Laudio’s Turnover Risk Score uses machine learning and predictive analytics to help leaders identify team members who may be at higher risk of leaving their role. By analyzing a range of factors, such as demographics, practice area, tenure, and manager span of control, leaders can gain proactive insight into potential turnover risks and take timely, targeted action.
This score is designed to support leaders’ judgment, not replace it. While data highlights trends and risk signals, leaders’ firsthand knowledge and relationships remain the most important context for decision-making.
Key Benefits:
- Contextual guidance that complements, not replaces, what leaders already know about their teams.
- Prioritization support to help leaders focus engagement where it may have the most impact.
- Continuous learning model that evolves as workforce data and trends change over time to highlight patterns and early signs related to turnover risk.
How the Turnover Risk Score Works
Laudio’s proprietary model continuously analyzes behavioral, demographic, and operational data to predict turnover risk. Trained on data from nearly 500,000 frontline staff across U.S. health systems, the model updates automatically as new data becomes available.
The score combines multiple data types, including demographics, work patterns, and burnout indicators, to produce a comprehensive, up-to-date picture of turnover likelihood. Turnover risk scores are dynamic and automatically update as new workforce data is received.
Example data inputs include:
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Tenure and Experience: Years in the organization, time in role
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Behavioral Changes: Reduced overtime, shift pattern changes
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Demographics: Age, years of service, role type
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Burnout Indicators: Working more than 15 hours within a 24-hour period
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Manager Span of Control: Number of direct reports
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Attendance Trends: Frequency of unscheduled absences
Role- and Department-Specific Insights
Turnover risk is tailored to the context of each role and department.
For example, engagement indicators for nurses differ significantly from those for environmental services or administrative staff. The model accounts for these distinctions, ensuring that risk levels are relevant and meaningful.
FAQs
Q: What if a team member’s risk score feels inaccurate based on my experience?
A: While the Turnover Risk Score is based on a consistent and robust data-driven model, it can’t capture everything that makes each team member unique. Think of it as a helpful data point, not a final answer. Your firsthand understanding of your team is essential and should always guide how you interpret and act on the information..
Q: How should I interpret the Turnover Risk Score in practice?
A: Use the Turnover Risk Score as a guidepost, not a conclusion. Leaders often have valuable insights, such as team members’ personal circumstances, goals, or upcoming changes, that complement the data and create a fuller understanding of each team member's situation.
Q: Is the score calculated differently for hourly and salaried employees?
A: The same predictive framework is used for both groups. For salaried employees, time clock data (such as clock-ins or clock-outs) is excluded, while other behavioral and engagement data remain part of the calculation.
Q: How often are scores updated?
A: Scores automatically refresh as new workforce data is received, ensuring they reflect current trends and team activity.
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