How to Use Predictive Analytics to Identify and Support At-Risk Employees

Employee turnover or disengagement often appears gradually, with symptoms manifesting long before an employee resigns or underperforms. The challenge for HR professionals is to recognize these signals early enough to intervene effectively. Predictive analytics enables HR teams to identify at-risk behaviors and provide proactive workforce support.

What Predictive Analytics Looks Like in HR

Predictive analytics is a growing industry with applications across numerous fields, including healthcare, finance, marketing and HR. In 2024, it reached a market value of $18.89 billion and is expected to grow by 28.3% annually.

In HR, predictive analytics is the practice of using data, statistical algorithms and machine learning techniques to assess and estimate the likelihood of future outcomes based on historical trends. While it has been an existing practice for decades, the emergence of AI and machine learning has led to advances that make the process more efficient and accurate.

Predictive analytics helps HR teams detect shifts in employee behavior or sentiment that suggest where they may need additional support. Proper implementation can help HR leaders anticipate challenges related to retention and performance before they escalate.

Key Data Sources for Building Your Predictive Model

The most accurate models rely on combining multiple data types and platforms, ensuring a broader understanding of the factors and complexities affecting employee performance and behavior.

Performance and Productivity Metrics

Performance data provides some of the clearest indicators of employee engagement. Metrics like project completion rates or quality scores can be beneficial when tracked over time, whether quarterly or annually.

A sustained decline in performance often signals disengagement or overwhelm. Predictive models can detect these downward trends that may not be immediately visible in regular reviews, especially for high performers who have historically performed well.

Employee Engagement and Sentiment Data

Engagement and sentiment data show how employees experience their work environment, such as leadership, workload, recognition and growth opportunities. HR can collect this information through engagement surveys and qualitative feedback.

Predictive models use changes in engagement scores to estimate retention and the risk of burnout. Teams can also use sentiment analysis of verbal or text-based responses to add nuance by identifying emotional tone or recurring themes.

Absenteeism and Time-Off Records

Attendance and time-off data can reveal behavioral signals that indicate stress or disengagement. Aside from tracking employees’ attendance or filed leaves, predictive analytics can assess how these patterns change over time and interact with other data points.

Increases in unplanned absences or extended periods without taking a vacation can indicate at-risk employees. Still, HR teams should interpret these factors holistically and consider other metrics. For example, minimal time off with declining productivity can signal burnout, while increased absences alongside lower engagement scores may point to disengagement or health strain.

Early and nuanced analysis allows HR leaders to identify employees who may need support before issues escalate.

Providing Targeted Employee Support

Predictive analytics delivers the most value when teams translate its insights into action. Identifying at-risk employees should precede timely and personalized support. With the average cost per hire reaching $4,700, helping struggling workers brings operational and functional advantages to any organization.

Addressing Burnout and Workload Issues

When data indicators point to burnout or excess workload, HR interventions should focus on sustainability. This process may involve redistributing responsibilities or encouraging them to take a break.

The effectiveness of these recommendations depends heavily on the context and framing. Employees may become more receptive when HR positions this type of support as a preventive health measure instead of a punishment or response to failure.

Fostering Career Growth

Models may identify issues among high performers whose productivity remains strong, but whose engagement scores are declining. In these cases, the risk might be tied to stagnation rather than exhaustion.

Targeted development plans and mentorship programs can reengage employees by aligning their personal career aspirations with broader company needs. Gallup research shows that a job’s career growth opportunities is cited as the top reason people give when they change jobs, and that organizations that invest in employee development are twice as likely to retain their workers.

Improving Manager and Team Dynamics

Manager dynamics can significantly impact employee retention. Predictive analytics can reveal patterns, such as a larger number of at-risk employees within specific teams or engagement declines following leadership changes.

If this is the case, effective interventions may include manager coaching, feedback processes or mediated team conversations. Some organizations can also use analytics to identify effective leadership behaviors and implement them across different departments.

Ethical Considerations and Responsible Implementation

Predictive analytics is a valuable tool, but it requires responsible and ethical use to maintain employee trust, especially since it relies on sensitive employee data.

Ethical implementation requires HR teams to focus on these principles:

●     Transparency about the data being collected and how the team uses these insights

●     Ongoing auditing to prevent bias in models and outcomes

●     Implementing data privacy safeguards to protect employee privacy

The main purpose of analytics should be support, not surveillance or punitive decision-making. Organizations that communicate clearly will see more employee engagement and long-term success.

Combining Analytics With Empathy

Predictive analytics allows HR professionals to identify workforce risk more easily, given responsible and ethical use. The strongest retention strategies combine data-driven insights with empathy and conversation.

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Devin Partida is the Editor-in-Chief of ReHack.com, and is especially interested in writing about human resources and BizTech. Devin's work has been featured on Entrepreneur, Forbes and Nasdaq.