Your HR team has employee records, hiring data, time-off history, and survey feedback. But when leadership asks why turnover is rising in Customer Support, it isn’t simple to find an answer. The information exists, but it’s scattered across different systems.
People analytics turns workforce data you already collect into answers you can act on.
In this guide, I’ll explain what is people analytics, how it works, and how to get started. I’ll also walk you through a practical example, from investigating employee turnover to measuring the results of an HR decision.
People analytics is the practice of collecting and analyzing employee data to answer questions about your workforce. HR teams use it to understand patterns in hiring, retention, performance, and absence. The goal is to base decisions about people on evidence instead of guesswork.
In human resources, the work always starts with a question. Why are new hires leaving? Where does hiring slow down? Which teams rarely take time off?
The data helps you investigate the question and decide what to do next. A report is useful when it helps someone make or review a decision.
As CIPD explains, people analytics uses information about employees to investigate business problems. People analytics in HR can cover the full employee lifecycle, from the first job application to the exit interview. That wide view lets you connect events that separate systems keep apart.
HR analytics, people analytics, and workforce analytics often mean similar things. When people discuss HR analytics vs people analytics, they usually mean a difference in focus.
| Term | Typical focus | Example question |
|---|---|---|
| People analytics | Employee experience, behavior, and outcomes across the employee lifecycle | Why do strong performers leave after two years? |
| HR analytics | HR processes and functions, such as hiring speed or review completion | How long does it take to fill an open role? |
| Workforce analytics | Headcount, cost, skills, and future staffing needs | How many engineers will we need next year? |
Talent analytics is another related term. It usually covers talent acquisition and talent management, such as quality of hire and internal moves. In this guide, I use people analytics as the umbrella term.
People analytics helps HR teams spot problems earlier and make decisions they can explain to leadership.
Without data, HR decisions often rely on memory, anecdotes, or the loudest opinion in the room. That makes it hard to defend budget requests or policy changes.
The main people analytics benefits include:
People analytics also changes the conversation with leadership. You can show workforce trends over time, explain what you plan to change, and report what happened afterward.
The four types of people analytics are descriptive, diagnostic, predictive, and prescriptive. Each one answers a different question about the same problem.
To keep it simple, the table applies all four types to one issue: rising turnover in a single team.
| Type | Question it answers | Turnover example |
|---|---|---|
| Descriptive analytics | What happened? | Turnover in Customer Support rose over the last two quarters. |
| Diagnostic analytics | What might explain it? | Exit feedback suggests that new hires may need clearer expectations. |
| Predictive analytics | What may happen next? | A model estimates how many new hires might leave next quarter. |
| Prescriptive analytics | What options could help? | Different onboarding and manager-support options are compared. |
Descriptive analytics uses historical data and is a practical place to start. Diagnostic analytics looks more closely at possible explanations for a pattern.
Predictive analytics uses statistics or machine learning. Useful forecasts require enough relevant, reliable historical data to test the results. Small teams can often answer their immediate questions with descriptive and diagnostic analysis.
These four types describe kinds of analysis. Maturity levels describe something else: how developed a team’s analytics capabilities are overall. Our guide to the people analytics maturity model covers those levels in detail.
People analytics helps anywhere HR makes repeated decisions about employees, including retention, recruitment, performance, absence, and workforce planning.
The table below covers five common people analytics use cases. It connects each HR question with the data needed, a possible finding, and a decision.
| HR question | Data needed | Possible finding | Decision |
|---|---|---|---|
| Retention: Why is turnover rising in one team? | Exit dates, department, tenure, exit reasons | Departures cluster among first-year employees | Review onboarding and manager support for new hires |
| Recruitment: Where does hiring slow down? | Application dates, stage dates, offer dates | Candidates wait long between first and second interviews | Set a time limit for scheduling second interviews |
| Performance: Are reviews happening on time? | Review deadlines, completion dates, manager | Two managers finish reviews late every cycle | Give those managers process support and earlier reminders |
| Absence: Are absence patterns changing? | Time-off records, department, dates | Unplanned absence rises in one team during peak season | Adjust workload planning for that period |
| Workforce planning: Will we have enough people for next year’s goals? | Headcount, hiring plans, departure history, skills | The current hiring pace will not cover planned growth | Start hiring earlier or plan internal moves |
Each example starts with a question, identifies the data needed, and points to a decision someone can own.
You can add engagement scores from surveys to many of these questions. For example, comparing engagement by team shows where a turnover question deserves a closer look.
Planning questions connect HR information to business plans. Over time, they become the basis for strategic workforce planning.
Some teams also try to measure employee productivity. Treat this area with care. Activity data rarely tells the full story, and close monitoring can damage trust.
People analytics works as a cycle: you ask a question, collect and check the data, analyze it, act on it, and measure the result.
Here is that cycle in one complete example. The company and all numbers are illustrative.
The question. A 300-person software company notices more resignations in Customer Support. The HR manager asks one question: why is turnover in Support higher than in other teams?
The data. She exports hire dates, exit dates, departments, and exit interview notes for the last 12 months. All of it comes from the company’s HR system and exit interview records.
The data check. Before calculating anything, she reviews the records. Two departures have no exit date, and one person is listed in the wrong department. She corrects all three records first.
The first result. With corrected records, she compares turnover in Support and Engineering. Both teams have an average headcount of 100.
| Team | Departures (12 months) | Average headcount | Turnover rate |
|---|---|---|---|
| Customer Support | 20 | 100 | 20% |
| Engineering | 9 | 100 | 9% |
Turnover in Support is more than twice the Engineering rate. If you want the formula and more worked examples, see our guide to employee turnover and attrition.
Digging deeper. Next, she groups the 20 Support departures by tenure. Thirteen of them left within their first year.
Exit interview notes from that group mention unclear expectations and limited training. Shift schedules show no clear pattern. Teams that use consistent exit interview questions get notes that are much easier to compare.
The pattern points to onboarding as a likely factor. A pattern alone cannot prove cause, so the team treats it as a hypothesis to test.
The decision. The HR manager reviews the findings with the Support lead. They agree to test two changes for new hires: a more consistent onboarding process and weekly check-ins with a team lead for three months.
The Support lead owns the changes. HR owns the measurement.
Measuring the result. Before the changes start, they agree on what to track over the next two quarters:
If first-year turnover falls and onboarding improves, the results are encouraging, but they do not prove the changes caused the improvement. HR should compare similar hiring groups and consider other changes during the period. If the numbers do not improve, the team can test another explanation.
The follow-up gives HR a clearer account of what changed and what still needs investigation.
Start with one question, use data you already have, and measure what happens after you act.
You can begin without a dedicated data team or advanced software. Here is how to use people analytics in five practical steps.
Turnover can be a practical first project because many teams already have hire and exit dates. Start with a question you can answer using those records.
Once you answer the first question, repeat the cycle with a new one. Over time, these small projects shape your people analytics strategy. You learn which questions matter, which data you trust, and where to invest next.
HR can lead the analysis, but the company needs clear ownership of data and decision-making.
The exact split depends on the size of your organization. This structure works for many growing companies:
In a small company, one HR generalist may cover the first and last roles. That works as long as every decision has a clear owner.
You need the data that answers your current question, and much of it already sits in your HR systems.
Common sources include:
These sources feed the people analytics metrics HR teams track regularly. Examples include headcount, turnover rate, time to fill, absence rate, and review completion.
For definitions and formulas, see our full list of HR metrics to track. Reliable HR reporting depends on the records behind each number, so keep those records current.
Common challenges include unreliable records, disconnected systems, limited skills, privacy risks, bias, and mistaking correlation for causation. Each one has a practical fix.
Poor data quality. Missing or outdated records can lead to wrong conclusions. Assign an owner to each key field and check the data regularly.
Disconnected systems. Employee information spread across spreadsheets and tools is hard to combine. Start by bringing core employee records into one HR system.
Limited skills. Descriptive questions need spreadsheet skills and a clear question. Bring in an analyst when you move to predictive work.
Privacy and data protection. Employee records contain personal information and may contain sensitive information. Collect only what you need, limit access, and follow the data protection laws that apply to you. Avoid reporting results for groups so small that individuals could be identified.
Tell employees what you collect and why. Transparency makes people analytics easier to accept across the organization.
Bias. Historical records can reflect past bias in hiring or promotion. Models trained on that information can repeat it. Review results by group and keep a person responsible for every decision.
Correlation versus causation. Two things that move together do not prove that one causes the other. Treat findings as hypotheses and test them, as the turnover example shows.
Many small teams can start with a spreadsheet or their existing HR system. Dedicated HR analytics software becomes useful as your questions and data grow.
A spreadsheet can work for an occasional analysis when your data is manageable, and access is controlled. It gets harder when you need to combine sources or repeat the same report every month.
Your current HR software may already cover the basics. Check whether it can filter headcount and turnover by team, export records, and save custom reports.
Dedicated analytics tools make sense when you need to:
Thrivea keeps employee records in one place and includes reports for headcount, turnover, and diversity.
You can start with Core HR and expand your capabilities as your team’s needs grow:
Thrivea also provides role-based permissions to help control access to sensitive employee data.
You can use Core HR Starter for free with up to 30 employees. The Growth plan costs $5 per employee per month, with additional modules available separately.
Explore Thrivea Core HR reporting
People analytics starts with clear questions and using data to answer them.
Start with one question your organization cares about. Check your records, compare the relevant data, make an informed decision, and measure the results.
Each analysis helps you better understand your workforce and make more informed HR decisions.
Thrivea gives you a place to manage employee data and track key workforce metrics. Start for free and build your people analytics practice from there.
Yes. Descriptive and diagnostic analysis need clean records and a clear question. Many HR generalists handle both with a spreadsheet or their HR system.
A metric is a single measure, such as turnover rate. People analytics combines measures to explain why something is happening and what to do next.
Start with enough accurate records to answer a specific question. You can analyze hiring or turnover in a small team, but reliable predictive models need sufficient historical data and careful validation.
Yes. Small companies have fewer records, but each departure or slow hire has a larger impact. Simple analysis of hiring and turnover can guide informed decisions early.
Artificial intelligence and machine learning can analyze large datasets and support workforce forecasting. Any predictions involving employees need validation, privacy safeguards, and human review.
These tools still need clean records and human review. If your team uses AI tools with employee information, set clear AI governance rules.
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