Workforce analytics: a practical guide for HR teams
Learn what workforce analytics is, the four levels of analysis it covers, and how employee survey data strengthens the people insights HR teams rely on.
Summary:
- Workforce analytics combines operational HR records with employee survey feedback to help organizations understand not just what happened, but why.
- The practice progresses through four levels moving from tracking past trends to recommending strategic actions.
- Successful implementation requires connecting siloed data, starting with a specific business decision, and regularly listening to employee sentiment.
In today's data-driven business landscape, organizations are increasingly looking beyond basic headcounts to truly understand what drives their people. Workforce analytics bridges the gap between raw HR metrics and strategic decision-making, enabling leadership to optimize talent management, boost retention, and foster a more engaged workforce
What workforce analytics is and what it measures
Workforce analytics is the practice of analyzing employee data to answer questions about how people join, perform, develop, and leave an organization. It turns scattered HR records into evidence that leadership can act on. The discipline covers both what your systems record and what your employees tell you.
In practice, that means combining two kinds of information.
- Operational data comes from the systems that run HR: headcount, tenure, compensation, absence, promotions, and exits.
- Attitudinal data comes from asking people directly, through employee surveys and other listening channels.
The distinction matters more than it sounds. Operational data tells you that six engineers resigned last quarter. Attitudinal data is how you learn why.
Start with an employee engagement survey
Get a methodologist-written question set you can edit, send, and trend over time.
Why workforce analytics fails without employee input
Most workforce analytics programs start and stall in the same place: the HR information system. Those systems are excellent at recording events and silent on causes. You can see every resignation and still have no idea what drove any of them.
This is the gap that keeps HR out of strategic conversations. When leadership asks why attrition is climbing in one function and flat in another, a dashboard built only on system-of-record data can describe the pattern but not explain it.
Explanation is what earns HR a say in the decision. Employee input only helps if it is honest, and HR teams already have doubts. In 2023 SurveyMonkey research among 269 HR professionals, 72% said they are concerned about whether employees provide open and honest feedback about their experiences at work. That is why anonymity affects the quality of your analysis and not just your response rate.
| Question leadership asks | What operational data can show | What employee input adds |
| Why are people leaving this team? | Who left, when, tenure at exit, and the manager they reported to | What made them start looking, and what would have kept them |
| Is our new manager training working? | Completion rates and who attended | Whether teams experience their managers differently since it ran |
| Where should we invest in development? | Role changes, internal moves, and promotion rates | Which skills employees want to build, and where they feel stuck |
| Are our benefits worth what we spend? | Enrollment and utilization figures | Which benefits employees value, and which they overlook |
Neither source is sufficient alone. Operational data without employee input produces confident conclusions about the wrong causes, and survey data without operational context produces sentiment nobody can tie to a business outcome.
The four levels of workforce analytics
Workforce analytics work sorts into four levels, each answering a different kind of question. Most HR teams begin at the first level and progress as their data and confidence improve. The levels build on each other, so skipping ahead usually produces predictions nobody trusts.
1. Descriptive analytics: what already happened
Descriptive analytics reports on the past.
It answers questions like how many people left last quarter, how headcount changed by department, and how engagement scores moved year over year. This is where most HR reporting already lives.
The output is usually a dashboard or a recurring report. Its value is a shared, reliable picture of the facts, which is harder to produce than it sounds when data sits in several systems.
2. Diagnostic analytics: why it happened
Diagnostic analytics looks for causes.
It compares groups, segments results, and tests which factors move alongside an outcome you care about. Cross-tabbing engagement results by department, tenure band, and location is diagnostic work.
This level is where employee feedback becomes indispensable. Causes for people decisions usually live in experience and perception, which no HR system records.
3. Predictive analytics: what is likely to happen next
Predictive analytics uses historical patterns to estimate future outcomes, such as which roles are most at risk of turnover in the next two quarters.
It requires enough clean history to find a real pattern rather than noise. Predictions built on incomplete data tend to be both confident and wrong.
4. Prescriptive analytics: what to do about it
Prescriptive analytics recommends an action and estimates its effect. It answers what to change, for whom, and in what order.
Very few HR teams operate here consistently, and reaching it depends entirely on the quality of the three levels beneath it.
What to track at each level
Metric selection depends on the question you are answering, not on a universal list. Broadly, workforce analytics programs draw on four categories of measure:
- Movement measures covering hiring, internal mobility, promotions, and exits, including employee turnover rate and its counterpart, employee retention.
- Cost and efficiency measures covering compensation, cost per hire, time to fill, and productivity at work.
- Experience measures covering engagement, satisfaction, manager effectiveness, and onboarding sentiment.
- Capability measures covering skills, training completion, and readiness for internal moves.
Choose the smallest set that answers the question in front of you. A tracked metric nobody acts on is overhead, not insight.
Where workforce analytics data comes from
A workforce analytics program is only as good as the inputs feeding it. Most organizations have more sources than they realize, sitting in systems that do not talk to each other. The work is less about finding data and more about connecting it.
That disconnection shows up in behavior. In the same 2023 research, 81% of companies whose employee insights feed a unified view said they ask employees for DEI input, compared with 66% where the data is scattered and siloed. Connected data appears to make organizations more willing to ask their people questions, not less.
Typical sources fall into three groups:
- Systems of record including your HR information system, payroll, applicant tracking, and learning platforms, which supply the operational history.
- Employee listening including engagement surveys, onboarding and exit surveys, pulse checks, and open-text feedback, which supply the reasons behind the numbers.
- Operational systems including scheduling, service, or project tools, which supply context on how work actually gets done.
Employee listening is the source most programs underuse, and the one that changes what the analysis can conclude. Structured questions produce comparable scores you can trend and segment. Open-text responses explain the scores, and text analysis makes that volume of comments workable rather than aspirational.
Getting survey data into the same view as everything else is a practical problem with practical answers. SurveyMonkey offers over 200 prebuilt integrations, and on the Enterprise plan, premium connections to Microsoft Power BI and Tableau plus API access let survey results flow into the reporting environment your analysts already use.
Running feedback as a recurring program rather than a one-off survey is what makes the data trendable.
How to start a workforce analytics program in five steps
Starting small and answering one question well beats building a comprehensive dashboard nobody opens. The sequence below works whether you have an analyst or you are the analyst.
- Pick one decision that is currently made on instinct. Choose something a leader will act on this quarter, such as where to focus retention effort or whether to extend a training program. A real decision gives the work a deadline and an audience.
- Inventory what data you already hold. List the systems that touch employees and note what each one records, how far back it goes, and how reliable it is. Most teams find the constraint is data quality, not data volume.
- Identify what your systems cannot tell you. Write down the parts of the question that no existing record answers. These gaps are what employee listening is for, and naming them keeps your survey focused.
- Collect the missing input directly. Ask employees the specific questions that close the gap, using anonymity settings where candor matters. An anonymous employee feedback tool raises the odds that what you collect is honest.
- Report the answer, not the data. Bring leadership a conclusion, the evidence behind it, and a recommended action. Interpreting results and presenting them are separate skills, covered in this walkthrough of how to analyze employee engagement survey results and this guide to building a survey analysis report.
Repeat the cycle on the next decision. Programs that survive are the ones that produced a useful answer early.
Workforce analytics questions HR teams ask most
- Is workforce analytics the same as HR analytics or people analytics?
- What is the difference between workforce analytics and workforce planning?
- Do you need a data analyst to do workforce analytics?
- How often should you collect employee data for analytics?
Survey templates and tools for workforce analytics
The listening side of a workforce analytics program does not need to be built from scratch. These resources cover the inputs most programs need first:
- Employee engagement survey template for the recurring, trendable measure most programs are built around.
- Text analysis for turning open-ended comments into themes and sentiment you can report.
- Multi-survey analysis for comparing results across surveys and tracking movement over time.
- Employee engagement solutions for running continuous listening across the employee lifecycle rather than one survey at a time, covered on the employee engagement page.
- The employee feedback guide for the wider practice of collecting and acting on what employees tell you, at the ultimate guide to collecting employee feedback.
Premium Power BI and Tableau integrations, API access, and US, Canada, or EU data residency are available on the Enterprise plan.
Start feeding your workforce analytics with SurveyMonkey
Workforce analytics earns HR a seat in strategic decisions only when it can explain causes, not just report events. That explanation comes from asking employees directly and analyzing what they say alongside what your systems already record. The programs that hold up are the ones built on both.
Methodology: SurveyMonkey research was conducted between August 25 to September 5, 2023 among 269 human resource professionals. Respondents were selected from an online non-probability panel.
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