Leveraging data analytics in HR to predict turnover

In today’s competitive talent market, HR can’t afford to simply react to resignations – there is in fact a way to predict and pre-empt them. With data analytics in human resources, organisations can turn absence data into powerful indicators, giving HR leaders foresight into who may be at risk of leaving.

Why does turnover prediction matter?

Voluntary turnover in an organisation is expensive. Estimates suggest that replacing an employee can cost between 50% and 200% of their annual salary, depending on their level of experience and seniority.

Add to that lost company knowledge, the time it takes to find a replacement, onboarding time, and ruptured team dynamics, and the business case for spotting early warning signs becomes clear.

Yet many HR teams remain underserved when it comes to having access to quality analytics. In 2018, for example, only 17% of organisations had accessible HR analytics capabilities, and just 2% had integrated, real-time analytical systems in place.

From absence data to predictive indicators

Absence management tools capture a plethora of information, including patterns of sick leave, frequency, duration trends, and patterns over time.

Systems like edays let you record and analyse more sophisticated data still, such as reasons for absence, and a break down of insights by individual, role, location, or team, as well as looking at the wider business.

With this information, you can look for:

Descriptive analytics – which lets you see who has taken sickness absence, how often, and in which teams

Diagnostic analytics – which lets you ask why people are taking absence, and whether increased absence correlates with stress, workload, seasonal patterns, or departmental pressure

Predictive analytics – which lets you use historical data to forecast future outcomes. For example, that an employee may leave in the next 6-12 months

Prescriptive analytics – which lets you suggest interventions to proactively take steps to address a worrying trend before it becomes a bigger problem. It may be a review of a person’s job role or workload, or a simple 1-to-1 conversation with their line manager to find out if that person is struggling

Using your absence data to its fullest like this can help to give you the complete picture of how your teams are doing, and lets you put processes and interventions in place to prevent more absences, and even change any unsettled or unhappy employees’ circumstances from negative to positive outcomes.

Moving HR from absence to insight

For HR leaders, absence management need no longer be simply a compliance or administrative burden.

Leveraging data analytics in human resources is a tool for strategic foresight. By mining absence and leave metrics, HR teams can transform from reactive problem-solvers to proactive leaders.

The result is lower voluntary turnover, improved retention of valuable talent, and a more confident, data-driven people strategy that puts employee wellbeing at the centre.