Different Examples of Predictive Analytics in Human Resources
Over the past decade, HR analytics has experienced tremendous growth. After learning to master indicators, dashboards, and descriptive reports, a growing number of organizations are now turning to predictive analytics to support more informed HR decision-making. But what does prediction actually mean in HR? And what types of analyses can truly help HR teams act more proactively?
In this article, we explore the main types of predictive analytics in HR, with concrete examples for each use case.
Employee Retention and Turnover Analysis
Objective: Predict which employees are at risk of leaving the organization in the coming months or years.
By combining historical data (tenure, performance evaluations, satisfaction, career progression, absenteeism, etc.), algorithms can be trained to identify employees who are likely to leave. This type of analysis allows for proactive action, such as adjusting development plans or implementing retention initiatives.
Example: A company finds that employees with rising absenteeism rates are more likely to leave their roles within six months. It implements an alert system to enable targeted intervention before these departures materialize.
Predictive Recruitment Analysis
Objective: Estimate the likelihood of success or retention of a candidate during the hiring process.
Recruitment data (candidate profile, assessments, interviews, academic background), combined with outcomes of past employees, can help predict which profiles and recruiting sources will integrate best within the organization.
Example: A model reveals that candidates who have held similar roles in small businesses have a higher retention rate than those coming from large corporations.
Workforce Demand Forecasting
Objective: Anticipate future staffing needs based on growth projections, expected departures, or upcoming organizational changes.
This type of analysis combines business forecasts, historical turnover rates, and internal data to plan staffing needs more effectively.
Example: A hospital anticipates a 20% increase in patient volume in a specific unit and adjusts its hiring plans accordingly.
Predicting Future Performance
Objective: Estimate an employee’s future performance based on training, performance, or management data.
This helps identify high-potential talent or employees who may benefit from additional support or development.
Example: An employee showing strong progress in training metrics and receiving positive peer feedback is flagged as a potential future manager.
Predictive Analysis of Workplace Climate and Engagement
Objective: Identify teams or departments where the work climate may be deteriorating, in order to intervene before it affects performance or retention.
By combining survey results (engagement, satisfaction), open comments, absenteeism trends, or internal requests (team changes, conflict reports, etc.), early warning signs of future issues can be detected. These insights help target preventive actions to improve well-being at work.
Example: A sudden spike in sick leave in one team, combined with a drop in engagement scores from the latest survey, triggers an alert. The HR department quickly steps in to assess the situation and support the team before the climate worsens further.
Predictive analytics transforms the HR function into a strategic player — one that can prevent rather than simply react. It relies on data quality, but more importantly, on a data-driven culture and a willingness to base decisions on evidence to improve the employee experience. However, while many organizations aspire to implement predictive analytics, mastering certain prerequisites is essential.
At Kara RH, we strongly believe that HR teams should have access to simple, accessible, and powerful tools to fully harness the power of prediction.
