In people analytics, we often find ourselves grappling with the challenge of employee turnover. While analyzing who has left your company is helpful, it’s a reactive approach. What if you could predict when employees are likely to leave, giving you a chance to act before it happens? This is where survival analysis comes in—a powerful but straightforward statistical method that helps forecast employee retention. And the good news? You don’t need to be a data scientist to start applying survival analysis in your HR strategy.

What is Survival Analysis?

Survival analysis is a branch of statistics that focuses on the time until an event occurs. In HR, the "event" can be employee exit. Survival analysis helps to estimate not just the likelihood of turnover but also when turnover is most likely to occur, providing deeper insights into employee retention patterns.

Key Metrics in Survival Analysis

Two primary metrics form the foundation of survival analysis in HR:

• Survival Function: This shows the probability that an employee will remain at the company after a given period. For example, you can calculate the likelihood that an employee will stay for 6 months, 1 year, or more.‍
• Hazard Function: The hazard function helps identify critical periods where the risk of turnover peaks, providing a measure of the "risk" of departure at any given time, conditional on the employee having stayed until that time.

Why Use Survival Analysis for Employee Retention?

For analytics-savvy HR professionals, survival analysis offers several compelling advantages:

• Predictive Capacity: Unlike traditional turnover reports, survival analysis allows for the forecasting of potential departures, enabling proactive interventions.
• Identify High-Risk Periods: By pinpointing high-risk periods in an employee's tenure, HR can design targeted retention strategies for specific time windows.‍
• Cohort Comparison: Survival analysis facilitates the comparison of retention rates across various employee segments, such as departments, job roles, or demographic groups. For example, it can compare the retention rates of new hires in different departments or evaluate whether employees who have gone through a specific on-boarding program stay longer than those who didn’t.

Practical Applications in HR Strategy

For people analysts looking to leverage survival analysis, here are some high-impact applications:

• On-boarding Efficacy: Evaluate the long-term impact of on-boarding programs on employee retention. For instance, you might find that employees who complete a comprehensive on-boarding process have a 25% higher probability of remaining with the company after two years.
• Risk Stratification: Identify high-risk employees based on various factors such as engagement scores, performance ratings, and compensation data. This allows for targeted retention efforts where they're most needed.
• Diversity and Inclusion Insights: Analyze retention patterns across different demographic groups to uncover potential disparities and inform targeted D&I initiatives.‍
• Performance-Retention Correlation: Investigate the relationship between performance ratings and retention.

Example

Imagine you’re analyzing a dataset of 1,000 employees at a tech company. Using survival analysis, you discover that:

• Employees in the engineering department have a 40% chance of leaving after their first year.
• Engagement scores show that employees who scored low on engagement are 60% more likely to leave within 18 months.

With these insights, HR can focus on developing engagement programs for at-risk employees and improving the experience for engineers early in their tenure.

Conclusion

Survival analysis offers HR analysts a powerful tool to transform retention strategies from reactive to proactive. By predicting when employees are most likely to leave, HR can implement targeted, data-driven interventions to improve retention and engagement.

For analytics-focused HR professionals, mastering survival analysis opens up new avenues for strategic impact. It provides a robust framework for making informed decisions, backed by statistical evidence. As we continue to evolve our approaches to talent management, survival analysis stands out as an indispensable tool in the modern HR analytics toolkit.

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