Predicting Employee Turnover Before Resign: Can HR Know Who Might Leave?

Predicting Employee Turnover Before Employees Resign

Introduction

Imagine HR receives a resignation email from a very good employee. The manager is surprised. HR is also surprised. Everyone starts asking, "Why did they leave?" So, HR conducts an exit interview, records the reason, and then moves on. But by this point, it is already too late.

Now imagine if HR could notice the warning signs before the employee resigned. Maybe the employee had not received a promotion for three years. Maybe their engagement score had slowly gone down. Maybe their workload had increased. Maybe they had started taking more leave. Maybe they had stopped attending training. So, instead of waiting for the resignation, HR could have a conversation with the employee and understand what was happening.

This is where predictive HR analytics comes in. It uses past and current employee data to find patterns that may be linked to future turnover. So, the idea is not to magically know who will resign. It is more like looking at the warning signs and asking, "Which employees or groups may be at higher risk of leaving, and what can we do about it?"

The Problem

Employee turnover is a normal part of every organization. People change jobs, move cities, start new careers, retire, or simply find another opportunity. So, not every resignation is a problem.

The bigger problem is unexpected and unwanted turnover, especially when good or difficult-to-replace employees leave.

For example, suppose a company has 1,000 employees and 150 leave every year. HR knows that the turnover rate is 15%. But this number does not explain what is really happening.

Maybe 100 of those employees are new employees who leave within their first year. Maybe most of them are from one department. Maybe employees who have not received a promotion for more than two years are leaving more often. Maybe employees working under one particular manager are leaving at a much higher rate.

So, if HR only looks at the overall turnover percentage, these patterns can easily be missed.

This is why traditional HR can sometimes become reactive. An employee leaves, HR asks why, and then HR tries to fix the problem. Predictive analytics tries to make HR more proactive.

Analysis of the Problem

The main problem is not just that employees are leaving. The problem is that organizations often do not know which patterns are connected to employees leaving.

HR usually has a lot of information. It may have data about employee salary, tenure, promotions, performance, attendance, overtime, job role, department, engagement surveys, training and manager changes. So, there may already be a lot of useful information sitting inside the organization.

But having data and actually using data are two different things.

For example, HR might notice that an employee has not received a promotion in four years. On its own, this may not mean anything. But imagine that HR looks at thousands of past employees and finds that employees with no promotion for several years are leaving much more often. Now this becomes an important pattern.

The same can happen with other factors. So, HR can look at things like tenure, salary changes, promotions, performance, engagement, overtime, absenteeism, manager changes and career movement and see whether these factors are connected with past turnover.

However, there is an important point here. A prediction is not a fact. If a model says that an employee has a high chance of leaving, it does not mean that the employee will definitely resign. So, HR should use the prediction as a signal for a conversation, not as a label attached to the employee.

A Real-Life Case: Experian

A strong real-life example is Experian, the global information services company. Experian faced a high employee attrition problem. In 2016, its global resignation rate was around 4 percentage points above the industry benchmark. The company reported that every 1 percentage point increase in turnover was costing around $3 million. So, this was not just an HR issue. It was also a business issue.

The company wanted to understand the problem better. Instead of only looking at employees after they resigned, Experian used data and predictive modelling to look for patterns linked to employee turnover.

So, the HR team used employee information and its own predictive technology to create what it called predictive workforce analytics. The aim was to give HR a better view of what was happening in the workforce and help the organization make more informed retention decisions.

This is interesting because the question changed.

Instead of:

"Why did this employee leave?"

the organization could start thinking about:

"What patterns are we seeing among employees who leave, and where can we act earlier?"

According to the published case, Experian's global attrition rate had decreased by 4 percentage points by 2019, and the company reported savings of around CAD$14 million over two years.

HR Theories, Frameworks and Concepts Reflected in the Problem

One useful concept here is the Employee Life Cycle. An employee does not simply join a company and then suddenly resign. There are many stages, such as attraction, recruitment, onboarding, development, performance, career growth, engagement and eventually exit. So, predictive analytics can help HR understand what is happening at different points in this journey.

Another useful concept is survival analysis. This sounds complicated, but the basic idea is quite simple. It looks at how long something takes to happen. In HR, it can be used to study how long employees stay before leaving. So, HR could ask whether employees who have been in the same role for three years have a different turnover pattern from employees who have been there for six months.

Another useful framework is CRISP-DM, which is a common data analytics process. It stands for Cross-Industry Standard Process for Data Mining. In simple words, it gives a step-by-step way to solve a data problem. HR first understands the business problem, then understands the data, prepares the data, builds a model, checks whether the model works, and finally uses the results in the real world.

This is useful because HR analytics should not start with, "Let's build an AI model." It should start with, "What HR problem are we actually trying to solve?"

Another important concept is feature importance. In simple words, this means finding out which factors are most useful in predicting an outcome. For example, if a model is predicting turnover, HR may find that overtime, lack of career movement and low engagement are important factors. This does not mean these factors automatically cause resignation. It simply means they are useful signals in the model.

There is also the concept of early warning systems. Just like a warning light in a car tells you that something might be wrong before the car completely stops, HR analytics can give HR an early signal that a workforce problem may be developing.

Solution Using HR Theories, Frameworks and Concepts

The first step would be to clearly define the problem. HR should decide whether it wants to predict overall turnover, voluntary turnover, early turnover, critical talent turnover or turnover in a particular department. So, the question needs to be specific.

The second step would be to collect the right data. HR could combine information from the HRIS, performance systems, engagement surveys, attendance records, compensation systems and learning systems. But HR should only use data that is relevant and collected in a responsible way.

The third step would be to clean the data. So, if one system says an employee has worked for three years and another says five years, HR needs to understand why the information is different. Poor-quality data can lead to poor predictions.

The fourth step would be to build and test a predictive model. HR could use methods such as logistic regression, decision trees, random forest or other machine-learning models. The aim would be to identify patterns connected with employee turnover.

But this is where the human side of HR becomes very important.

Suppose the model says that an employee is at high risk of leaving. HR should not tell the manager:

"The algorithm says this employee is going to resign."

That could create distrust and make the employee feel watched.

Instead, the manager could simply have a normal conversation:

"How are you feeling about your role? Is there anything you would like to change or develop?"

So, the prediction becomes a starting point for a human conversation, not the final decision.

HR could then offer different solutions depending on the actual problem. If the employee wants career growth, HR could explore an internal role or development plan. If workload is the problem, the manager could review the workload. If the employee feels they are not being recognised, the manager could discuss recognition and career opportunities.

This is also why predictive analytics should be connected to retention strategy. The model alone does not retain anyone. The action taken after the prediction is what matters.

Actual Solution Implemented in Real Life

Experian's approach shows this idea in practice. The company used its own data and predictive modelling technology to understand employee attrition and identify patterns that could help HR make better retention decisions. The goal was to move away from relying only on traditional workforce management and toward a more data-driven HR strategy.

The results reported in the case were significant. By 2019, Experian's global attrition rate had fallen by 4 percentage points, and the company reported CAD$14 million in savings over two years. Experian later began offering its predictive workforce analytics solution to other businesses as well.

Another useful real-world example comes from Qualfon, which recently described an Early Warning System designed to identify employees at higher risk of leaving. The system gave supervisors earlier visibility so they could have conversations and take action before a resignation. During the pilot, 71% of high-risk employees who received supervisor engagement were retained. However, Qualfon also reported that overall attrition remained fairly stable, so the result should not be treated as proof that the prediction system alone caused a reduction in turnover.

This shows something very important about predictive HR analytics. Prediction is only one part of the solution. The real value comes when HR uses the information responsibly and then takes the right human action.

Conclusion

Predicting employee turnover sounds a little like HR trying to predict the future. But it is actually more about understanding the warning signs from the past and present.

So, if employees who have low engagement, no career movement and very high workloads have historically left more often, HR can use that information to ask better questions.

But the goal should not be:

"Find employees who are going to leave."

The goal should be:

"Find patterns that tell us where employees may need attention, and t