Introduction
The AI Query Helper in Insights revolutionizes how you interact with your hospitality data, saving you valuable time while delivering deeper insights. This powerful tool transforms complex data exploration from a technical task into a natural conversation, allowing you to focus on strategic decisions rather than query building. By following these best practices, you'll maximize the effectiveness of this feature, dramatically accelerate your workflow, and uncover actionable insights that might otherwise remain hidden.
Getting Started with AI Query Helper
Start in the Right Topic
Before typing your first request, ensure you're building your query in the appropriate topic. This is essential for getting the results you want:
To compare current on-the-books (OTB) data to a previous period ➝ use the Bookings topic
To view what was OTB as of a prior date or analyze pick-up ➝ use the Pick-up topic
To evaluate forecast or budget data ➝ use the Forecast & Budget topic
To assess rate shopping data and competitor rates ➝ use the Rate Shopping topic
📖 Need help choosing the right topic? Refer to our Insights Topics Overview for guidance.
Accessing the Feature
Location: Look for the "AI Query" button in your Insights workbook
Conversation interface: The assistant opens a chat-like interface for your queries
Understanding the Query Building Process
Real-time feedback: Watch how your query changes as you refine your requests
Field selection visibility: Notice which fields the AI selects to satisfy your request
Translating business questions: The AI converts natural language to technical queries behind the scenes
Formulating Effective Queries
Be Specific and Complete
Complete requests yield accurate results the first time, saving multiple iterations
Include all key elements in your initial request:
Metrics: "Show me ADR, Occupancy, and RevPAR..."
Time period: "...for the next 90 days..."
Comparison period: "...compared to same time last year..."
Breakdown dimensions: "...broken down by segment and channel..."
Example: "Show me ADR and Revenue by channel for April 2025 compared to April 2024, sorted by Revenue descending"
Structure Multi-Part Requests Clearly
Clear structure helps the AI parse complex requests correctly, delivering precisely what you need
Instead of: "I want to see occupancy, ADR, and revenue year-to-date with monthly breakdowns showing year-over-year changes for my downtown properties focusing on groups versus transient business"
Better approach: "Show me YTD performance with monthly breakdown, comparing to last year. Include Occupancy, ADR and Revenue. Filter to downtown properties only and segment by group versus transient business."
Refinement Techniques
Iterative Query Building
This approach lets you build complex analyses incrementally while maintaining control
Begin with a basic question and refine with follow-up requests:
Start: "Show ADR and Revenue by month for this year"
Refine: "Now add Occupancy to the query"
Further refine: "Filter to only show weekends"
Final touches: "Sort by Revenue descending"
Describing Modifications Clearly
Clear modification requests ensure precise adjustments without rebuilding entire queries
Be specific about changes: "Change the date range to last 90 days"
Reference existing elements: "Keep the same metrics but change the breakdown to segment instead of channel"
Add calculations: "Add a column showing year-over-year growth percentage"
Navigating Mistakes and Making Corrections
Undoing Changes
Reset to previous state: "Go back to the previous query" or "Undo the last change"
Start fresh: "Start over with a new query" or "Reset this query"
Remove specific elements: "Remove the channel filter" or "Remove the Revenue column"
Correcting Misinterpreted Requests
Be explicit about corrections: "You misunderstood - I meant Property X, not Property Y"
Clarify ambiguities: "I meant revenue per available room, not revenue"
Provide context for terms: "By 'direct' I meant direct booking channels, not direct costs"
Version Management
Save intermediate versions: Before making substantial changes, save your current query
Use descriptive titles: Name saved queries based on their purpose for easier reference
Keep track of manual edits: After AI generates a query, note any manual adjustments you make
Understanding Results and Insights
Interpreting Generated Data
Check your units: Pay attention to the actual measurements (e.g., currency, percentages)
Verify time frames: Confirm that the date ranges match your intended analysis period
Understanding variance: When comparing periods, note whether differences are absolute or percentage-based
Getting Explanations
Ask for context: "Explain why ADR is higher this month compared to last year"
Request analysis: "What insights can you provide about this occupancy trend?"
Clarify metrics: "What exactly does RevPAR STLP represent in this context?"
Transitioning Between AI and Manual Analysis
Export results: Take AI-generated queries to Excel or other tools for further analysis
Save as templates: Use successful AI queries as starting points for similar analyses
Manual fine-tuning: Make precise adjustments to AI-generated queries when needed
Understanding Limitations and Working Within Them
Data Availability Constraints
Cross-Topic Data: The AI can only access data within the current topic (e.g., Bookings). For analysis combining Forecast & Budget, STR Benchmarking, or Pick-Up data, you'll need to:
Build separate queries in each topic
Use the XLOOKUP functionality to combine them
Reference: Combining Data Sets Across Topics
Complexity Boundaries
Too Many Metrics: Queries with excessive dimensions and metrics may become unwieldy
Solution: Break into multiple focused queries
Highly Complex Calculations: Some advanced custom calculations may require manual formula creation
Solution: Start with AI-generated query, then add calculations manually
Performance Impact: Very large datasets or highly complex queries may take longer to process
Special Cases
Custom Time Periods: Very specific date comparisons (e.g., "Compare the third Tuesday of each month") may require manual refinement
Complex Filtering Logic: Multiple nested conditions can be challenging for AI to interpret correctly
Solution: Build filters incrementally, confirming each step
Technical Constraints
Field Name Precision: The AI may occasionally confuse similarly-named fields
Example: Distinguishing between "Lead Time (Stay Date)" and "Lead Time (Arrival Date)"
Solution: Be explicit about which version you want
Advanced Visualizations: Some specialized visualizations may require manual configuration
Solution: Request the data structure you need, then customize the visualization manually
Industry-Specific Terminology and Concepts
Hospitality Metrics Understanding
The AI understands common hospitality terms like ADR, RevPAR, STLP, and MTD
Use standard industry terminology to get the most accurate results
For specialized metrics, provide a brief definition if needed
Handling Property-Specific Terms
Property codes or custom segment names may need additional explanation
Custom field groupings or user-defined labels should be explained clearly
Consider using the exact names from your property setup for best results
Advanced Usage
Comparative Analysis
Period-over-period analysis: "Compare Q2 performance to same period last year and two years ago"
Custom date ranges: "Show me June 1-15, 2025 versus June 1-15, 2024"
Multiple comparisons: "Show this month's ADR versus STLP and Previous"
Filtering & Segmentation
Multiple filters: "Show only high-ADR segments with occupancy below 70% for next month"
Nested criteria: "Find dates where weekday occupancy is above 85% but ADR is below $150"
Exclusions: "Show all channels except OTAs for this quarter"
Visualization Guidance
Request specific chart types: "Show this in a bar chart with months on the x-axis"
Ask for pivoted data: "Pivot the query to show months across columns and segments down rows"
Request totals: "Add column totals to the query"
Common Pitfalls to Avoid
Overcomplicating Requests
Simpler requests deliver more accurate results and are easier to refine
Avoid: Extremely long, multi-part questions with many conditions
Better: Break down complex analyses into sequential, focused queries
Ambiguous Terminology
Precise terminology ensures the AI retrieves exactly what you need
Avoid: Vague terms like "performance," "results," or "data"
Better: Specify exact metrics like "ADR," "Revenue," "Occupancy," or "Units"
Assuming Context
Clear context prevents misinterpretation and rework
Avoid: References without context ("Show me the same for last month")
Better: Provide sufficient context in each request
Summary
The AI Query Helper transforms data analysis from a technical exercise into a strategic advantage for your property or portfolio. By following these best practices and understanding its capabilities and limitations, you'll unlock deeper insights in a fraction of the time traditionally required. The result? More informed decisions, optimized revenue strategies, and a competitive edge in your market. As you continue to interact with the AI, it learns your specific needs and terminology, becoming an increasingly valuable partner in your revenue management process.
📌 Need more information? Reach out to our Advisory team via the chat with any feedback on particularly good examples you've used and/or you need any support!