Overview
The Occupancy Statistics page provides comprehensive analytics on facility utilization, parking duration patterns, and visitor behavior. Track how your spaces are being used, understand peak occupancy periods, and optimize capacity planning based on real-world data. Access this page from Statistics → Occupancy in the main navigation.
Key Features:
- Average Duration: Understand typical parking session lengths
- Occupancy Rate: Monitor real-time facility utilization percentage
- Unique Visitors: Track individual users and visitor count
- Hourly Patterns: Visualize occupancy levels throughout the day
- Duration Analysis: Breakdown of short-term vs long-term parking
- Customer Insights: Identify frequent users and high-value customers
Dashboard Metrics
The top section displays three critical occupancy indicators that provide instant visibility into facility performance and utilization efficiency.
Average Duration
Description: Mean parking session length across all completed visits
Icon: Cyan/green timer symbol
Format: "X hours and Ymin" (e.g., "3 hours and 18min")
Calculation: Sum of all session durations ÷ number of sessions
- Represents typical customer parking behavior
- Calculated from entry to exit time for completed sessions
- Excludes active/ongoing parking sessions
- Critical for pricing strategy and revenue optimization
- Helps determine ideal rate structure (hourly vs daily vs flat)
Occupation Rate
Description: Percentage of capacity currently in use
Icon: Cyan clock/percentage symbol
Format: Percentage (e.g., "2%", "67%", "95%")
Calculation: (Currently occupied spaces ÷ Total capacity) × 100
- Real-time indicator updated continuously
- Shows snapshot of current facility utilization
- Essential for capacity planning and expansion decisions
- Helps identify over/under-utilized facilities
- Drives dynamic pricing decisions in smart systems
| Rate Range | Status | Action Needed |
|---|---|---|
| 0-30% | Low utilization | Consider marketing, pricing adjustments |
| 30-70% | Optimal range | Maintain operations, monitor trends |
| 70-90% | High utilization | Prepare overflow plans, consider expansion |
| 90-100% | At capacity | Activate overflow, turn away customers |
Unique Visitors
Description: Count of individual users/customers in selected period
Icon: Yellow/orange people/users symbol
Calculation: Distinct user IDs or vehicle identifiers
Purpose: Measure customer base and visitor reach
- Counts each customer only once regardless of visit frequency
- Based on registered users, RFID cards, or license plate recognition
- Excludes anonymous/unregistered parkers unless tracking available
- Indicates customer diversity vs repeat visitors
- Higher count = broader customer base and market penetration
Occupancy by Hour Chart
The line chart displays average occupied spaces throughout 24-hour periods, revealing facility utilization patterns, peak demand times, and capacity planning opportunities.
Chart Features
- X-Axis: 24 hours from 00:00 to 19:00+ in hourly increments
- Y-Axis: Number of occupied spaces (automatically scaled to capacity)
- Data Points: Blue circles marking occupancy level per hour
- Trend Line: Smooth curve connecting hourly occupancy values
- Baseline: Zero line showing empty facility
- Capacity Indicator: Top of chart represents maximum capacity
Understanding Occupancy Patterns
Typical patterns reveal facility purpose and user behavior:
| Facility Type | Peak Occupancy Hours | Pattern Characteristics |
|---|---|---|
| Office Building | 8:00-18:00 | Sharp morning rise (7:00-9:00), plateau during day, evening drop (17:00-19:00) |
| Shopping Mall | 11:00-21:00 | Gradual morning rise, peak afternoon/evening, late close |
| Airport | All hours | Multiple peaks matching flight schedules, high overnight |
| Hospital | 9:00-20:00 | Consistent moderate levels, visiting hours impact |
| Event Venue | Event-dependent | Dramatic spikes during events, near-zero otherwise |
Capacity Planning Insights
Use occupancy patterns to optimize facility management:
- Peak Identification: Hours consistently near capacity indicate need for expansion
- Low Utilization: Hours with < 30% occupancy suggest over-capacity or marketing opportunity
- Turnover Windows: Dips between peaks show natural clearing periods
- Staffing Optimization: Align attendant schedules with occupancy curves
- Maintenance Scheduling: Perform work during predictable low-occupancy hours
Duration Distribution
The donut chart visualizes parking session lengths in five categories, revealing customer behavior patterns and helping optimize pricing tiers for different usage types.
Duration Categories
- < 1h (Cyan): Quick stops, errands, pickups/dropoffs
- 1-2h (Orange): Short-term parking, shopping, appointments
- 2-4h (Gray): Medium-term visits, dining, entertainment
- 4-8h (Purple): Long-term parking, work day, all-day shopping
- > 8h (Pink): Extended parking, overnight, multi-day stays
Each segment shows percentage of total sessions falling into that duration range. Center displays total session count for reference.
Analyzing Distribution Patterns
Different distributions indicate different facility usage:
| Pattern | Typical Distribution | Facility Type |
|---|---|---|
| Short-term dominant | 60%+ under 2 hours | Retail, quick-service dining, transit drop-off |
| Mid-range dominant | 50%+ in 1-4 hour range | Entertainment, casual dining, healthcare |
| Long-term dominant | 60%+ over 4 hours | Office buildings, commuter lots, airports |
| Extended stays | 30%+ over 8 hours | Airport long-term, event venues, residential |
| Balanced mix | Even across categories | Mixed-use developments, city centers |
Pricing Strategy Optimization
Use duration distribution to optimize rate structure:
- High Short-Term (< 1h): Consider premium first-hour rate to maximize turnover revenue
- Dominant 1-2h: Offer 2-hour packages or "shopper specials" to capture this segment
- Long 4-8h: Introduce daily maximum rates to attract commuters
- Extended > 8h: Create multi-day packages or monthly permit programs
- Balanced Mix: Implement tiered hourly rates that reward longer stays
Top Customers
The Top Customers section displays your most frequent users and highest-revenue customers, enabling customer relationship management and loyalty program targeting.
Customer Ranking Criteria
Customers can be ranked by multiple metrics:
- Visit Frequency: Total number of parking sessions in period
- Revenue Generated: Total fees paid across all sessions
- Average Duration: Longer sessions indicate different usage patterns
- Recency: Recent activity vs dormant accounts
- Lifetime Value: Historical revenue since account creation
When No Data Appears
If the section displays "Aucune transactions associer a un client pour l'instant" (No transactions associated with a client at the moment):
- Anonymous Parking: All sessions are unregistered/guest parking
- No User Accounts: System not configured for user registration
- License Plate Only: Tracking by plate but not linked to customer profiles
- New System: Recently deployed with no historical customer data yet
- Filter Issue: Selected time period has no registered customer activity
Customer Insights & Actions
When customer data is available, leverage it for:
- Loyalty Programs: Reward frequent parkers with discounts or free hours
- Monthly Permits: Offer subscriptions to top 20% of users for guaranteed revenue
- Personalized Pricing: VIP rates for high-value customers
- Retention Campaigns: Target customers who haven't visited recently
- Feedback Collection: Survey top customers to improve service quality
- Business Accounts: Identify companies for B2B parking contracts
Time Period Filtering
Time filter buttons in the top-right corner allow you to analyze occupancy patterns across different timeframes, from daily operations to long-term trend analysis.
Available Time Periods
| Period | Label | Analysis Use Case |
|---|---|---|
| 24 Hours | 24h | Real-time operations, today's performance monitoring |
| 7 Days | 7d | Weekly patterns, weekday vs weekend comparison |
| 1 Month | 1m | Monthly performance, pricing impact analysis |
| 6 Months | 6m | Seasonal trends, semi-annual business reviews |
| 4 Years | 4y | Long-term growth, capacity planning, ROI analysis |
Custom Date Ranges
Use custom filtering for specific business analysis:
- Event Impact: Compare occupancy during special events vs normal days
- Holiday Analysis: Understand seasonal patterns (Christmas, summer, etc.)
- Pricing Tests: Measure before/after impact of rate changes
- Marketing Campaigns: Track occupancy changes during promotional periods
- Year-over-Year: Compare same week/month across multiple years
Filter Effects on Metrics
When you change the time filter:
- ✓ Average Duration recalculates for sessions in selected period
- ✓ Unique Visitors counts distinct users within timeframe
- ✓ Occupancy by Hour chart shows average occupancy for period
- ✓ Duration Distribution updates to show period's session lengths
- ✓ Top Customers ranks based on activity in selected timeframe
- ✗ Occupation Rate always shows current real-time percentage
Usage Insights & Business Intelligence
Transform occupancy data into actionable business insights that drive revenue growth, operational efficiency, and strategic planning.
Capacity & Expansion Planning
- Utilization Rate: Consistently > 85% indicates capacity constraints
- Peak Hour Saturation: Hours at 100% = lost revenue from turned-away customers
- Growth Trajectory: Compare occupancy rates across months/years
- ROI Justification: High utilization justifies expansion investment
- Overflow Alternatives: Consider valet, off-site, or partnered lots
Revenue Optimization Strategies
- Dynamic Pricing: Increase rates during high-occupancy hours (surge pricing)
- Duration-Based Tiers: Optimize rates for your actual duration distribution
- Early Bird Specials: Incentivize parking during low-occupancy morning hours
- Maximum Daily Rates: Capture long-stay customers without price resistance
- Membership Programs: Convert frequent visitors to monthly permits for guaranteed revenue
- Event Pricing: Special flat rates during peak demand events
Operational Excellence
- Staff Scheduling: Align attendants with occupancy patterns, not clock hours
- Maintenance Windows: Schedule cleaning/repairs during low-occupancy periods
- Customer Experience: Prevent overcrowding that leads to poor reviews
- Security Optimization: Increase patrol during high-occupancy hours
- Technology Investment: Automated systems reduce costs at high-volume times
Best Practices
Implement these proven strategies to maximize occupancy analytics value and drive continuous improvement in facility performance.
Daily Operations Monitoring
- ✅ Check 24h view every morning to review yesterday's performance
- ✅ Monitor occupation rate throughout day - activate overflow at 85%
- ✅ Track average duration trends - sudden changes indicate issues
- ✅ Review unique visitors - compare to historical averages
- ✅ Document any anomalies or unusual patterns for investigation
Strategic Analysis Cadence
- ✅ Weekly reviews using 7d filter to identify weekly patterns
- ✅ Monthly business reviews with 1m filter for performance analysis
- ✅ Quarterly capacity planning using 6m filter for seasonal trends
- ✅ Annual strategic planning with 4y filter for long-term growth
- ✅ Compare year-over-year using custom ranges for growth validation
Revenue Maximization
- ✅ Adjust pricing based on duration distribution every quarter
- ✅ Test dynamic pricing during consistent high-occupancy hours
- ✅ Create loyalty programs targeting top 20% of customers
- ✅ Offer monthly permits when occupancy consistently hits 70%+
- ✅ Implement early bird/off-peak discounts to balance occupancy curves
Data Quality & Accuracy
- ✅ Verify occupation rate matches physical reality through spot checks
- ✅ Ensure all entry/exit sensors functioning correctly
- ✅ Audit duration calculations - verify entry/exit timestamp accuracy
- ✅ Validate unique visitor counts against known customer database
- ✅ Regular system health checks to prevent data recording failures
Ready to optimize your parking facility?
Explore visitor analytics and revenue statistics for complete facility performance optimization and data-driven management.
