization, weight it higher. If management quality dominates, weight that factor higher. Regular calibration improves accuracy over time.