10 HR Analytics Examples and Real-World Use Cases
Data is the building block of a strong HR strategy. Here are five HR analytics examples, with real-world use cases to get you started.
The general discourse around HR analytics is that it’s a capability problem. If orgs just had more data or a larger analytics team, they’d finally be able to make smarter workforce decisions that made an impact on the business.
But the real challenge is usually trust. As Visier's own Chief Customer Officer Paul Rubenstein explains it, when HR goes in front of a business leader to present a trend, and the business leader doesn’t like that trend, or they don’t like the projection, they question the data altogether. And when they don’t believe the data, they don’t believe HR.
Even when the analytics exist, HR’s credibility for leading business outcomes often doesn't.
The companies in this article solved the credibility equation first. What they were able to do from there is what we're here to talk about.
What is HR analytics?
HR analytics is the process of collecting, analyzing, and reporting on employee data. It connects the dots between workforce trends and business outcomes so that orgs can make evidence-based HR decisions across hiring, performance, retention, and longer-term strategic planning.
You’ll also hear it called people analytics and workforce analytics.
For HR analytics to work, the one prerequisite is reliable people data. For instance, time-to-hire tells you how long it takes to fill a role and, by extension, how efficient your recruitment process is, which in turn helps you determine how to reduce the time- to-hire
This means figuring out which hiring stages are creating bottlenecks; which recruiters or job families are outliers; and whether longer time-to-hire in certain roles correlates with better or worse 90-day performance.
All of which requires reliable, connected data across recruiting, performance, and compensation systems. Without which, the entire line of time-to-hire questioning hits an impassable wall.
The 4 types of HR analytics
Four types of HR analytics come together to make your people data trustworthy enough for high-level decision-making:
Predictive analytics
Prescriptive analytics
Diagnostic analytics
Descriptive analytics
Before moving on to the real-world examples of HR analytics, let’s take a quick look at what each of these entails.
Prescriptive analytics
Prescriptive analytics takes the output of predictive modeling and turns it into recommended actions. It’ll tell you, for example, what the most effective tactics for internal mobility and mentoring are.
Predictive analytics
Predictive analytics uses statistical models and historical patterns to forecast what's likely to happen next. Which employees have the highest turnover risk? How long will a role take to fill? Those are the kinds of questions you can answer with predictive analytics.
Diagnostic analytics
Diagnostic analytics goes a level deeper, using that same historical data to understand why something happened, such as why turnover spiked in a particular department or why time-to-hire increased last quarter.
Descriptive analytics
Descriptive analytics looks at historical workforce data to explain what has already happened. This is the foundational data layer most HR teams start with, and examples include headcount changes, turnover rates, and hiring trends.
10 real-world HR analytics examples
Without further ado, here are 10 examples of how real companies use HR analytics to turn their data into decisions leaders can act on:
1. Ascension
During COVID, Ascension leaned hard on large sign-on bonuses and contract labor to keep hospitals staffed with nurses. The strategy made sense in a crisis but years later, the spending habit had outlived the emergency that justified it.
So Arielle Grupe, Ascension's AVP of Workforce Insights and Planning, set out to test whether the bonuses were actually doing their job. Her team pulled ATS data into Visier and broke it down by bonus amount, timing, and job family to see what was driving applications and hires.
It's not just looking at [HR] as a support structure. But it’s looking at us and saying, you are a strategic partner. You're really driving areas that affect our bottom line. You're driving reduction of sign-on bonuses. You're driving reduction of contract labor.
Arielle Grupe, AVP of Workforce Insights and Planning, Ascension
What they found was that sign-on bonuses had surprisingly little impact on applications or hires, and only marginal retention benefits for specific groups. Based on this process, Ascension revamped the bonus policy and, in doing so, cut tens of millions in ineffective spend—without hurting hiring outcomes.
2. Sunstate Equipment
When Sunstate Equipment brought on Sameer Raut as their new VP of HRIS, he realized there was essentially no workplace analytics function in place. Leadership was flying totally blind on employee turnover and overtime trends even as both were climbing.
Raut built an analytics system from scratch at a previous company and didn't want to repeat that lift, so he pitched Visier to HR and Finance as a faster, more capable alternative to BI tools like Power BI or Tableau.
According to Sameer himself, this was critical for the ‘trust’ aspect of getting HR (and more broadly, himself as a leader) taken seriously as a strategic partner, not just a reporting function.
“With Visier, we have the flexibility and confidence to be in front of our executive team and say, ‘Don’t worry, we can get you the analysis you need.’”
From there, Sunstate cut turnover by 50% and overtime by 10%, driven by the connection they uncovered between declining headcount, rising overtime, and falling equipment utilization, which gave leadership a clear, data-backed case for where to implement targeted workforce planning and retention strategies.
3. Gore Mutual
Gore Mutual is a textbook example of using predictive HR analytics to get ahead of a problem before it becomes one. When VP Jason Ramgoolam took over People Operations in 2021, the company was still running its workforce data through spreadsheets.
Rather than jumping straight into a new platform, the team spent phase one simply stabilizing their existing data and reporting, proving its accuracy before asking leadership to invest further.
"Establishing a digital and scalable HR platform is not just an operational necessity but a strategic imperative to support the company's growth ambition."
Jason Ramgoolam, VP of People Operations, Gore Mutual
That groundwork paid off. With Visier’s HR analytics platform fully integrated by 2022, predictive analytics flagged a turnover spike building in a specific team months before it would’ve shown up in a quarterly report.
They also went beyond a typical HR data analysis by using the platform to map full employee journeys and personas. Then, they could reverse-engineer what separates the high performers holding key roles in order to build hiring and org structure decisions around it.
4. Global luxury retailer
A luxury retail group had no efficient way to connect workforce data to sales performance, meaning they were able to see which stores were under- or overperforming, but not why. They needed diagnostic analytics, something which they couldn’t produce on their own.
As one of their senior leaders put it:
"We needed a way to connect workforce decisions with their direct impact on sales and profitability, without spending months manipulating data."
Once they integrated their workforce and sales data in Visier, they found a real correlation between HR metrics like employee engagement and productivity, and store-level sales performance.
That correlation let them act in two specific ways. First, they rebalanced staffing to match actual store traffic patterns. Second, they identified which stores had high attrition that was actively costing them sales, which then helped them build targeted retention/career development programs for those locations.
5. Suntory Global Spirits
Suntory’s more than half-a-decade-long journey with Visier is one of the clearest examples of why adoption—not just implementation—is what truly determines whether HR analytics pays off.
VP Colleen Houlihan picked Visier in 2020 for three specific reasons:
“It's user-friendly and transforms our raw data into bite-sized analyses for our users. And, the fact that Visier can analyze data from multiple data sources."
Consolidating data from their HRIS, ATS, budgeting software, and engagement surveys was ultimately what made Visier’s insights reliable enough to build on. And six years later, that trust has compounded.
What started with one analytics team now facilitates data-driven decision-making across every function within People and Culture. Visier’s cost governance reporting gives Suntory Global Spirits execs clear visibility into headcount, open roles, and compensation costs, and an optimized talent acquisition funnel keeps them ahead of constantly changing staffing needs.
6. Printpack
Printpack was already using Workday, but Workday is an HRIS and system of record first—not a dedicated analytics platform built for flexible, on-demand reporting.
HR Data & Analytics Manager Brandon Fox described being stuck in the platform:
“I could see a 12-month rolling turnover for all locations, but when asked to slice the data further—such as isolating employees with one to two years of tenure—it would take days to recalculate everything.”
After evaluating an in-house build and Workday Prism, Printpack deployed Visier in 2022 to pull compensation, benefits, onboarding, and turnover data into one connected system.
The payoff was measurable: leaders went from days to minutes when comparing turnover across locations, and cross-referencing that newly connected data revealed that first-year employees had by far the highest turnover rate (a finding that let them redirect retention spend at the exact population driving the loss instead of spreading it thin across everyone).
7. eBay
One way global commerce company eBay uses HR analytics and insights is to make data-driven decisions that improve the employee experience.
"Employees in many ways are the most important asset that any company has, and you need data to understand how you can help them stay with your company and help them improve. Analytics is a great way to drive those discussions with data and help make the future more exciting for employees and better for your clients."
Scott Judd, Senior Director of People Analytics & Technology, eBay
By using HR analytics across the employee lifecycle, eBay found new ways to increase retention, like promotions, compensation changes, and career development plans.
8. Providence
Providence has used Visier for HR analytics to improve its hiring process. In a tight labor market, their team was able to use insights to accurately forecast vacancies and proactively hire the right talent to ensure they have the right people in the right roles at the right time—ultimately saving the company $3 million.
“The biggest outcome we’ve seen is how we now forecast vacancy. We used our data from Visier to understand what would happen in the next quarter for some of our key openings. It’s allowed us to give leaders an opportunity to make better choices about whether or not they want to hire ahead.”
Mark Smith, Vice President of Workforce Strategy & Analytics, Providence
By reuniting their people and business data, Providence gained powerful, digestible insights that business leaders continue to use to make informed hiring decisions impacting the workforce and the bottom line.
9. T. Rowe Price
HR analytics has given the T. Rowe Price team a single, reliable data source and access to the Vee chat-based interface for easier data access and fewer inquiries directed to the analytics team. Vee, Visier’s people analytics AI analyst, also helped them automate HR reporting tasks, like headcount and turnover, so they can focus on strategic processes instead of manual work of data collection and consolidation.
Shannon Rutledge, Director, HR Data & Analytics at T.Rowe Price, discussed how they plan on using Visier down the line:
“One key area we're focusing on is measuring the impact of our TA function, which has historically been difficult to analyze. We're excited about the insights that Visier will provide.”
10. Experian
Experian used Visier's HR analytics to reduce reporting workload by 70% and create a single, dependable source for their people data. Now with Visier, the team spends more time on retention risk planning, diversity and inclusion, and cutting costs.
With their arsenal of Visier HR technology (Essentials, Talent, Benchmarks, and Standardized Occupations), Experian management made data more accessible for both HR and finance.
“After the implementation of Visier we—almost overnight—managed to cut about two-thirds of our reporting capacity and capability into the self-service remit, which was massive in terms of the time saved for the team and the effort we were spending on it.”
Kevin Metherell, Head of People Analytics Insights and Innovations, Experian
How to apply HR data analytics for better decision-making
The way you use HR analytics depends on your goals, strategy, and ultimately what your employees want; we’ve got a few steps to help you get started on the right path.
1. Set your goals
Vague ambitions like "improve retention" don't give you anything to measure against. Set Specific, Measurable, Achievable, Relevant, Time-bound (SMART) goals like "reduce turnover in critical roles by 20% within 12 months" instead.
2. Collect accurate and relevant data
Never gather data just for the sake of having data. Choose data types that can help you reach your goals and make sure you’re pulling these from all available sources for a more accurate representation of where you stand.
3. Choose the right data analysis tools
There are a few important things to look for when choosing a people analytics platform, but one of the most important is seamless data integration with the rest of your HR tech stack.
The other critical consideration is ease of use. An important lesson from Ascension's rollout is that adoption hinges on how intuitive the tool feels to everyone using it, not necessarily how powerful it is on paper.
4. Run the data analysis
The world is your oyster here. In Visier, data analysis looks like opening a guidebook or the Explore room, picking a pre-built metric (like turnover, headcount, or time-to-fill), then setting your analysis context—the population and time period you care about.
From there, you can quickly drill into specific cohorts and layers in comparisons until the pattern behind your goal becomes clear.
5. Turn data-driven insights into impact
The main reason you're using HR analytics tools is to improve your decision-making process, which in turn will produce better business outcomes. If you've covered the previous four steps, you should now have easily accessible and interpretable data, but the next step is to interact with and act on it.
General-purpose tools like ChatGPT can answer questions, but only a purpose-built platform established on governed, accurate workforce data will answer them correctly.
Vee, Visier's AI analyst, interprets and reasons from over 2,000 pre-built workforce metrics pulled from your actual data, enforces role-based access on every answer, and works where your leaders already are—inside Slack, Teams, Copilot, and the Visier platform.
It's designed to help leaders find the signal quickly, to pinpoint the biggest risks and opportunities.
Trust is the foundation of HR analytics
Every org on our list of HR analytics examples started in the same place: HR reporting spread across disconnected systems, and leaders who weren't confident enough in the numbers to act on them.
The first move for any organization wanting to ensure the insights surfaced by HR analytics teams is to build a foundation people can actually trust. A centralized source of truth that's tied to business context everyone agrees on.
Everything after that—the turnover prevention, the succession planning, the bonus policies—t is what becomes possible once that trust is established.
See how Visier builds that foundation
Data reliability and ease of access are the two prerequisites for trust in the HR analytics function. Visier accomplishes both.
It pulls together your HR, ATS, payroll, and performance data into a single governed model, then makes it easy to get instant, scoped answers to your most pressing HR analytics questions.
HR analytics FAQs
What are the core HR analytics metrics you need to track?
Start with time to fill, time to productivity, turnover rate, promotion and internal mobility rates, and diversity/inclusion metrics. These five cover hiring and onboarding efficiency, retention, career progression, and workforce composition, which are the areas most companies need visibility into first.
Beyond that, offer acceptance rate, cost per hire, and engagement scores round things out for companies looking to go deeper on recruitment analytics and workforce health.
What is the difference between HRIS and HR analytics?
An HRIS (Human Resource Information System) stores employees’ payroll and benefits info, plus their personal details. HR analytics is the process of analyzing that data to make decisions.
Most HRIS platforms include basic native analytics in the form of HR dashboards, but dedicated analytics tools typically connect to the HRIS and other systems like applicant tracking, payroll, and performance management to pull a more complete picture.
What is the difference between predictive analytics and prescriptive analytics?
Predictive analytics uses machine learning algorithms and historical patterns to forecast what's likely to happen. An example of this would be a skills gap analysis pointing to a future limitation in your current workforce.
Prescriptive analytics goes a step further by using those predictions, combined with rules-based or AI-driven recommendations, to suggest what to do about it, such as creating a targeted upskilling program or reallocating talent to fill that skills gap.
How does HR analytics benefit businesses?
HR analytics replaces guesswork with evidence across hiring, retention, performance, and workforce planning. In practice, that means catching problems like turnover spikes or staffing gaps before they get expensive, and giving leaders enough confidence in the data to actually act on it.
And with machine learning in HR analytics, those predictions get sharper the longer you use them because the model picks up on patterns specific to your org, like which retention factors matter for your specific workforce. The more data it sees, the more accurate its f
