You're losing employees faster than you'd like, so you ask your managers why people are leaving. They tell you it's pay. Everyone always says it's pay. So you increase wages by $2/hour and turnover barely budges. Turns out the real issue was inconsistent scheduling practices at three of your locations and a toxic shift lead at a fourth. You could have identified these root causes months earlier through data.
People analytics is the practice of collecting, analyzing, and acting on workforce data to make better HR and business decisions. Instead of relying on instinct or anecdotes, you use systematic analysis to understand what's actually happening with your workforce and what factors drive the outcomes you care about.
The sophistication level varies dramatically. Basic people analytics might be running a report that shows turnover by location. Advanced analytics builds predictive models that identify which employees are likely to quit in the next 90 days. According to HR Vision's 2024 trends research, more than 9 in 10 (94%) business leaders say that people analytics elevates the HR profession.
What People Analytics Looks Like for Shift Workers
The analytics that matter most for businesses with hourly employees differ from what matters for office-based organizations. Frontline businesses care about shift attendance, schedule adherence, time-to-productivity for new hires, and customer satisfaction scores by team.
Turnover analysis forms the foundation for most frontline analytics initiatives because turnover is expensive and measurable. Start with basic reporting: What's your overall turnover rate? How does it vary by location, by position, by shift, by manager, and by tenure? Patterns emerge quickly. You might discover that turnover in the first 30 days is 3x higher than turnover after 90 days, that overnight shifts turn over 50% faster than day shifts, or that one location has turnover half the company average.
Those patterns raise questions that deeper analysis can answer. Why do new hires leave in the first month? Exit interview data might point to unrealistic expectations set during hiring. Schedule analysis might reveal that new hires get the worst shifts until they build seniority.
Scheduling analytics optimize labor costs and employee satisfaction simultaneously. How often do employees get their preferred shifts? What's the relationship between schedule predictability and retention? Do employees who regularly work split shifts or clopening leave faster than those with consistent schedules?
Performance analytics identify what separates great employees from struggling ones. This gets tricky with hourly workers because "performance" can be hard to quantify beyond obvious metrics like attendance. Better organizations track customer satisfaction scores by employee, sales per hour worked, speed of service metrics, and error rates or quality scores.
Getting Started Without a Data Science Team
The phrase "people analytics" conjures images of data scientists building complex models. The reality for most businesses is much more accessible. You don't need a PhD to analyze whether your Monday night shift has higher turnover than your Friday day shift.
Start with the data you already have. Your payroll system tracks hours worked, overtime, wage rates, and tenure. Your HRIS stores demographic information, job titles, locations, hire and termination dates. Your scheduling system knows who worked when and whether they showed up.
Export this data to Excel or Google Sheets and start with basic analysis. Calculate turnover by location, by position, by hire month. Look for patterns. Maybe summer hires turn over 2x faster than employees hired in fall. Maybe kitchen staff leave 30% faster than front-of-house.
Each pattern suggests a question worth investigating. If summer hires leave faster, is it because you're hiring lower-quality candidates during peak season, or because students return to school, or because summer working conditions are harder?
According to Crunchr's State of People Analytics report, organizations that excel in people analytics are 5x more likely to integrate HR data with non-HR business data. For frontline businesses, this means connecting workforce data with operational metrics. Match your labor scheduling data to your sales data. Do you have the right staffing levels during peak times?
Predictive Analytics: Knowing Who's About to Quit
Basic analytics tells you what happened. Predictive analytics tells you what's likely to happen next, giving you time to intervene. The most valuable prediction for most businesses is turnover risk, which means identifying those employees most likely to quit before they give notice.
Turnover prediction models look for patterns in employees who left voluntarily. What did they have in common in the 60-90 days before they quit? Common leading indicators include decreased shift pickup (employees who were flexible about taking extra shifts suddenly stop volunteering), increased time-off requests or call-outs, reduced engagement in workplace communication, and schedule change requests suggesting they're looking for different hours.
You don't need fancy software to spot these patterns. Pull data for employees who quit in the last six months. Look at their behavior in the three months before they left. If 70% of them had increases in absenteeism during that window, absenteeism becomes a turnover risk indicator worth monitoring.
Once you know what predicts turnover, you can act on it. If an employee who's been reliable for eight months suddenly calls out twice in two weeks, that might be random. Or it might be a signal that something changed and they're disengaging.
Common Analytics Mistakes
Poor data quality undermines everything. If your turnover calculation includes employees who were fired in your "voluntary turnover" number, your analysis is wrong. Before analyzing anything, verify your data is accurate and consistently formatted.
Correlation isn't causation, but it's still useful. Just because employees who work weekend shifts have higher turnover doesn't necessarily mean weekend shifts cause turnover. Maybe weekend shifts attract younger workers who are more likely to leave for college. The correlation is still valuable because it tells you where to investigate further.
Small sample sizes create misleading patterns. If you have one location with three employees and two quit, that's 67% turnover. That sounds terrible, but it's too small a sample to conclude anything meaningful.
Looking at averages hides important variation. Your company-wide average tenure might be 18 months, which seems decent. But if half your employees leave in 30 days while the other half stay for three years, that average doesn't tell you anything useful. Look at distributions, not just averages.
Tools and Technology for Frontline Analytics
The HR Analytics Market was valued at $3.61 Billion in 2023 and is expected to reach $11.96 billion by 2032, driven largely by businesses realizing that workforce data is as strategically important as financial data.
For small to mid-sized businesses, analytics functionality often comes bundled in your HRIS or workforce management platform. These built-in tools won't produce cutting-edge predictive models, but they'll generate the reports you actually need, like turnover by location, overtime trends, schedule adherence, and time-to-fill for open positions. Start there before investing in specialized analytics software.
Dedicated people analytics platforms offer more sophisticated capabilities: predictive turnover modeling, automated alerts when metrics cross thresholds, natural language queries, data visualization that makes patterns obvious, and benchmarking against industry standards.
These platforms vary in how well they handle shift-based workforces. Many were built for office environments and struggle with the realities of hourly scheduling, multiple concurrent positions, and high-volume hiring. Ask specific questions during demos: Can it track someone who works different positions at different locations? Does it understand shift differentials?
Spreadsheet-based analytics remains viable for many businesses. Excel or Google Sheets can perform surprisingly sophisticated analysis if you have clean data exports from your source systems. The limitation is that manual exports and analysis don't scale well as you grow.
Privacy and Ethics in Workforce Analytics
Analyzing employee data creates ethical obligations beyond legal compliance. Just because you can track something doesn't mean you should.
Transparency matters. Employees should know what data you're collecting and how you're using it for analysis. You don't need to explain your entire analytical methodology, but they should understand that workforce data informs decisions about staffing and compensation.
Avoid creating perverse incentives. If you track and reward individual sales metrics too heavily, you'll create competition between team members instead of collaboration. Analytics should improve business outcomes and employee experience simultaneously.
Protect individual privacy even while analyzing aggregate patterns. Turnover analysis that identifies patterns by location and shift is valuable. Dashboards that let managers see which specific employees are predicted to quit create risks if managers act inappropriately.
Test for disparate impact. If your analysis identifies that employees who live more than 20 miles away have higher turnover and you use that to screen candidates, you might be creating illegal discrimination if distance correlates with protected characteristics.
Making Analytics Actually Useful
The point of people analytics isn't generating reports. It's changing decisions and behaviors. Reports that sit unread deliver zero value.
Start with a business problem. "We need better people analytics" isn't a goal. "We're spending $400K annually replacing employees who quit in their first 90 days and we want to reduce that by 50%" is a goal. The analytics work backward from that objective.
Make insights accessible to decision-makers. If only your HR director can access analytics, you're limiting impact. Managers making daily decisions about scheduling and coaching need relevant data at their fingertips. This might mean automated reports that show each manager their team's metrics compared to company averages.
Close the feedback loop. When you take action based on analytics, measure whether it worked. If you implemented a new onboarding program because analytics showed high early turnover, track whether 30-day turnover actually decreased after the change.
For businesses managing hourly workers across multiple locations, people analytics transforms HR from reactive crisis management to proactive strategy. You stop guessing why turnover is high and start knowing. You stop applying the same solutions everywhere and start targeting interventions where they're needed.
The barrier isn't technology or statistical expertise. It's the commitment to actually look at your workforce data systematically and act on what you find. Most businesses already have the data they need to make significantly better decisions. They just haven't analyzed it yet.
