Predictive People Analytics That Drive Real Results

Predictive HR analytics is the use of data analysis, statistical models and machine learning algorithms to forecast future outcomes related to human resource management. It leverages historical data sets and workforce analytics to anticipate trends such as employee turnover, skill gaps and future performance. This allows HR leaders to make informed decisions based on patterns in employee behavior, demographics and engagement surveys.

isolved Predictive People Analytics makes this process accessible for HR professionals by combining enterprise-grade analytics with easy-to-use dashboards—no data science expertise required. The platform helps visualize key metrics like turnover risk, tenure and performance scores across departments in real-time. With tools like predictive modeling, benchmark comparisons and configurable visualizations, isolved enables data-driven decision-making across the entire employee lifecycle.

The four types of HR analytics help HR leaders understand workforce trends, make better decisions and improve outcomes across talent management, performance and retention:

1. Descriptive analytics: Summarizes historical HR data, such as turnover rates or performance review scores, to show what has happened.

2. Diagnostic analytics: Explains why certain outcomes occurred by identifying patterns and relationships in data sets.

3. Predictive analytics: Forecasts future outcomes using statistical models and historical trends, such as employee attrition or productivity shifts.

4. Prescriptive analytics: Recommends specific actions based on predictive models to guide HR strategy and improve decision-making.

isolved Predictive People Analytics supports all four types by combining historical reporting, predictive modeling and prescriptive guidance in one centralized dashboard.

Predictive HR analytics forecasts future workforce outcomes using historical data, data analytics and statistical models. It helps HR leaders anticipate trends like employee retention risks, future performance, or skill set gaps. Predictive models are often used to support proactive decision-making and improve talent management by identifying patterns in HR metrics and workforce behavior.

Prescriptive HR analytics takes it a step further by recommending actions based on those forecasts. It uses algorithms and real-time data to guide HR strategy—for example, suggesting interventions to reduce turnover or improve performance reviews. While predictive analytics answers "what might happen," prescriptive analytics answers "what should be done." Both provide valuable insights that help HR teams excel in decision-making, resource allocation and employee well-being planning.

The three types of predictive analytics most commonly used in HR are:

1. Predictive modeling: Uses statistical algorithms to forecast outcomes based on historical data. HR departments apply this to anticipate employee retention risks, future performance or compensation needs.

2. Decision analysis and optimization: Helps evaluate potential actions and select the best course based on projected outcomes. This supports performance management, recruitment strategies and other HR functions where optimization is needed.

3. Forecasting: Focuses on projecting future trends such as hiring needs, turnover rates or workforce costs. It’s often used to support initiatives related to employee engagement, workforce planning and budgeting.

HR predictive analytics, like what’s available in isolved Predictive People Analytics, combines these approaches to give HR teams actionable data analytics and insights to improve business outcomes and guide interventions.