Get smart insights about employee performance, reliability, shift patterns, cost optimization, and actionable recommendations powered by AI.
AI Analytics turns your scheduling data into actionable insights. Instead of manually reviewing spreadsheets and trying to spot patterns, the AI automatically analyzes employee performance, attendance, shift coverage, costs, and identifies trends you might miss.
The system tracks everything happening in your schedules and provides clear recommendations: who your most reliable employees are, where you're overspending, which shifts consistently have coverage problems, and specific actions you can take to improve.
The analytics dashboard has 4 main tabs, each focusing on different aspects of your scheduling:
📊 Overview
High-level metrics, AI insights, recommendations, and top performers
✅ Attendance
Attendance rates, clock-in status, and employee attendance details
🏆 Performance
Performance scores, top performers, improvement trends
📈 Coverage
Coverage percentage, gaps, trends over time
You can view analytics for different time periods by clicking the timeframe buttons:
All metrics, charts, and insights automatically update when you change the timeframe. This lets you compare performance across different periods or zoom in on specific weeks for detailed analysis.
Track how well each employee is performing based on shift completion, attendance, clock-in punctuality, and overall reliability.
Shift Completion Rate
Attendance Rate
Overall Performance Score
Sarah Johnson
45 shifts | 100% attendance
98%
Performance
Mike Davis
32 shifts | 87% attendance
79%
Performance
John Smith
28 shifts | 71% attendance
65%
Performance
Identify your most and least reliable employees with detailed attendance tracking and pattern recognition.
Employees with high attendance, zero no-shows, and consistent reliability
See if attendance is improving, declining, or staying steady over time
Employees who frequently don't show up for scheduled shifts
Track punctuality and identify chronic late arrivals
How often employees complete their full scheduled shifts
AI analyzes past scheduling data to recommend the best employees for specific shifts based on their performance history.
Friday Evening Shift (6 PM - 10 PM)
Restaurant Location - Server Role
1. Sarah Johnson (Recommended)
Worked 12 similar Friday evenings - 100% attendance
Average performance: 98% on this shift type
2. Mike Davis (Good Fit)
Worked 8 similar shifts - 88% attendance
Available and qualified
3. Lisa Chen (Available)
New employee - Limited Friday evening experience
Consider pairing with experienced staff
Track labor costs, identify overspending, and get recommendations to optimize your scheduling budget.
Total Labor Hours
Estimated Labor Cost
Cost Per Shift Analysis
Overtime Alerts
AI automatically detects recurring patterns in your scheduling, coverage, attendance, and performance data.
Coverage Patterns
Attendance Patterns
Performance Patterns
Time-Off Patterns
Sunday evening shifts are 40% understaffed on average
Recommendation: Offer incentive pay for Sunday evenings or hire additional weekend staff
Friday and Saturday nights have 2x more staff than needed
Recommendation: Reduce staffing by 2 employees to save approximately $800/month
No-show rate drops from 15% to 3% on shifts scheduled 2+ weeks in advance
Recommendation: Publish schedules at least 2 weeks ahead for better attendance
AI doesn't just show you data - it tells you exactly what actions to take to improve your scheduling, reduce costs, and boost performance.
Staffing Recommendations
Performance Recommendations
Cost Recommendations
Coverage Recommendations
Review the recommendations in your analytics dashboard and take action on the most important ones. Each recommendation tells you exactly what to change and why. Start with high-impact items like reducing overstaffing or addressing attendance issues, then work through lower-priority suggestions as time allows.
Monitor how well your shifts are covered and identify gaps before they become problems.
What percentage of your shifts are fully staffed (e.g., "82% coverage this week")
Specific shifts that still need employees, ordered by urgency
Is coverage improving or declining over time
Which locations have better/worse coverage rates
92%
This Week Coverage
↑ 8% from last week
6
Open Shifts
Need immediate attention
15
Employees Available
Can fill open shifts
3
High-Risk Days
Below 75% coverage
Track who's clocking in on time, who's frequently late, and overall attendance trends across your team.
See who's currently clocked in vs scheduled to be working
Track employees who clock in after their shift start time
Employees who were scheduled but never clocked in
Overall percentage of scheduled shifts where employees showed up
Get notified when:
View charts and graphs showing how your key metrics change over time - daily, weekly, monthly, or quarterly.
The Overview tab is your main analytics dashboard showing all key metrics at a glance. This is where you'll spend most of your time reviewing overall performance.
Four main metric cards displayed prominently at the top:
Total Employees
Number of active team members in your organization
Coverage Rate
Percentage of shifts that are fully staffed (e.g., 92%)
Attendance Rate
Percentage of employees who showed up when scheduled
Total Hours
Total scheduled hours across all employees
Below the key metrics, you'll see AI-generated insights about your scheduling:
See your best employees ranked by performance score. Shows:
Visual bar chart showing required vs assigned shifts for each day:
Set aside 15 minutes every Monday to review your analytics dashboard. Look for new recommendations, check coverage trends, and identify any red flags before they become bigger problems.
Focus on the top 3 AI recommendations each week. Don't try to implement everything at once. Small, consistent improvements compound over time.
Recognize high-performing employees by showing them their stats. "You had 100% attendance this month - thank you!" goes a long way. Use data to reward excellence.
Export cost trends when planning your labor budget. Historical data helps you estimate future costs more accurately and justify budget requests to upper management.
When analytics flags an employee with declining attendance, have a conversation immediately. Early intervention prevents small issues from becoming termination-worthy problems.
Use location comparison data to identify your best-performing location, then see what they're doing differently. Replicate successful patterns across other locations.
When you implement a recommendation, note the date and check back in 2-4 weeks. Did coverage improve? Did costs go down? Use data to validate what's working.
Ready to unlock powerful insights and optimize your scheduling with AI analytics?