What is the Bookings Dataset?
The Bookings dataset provides detailed, reservation-level data directly from your property management system (PMS). This powerful dataset gives you granular insights into stay patterns, booking behavior, and revenue performance across your properties. It serves as the foundation for understanding your current business performance as well as historical trends.
Key Information Available in the Bookings Dataset
The Bookings dataset allows you to analyze:
Occupancy and Revenue Performance: Track Units, ADR, Revenue, RevPAR, and other key metrics
Guest Stay Patterns: Analyze Length of Stay and Lead Time trends
Segment Performance: Compare business mix across different market segments
Channel Production: Evaluate booking channel effectiveness and profitability
Period-over-Period Analysis: Compare current performance to historical benchmarks (STLP or Previous)
Core KPIs Explained
Units: The number of room nights sold during a specific period
ADR (Average Daily Rate): The average rate per occupied room
Accommodation Revenue: The total rooms revenue generated
RevPAR (Revenue Per Available Room): Total rooms revenue divided by total available rooms
Occupancy: The percentage of available rooms that were sold
Period-over-Period Analysis
Every KPI in the Bookings dataset has three variants to help you assess performance trends:
Base Measure (e.g., "ADR"): Current on-the-books performance
STLP Measure (e.g., "ADR STLP"): Same Time Last Period - shows how the metric was performing at the same point in time in your comparison period
Previous Measure (e.g., "ADR Previous"): Shows the final actualized value from your comparison period
Common Ways to Use the Bookings Dataset
Daily Performance Tracking
Monitor daily Units, ADR, and Revenue performance
Compare current performance to previous periods
Identify days requiring revenue management attention
Business Mix Analysis
Break down performance by segment, channel, or rate plan
Identify high-value segments or channels
Target marketing efforts based on performance data
Booking Behavior Analysis
Track lead time and length of stay trends
Identify shifts in guest booking patterns
Adjust pricing and distribution strategies accordingly
Profitability Assessment
Analyze revenue net of commission costs
Evaluate channel cost efficiency
Make informed distribution decisions
Advanced Analysis Tips
Combine Multiple Dimensions: Add multiple dimensions like segment, channel, and rate plan to understand performance drivers
Use Day-Type Analysis: Compare weekday vs. weekend performance patterns
Track Geographical Source Markets: Analyze guest country of origin to inform marketing strategies
Monitor Lead Time Changes: Watch for shifts in booking window to adjust pricing strategies
Need More Help?
For more guidance or specific inquiries about integrating and leveraging this data, please refer to the Insights help documentation or reach out to our Advisory team for personalized support.