Employee turnover tells HR what’s wrong with their employee retention strategy, but by then it’s too late for them to intervene. Specific instances of turnover (or broader turnover trends) are each different; a senior leader rarely leaves for the same reason as a string of five junior software developers. Knowing why people leave is crucial, and that isn’t covered by an organization-wide turnover rate.
Instead of just reacting to employee turnover or adopting strategies based on broader assumptions (rather than team- or department-specific trends), HR can use people data to identify emerging retention risks earlier and reduce regrettable turnover. Identifying these risks allows HR to predict potential turnover trends and take specific action with the right teams and departments rather than only labeling individual employees as flight risks.
Key takeaways
Voluntary turnover costs U.S. businesses an estimated $1 trillion a year, according to Gallup. The worst part? That problem can be addressed. Turnover isn’t inevitable, and it’s not completely out of HR’s control. It takes action and the data to know where that action is most needed.
HR traditionally tracks turnover rates and retention rates, but these numbers come after resignations have already happened. While it’s possible to look back over a period of particularly high turnover and find potential triggers, it doesn’t prevent the resignations that have already happened.
Many HR teams try to address turnover reactively, meaning they’ll only respond when employee disengagement and resignations happen. With proactive retention, HR teams identify and track signals that can lead to turnover before it happens, allowing them to target retention initiatives to at-risk employees. This can help prevent turnover, even without anticipating every departure.
Predictive analytics uses a combination of historical and current data, statistical modeling, and machine learning to estimate the likelihood of a potential outcome. In people analytics, prediction allows HR to determine which teams are at risk of seeing increased turnover, predict how their workforce will need to change to meet coming challenges, and identify potential leaders for succession planning.
For turnover, predictive analytics allows HR to look beyond rates that only update after resignations to identify the signs that an employee might leave. It’s important to note that these predictions aren’t guaranteed; they turn messy data into probabilities, allowing HR to focus their efforts.
Employee flight risk is a term used to represent the likelihood that an employee or a group of employees will choose to leave. A number of signals can be used to predict that risk, from a drop in employee engagement to a change of manager and declining goal progress. Predicting employee retention means identifying and tracking these signals over time to investigate the causes behind them.
A number of signals contribute to predicting employee retention.
Employee engagement describes how motivated and dedicated to the company’s mission an employee is. Engagement can fluctuate over an employee’s tenure, but a significant decrease can be a signal of potential resignations. Many organizations rely heavily on an annual employee engagement survey, which doesn’t have the right level of granularity for this. Regular pulse surveys and check-ins, combined with employee engagement analytics, surface shifts much earlier.
Employee feedback can also be an effective retention risk signal. If employees share less feedback or lower-quality feedback, it might be a signal that they’re less dedicated to or satisfied with their work.
Manager effectiveness describes how successfully a manager meets their responsibilities, including coaching their team and making effective decisions. Manager effectiveness can be measured with employee engagement scores, performance outcomes, and feedback quality. With managers accounting for at least 70% of the variance in employee engagement across business units, their effectiveness is an important signal to track. Tracking manager effectiveness can also identify where managers need additional support.
Changes in employee performance can signal a potential resignation. Decreasing performance, irregular goal setting, slow goal progress, and stalled career growth can all signal that an employee is thinking of leaving.
Connecting performance with employee engagement, manager effectiveness, and other signals through performance management software gives HR a more holistic view of the risks leading to potential resignations.
While overall retention rates might not signal resignations before they happen, HR can drill down and turn this rate into more useful trends. By reviewing retention by team, role, tenure, location, and manager, HR can uncover trends that organization-wide averages might hide.
HR should track a combination of leading and lagging indicators. A leading indicator is a metric that’s seen before a specific outcome, while a lagging indicator is seen following that same outcome.
Here are some of the leading indicators of employee retention:
Here are some of the lagging indicators of employee retention:
Both leading and lagging indicators have their roles to play. Leading indicators provide earlier opportunities for action, while lagging indicators confirm long-term patterns. No single metric should be considered clear proof of a retention risk; metrics should be combined to create a better picture of the situation.
With the right metrics, framework, and commitment, HR analytics can be used to predict and address resignations. Here’s how.
HR needs to track multiple metrics and consider signals from multiple sources rather than only tracking a single average turnover rate. Sources for that information include employee engagement surveys, performance reviews, 360° reviews, and HRIS data. Combining these sources gives HR a more complete picture of the risks that lead to resignations.
From a broader turnover rate, HR can drill down to specific teams, managers, roles, and tenure groups to isolate more specific retention trends. By isolating these trends, HR can establish internal benchmarks more precise than broad industry averages, giving more granularity to their analysis. Finding these precise patterns eventually allows HR to tailor their initiatives to specific hotspots rather than applying them too broadly.
Once HR teams identify retention risks, they need to dive deeper into each pattern. Employee feedback is a strong source of information for these deep dives, as HR can ask specific questions about these patterns. Feedback can suggest factors that impact employee retention, like manager relationships, career growth, workload, recognition, role clarity, and employee experience.
Predicting employee retention identifies risk and gives HR teams opportunities to act before retention risks lead to resignations. But that doesn’t happen if HR doesn’t act. By matching specific retention risks to specific actions, HR can build a framework over time that allows them, leaders, and managers to decide which actions to take when dealing with retention risks. Combining this with dedicated tools and HR analytics dashboards gives HR clearer paths to action.
Patterns across signals allow HR to identify retention risks. From there, HR leaders can match action to risk hotspots and prioritize initiatives that have a better chance of actually impacting these risks. Here’s a spread of initiatives HR leaders can launch to act on retention risk:
No matter what action HR leaders take, they need to follow up on these actions, examining how they’ve affected retention risks. From there, they can make adjustments as needed.
Predictive analytics is a powerful tool, but it also requires special care to be used appropriately. When implementing this practice for predicting employee retention, HR teams should remember to consider the following:
When identifying potential flight risks, there’s a tendency for HR to hyper-focus their efforts on individual employees who are likely to leave. This can feel unfair to employees when these efforts aren’t paired with broader retention strategies.
Implementing any new tactic takes time, and in that time there will be mistakes. But here are the most common mistakes HR can avoid:
Dedicated platforms can help HR teams gather data, analyze it, and plan the right next step. That’s where 15Five, dedicated employee engagement software, comes in. It brings employee engagement, performance, retention, and manager insights together in a single platform, serving as the command center for identifying retention risks and acting on them. With 15Five, HR leaders can:
15Five’s HR Outcomes Dashboard is a dedicated tool for tracking the metrics and signals that impact employee retention, allowing HR to find retention patterns, turn them into insights, and turn insights into action.
Employee retention prediction is a practice where HR teams use people analytics to identify signals and track metrics that can predict potential resignations, instead of only trying to identify trends after employees have already left.
HR can predict employee turnover by tracking data across multiple sources (e.g., employee performance, employee engagement, manager effectiveness), looking for patterns and hotspots before resignations happen, investigating trends in employee feedback, and turning insights into action.
HR teams can predict employee retention risks with leading indicators, metrics that usually come before voluntary turnover. These metrics include:
Employee flight risk is a term that describes the likelihood that an employee or a group of employees will soon leave an organization voluntarily. Flight risk is most useful when tracked across teams or segments, where it points HR toward conditions to fix rather than individuals to watch.
Predictive HR analytics allows HR teams to detect and track signals that lead to potential voluntary turnover. This allows them to tailor their initiatives to specific teams most likely to be affected by resignations, match employee retention trends with specific metrics, and potentially prevent resignations before they happen.
Predicting employee retention is about identifying the signals that can lead to increased turnover before it happens. It requires combining multiple data sources to find and cross-reference the right signals, working closely with managers to identify the right course of action, and reviewing results to correct that action as needed. Specific retention metrics like manager effectiveness and employee engagement should be combined with performance data, employee feedback, and more to get a more holistic picture of these trends and where action is required. Data should lead to responsible, targeted action and an ongoing culture of iteration, testing, and review.
Want to see how 15Five can impact employee retention efforts? Book a demo here.