HR Analytics Examples to Enhance People Insights

Illustration of a person using a magnifying glass to examine analytics dashboards, charts, and reports, symbolizing different types of HR analytics insights.

Types of HR Analytics

When conducting an HR data analysis, beginning with the specific problem you want to address is essential. For example, is your team having trouble retaining top talent in a particular department or geographic area? Are you aiming to optimize workforce costs?

You can answer these questions (and more) with HR data analysis, also known as HR analytics. This process involves collecting and analyzing workforce data to make educated decisions that increase employee engagement, productivity, and overall performance.

This post covers four different examples of HR analytics that can help you drive toward your organization’s goals:

1 – Descriptive Analytics

2- Diagnostics Analytics

3 – Predictive Analytics

4 – Prescriptive Analytics

Understanding the difference between these types of HR analytics can help you ask the right questions. If your team wanted to hire more qualified talent, what would be the first step to get there? You’d first need to understand why the talent you’ve hired over the past year wasn’t good enough. What specific skills were lacking? Was there a culture mismatch? Ununcovering these insights is key to building a strategic foundation for planning. Let’s get started.

1. Descriptive Analytics: Understanding What Happened

What it is: Descriptive analytics focuses on summarizing and interpreting historical data to answer the question, “What happened?” This is the foundation of any HR analytics strategy and provides the most accessible way to derive insights from data. By organizing past events and patterns, HR professionals gain a clear picture of workforce trends. 

Crunchr dashboard displaying a bar chart showcasing the top skills hired in the sales department, with categories like presentation, organizing, and verbal communication highlighted
How to ask a descriptive analysis question with AI in Crunchr.

Descriptive Analytics Examples: 

  • Analyzing monthly turnover rates for specific departments. 
  • Tracking hire rates per department over the last year.
  • Monitoring diversity and inclusion statistics. 

Most organizations are still working on getting their descriptive analytics right, moving from basic ad-hoc reporting to systemic, advanced workforce reporting. Organizations may struggle to progress to diagnostic or predictive insights without a solid understanding of the current status quo and historical trends. 

2. Diagnostics Analytics: Understanding Why It Happened

What it is: Diagnostic analytics goes a step further by asking, “Why did it happen?” This question helps you uncover the drivers behind trends or issues identified through descriptive analytics. This type of analysis helps HR teams understand the reasons for workforce changes and to address challenges. 

Diagnostics Analytics Examples: 

  • Analyzing why turnover spiked in a specific department by correlating it with engagement scores or managerial changes. 
  • Identifying reasons for a decline in employee productivity through feedback surveys. 
  • Investigating the potential causes of why the promotion rate in a specific department is much lower than the median rate. 

Knowing what happened is is the first step, then, understanding why it happened opens the door to uncovering strategic insights. This analysis can bridge the gap between simple HR reporting and more strategic HR analytics. 

3. Predictive Analytics: Anticipating What Will Happen

What it is: Predictive analytics uses historical data and statistical models to answer, “What will happen?” By identifying patterns and trends, HR professionals can forecast future events, such as employee turnover or workforce needs, allowing them to be proactive. 

Examples of Predictive Analytics in HR: 

  • Predicting which employees are at risk of leaving based on engagement scores and tenure. 
  • Forecasting future hiring needs based on company growth projections. 
  • Anticipating diverse leadership based on the leadership pipeline and historical promotion rates. 

Predictive analytics empowers HR teams to move from reactive to proactive strategies, which leads to more agile and resilient teams. However, it requires reliable data and analytics. Misinterpreting predictions or relying on flawed data can lead to incorrect assumptions. 

4. Prescriptive Analytics: Recommending What to Do

What it is: Prescriptive analytics recommends specific actions to achieve desired outcomes or prevent negative ones. It answers the question, “What should we do?” by suggesting data-driven strategies and interventions based on predictive insights. 

Prescriptive Analytics Examples: 

  • Suggesting personalized retention strategies for high-risk employees (e.g., offering career development opportunities). 
  • Recommending an optimal hiring plan to avoid talent shortages. 
  • Advising on workload redistribution to prevent burnout. 

Prescriptive analytics typically refers to leveraging AI and advanced algorithms to suggest specific actions automatically. In this context, prescriptive analytics often involves software-driven recommendations that guide users toward optimal decisions based on predictive models and real-time data. While practical examples of prescriptive analytics are still limited, rapid AI development is accelerating its potential for real-world applications. 


These examples of HR analytics are the core principles that will guide you on the next step in your strategic journey. By understanding what happened, why it happened, and what will happen, you can begin to make recommendations on what to do next. It’s a practice that takes time, and it’s possible with the right HR metrics.

Learn more about the top HR metrics today or chat with us to see what’s possible with the right people and analytics solution.

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