🔍 QueryBox Query Examples & Tips

Master natural language queries for your data

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📊 CSV & Excel Files

Sales & Revenue Data

💰 Basic Revenue Analysis
"What were our total sales last quarter?"
"Show me the top 10 products by revenue"
"Which customers spent the most money?"
📈 Trend Analysis
"Compare sales between Q1 and Q2"
"Show me monthly revenue trends for 2024"
"Which months had sales above $50,000?"
🎯 Customer Insights
"How many unique customers do we have?"
"Which customers haven't purchased in 6 months?"
"What's the average order value by customer segment?"

Employee & HR Data

👥 Workforce Analytics
"How many employees in each department?"
"What's the average salary by role?"
"Show me employees hired in the last year"
📊 Performance Metrics
"Which employees have the highest performance ratings?"
"What's the average tenure by department?"
"Show me employees due for review this month"

Inventory & Supply Chain

📦 Stock Management
"Which products are low in stock?"
"What's the total inventory value?"
"Show me products that haven't sold in 90 days"
🚚 Supplier Analysis
"Which suppliers have the fastest delivery times?"
"What's our average order quantity by supplier?"
"Show me overdue orders"

📄 PDF Documents PRO

Research Papers & Reports

🔬 Content Summarization
"Summarize the main findings of this research paper"
"What are the key recommendations in this report?"
"List the main topics covered in this document"
🔍 Specific Information Extraction
"What does this paper say about climate change?"
"Find all mentions of 'artificial intelligence'"
"What methodology did the researchers use?"
📊 Multi-Document Analysis
"Compare the conclusions across all uploaded reports"
"What are the common themes in these documents?"
"Summarize each document separately"

Contracts & Legal Documents

⚖️ Contract Review
"What are the key terms of this contract?"
"What are the payment terms?"
"When does this contract expire?"
🔐 Compliance & Risk
"What are the liability clauses?"
"List all obligations in this agreement"
"What are the termination conditions?"

Financial Statements & Reports

💼 Financial Analysis
"What was the net profit for Q4?"
"Summarize the financial highlights"
"What are the major expenses listed?"

📊 Google Sheets PRO

Live Data Queries

🔄 Real-Time Analysis
"What's the current total in column B?"
"Show me rows where status is 'Pending'"
"What's the average of the last 30 entries?"
📈 Collaborative Data
"Which team members have completed their tasks?"
"What's the progress percentage for each project?"
"Show me overdue items"

💡 Pro Tips for Better Queries

1. Be Specific

Good: "Show me sales above $10,000 in Q2 2024"

Avoid: "Show me some sales"

2. Use Natural Language

No SQL needed! Ask questions like you would ask a colleague.

Example: "Which customers bought more than 5 items?"

3. Ask Follow-Up Questions PRO

With conversational memory, you can build on previous queries.

Example: "Now show me their contact information"

4. Combine Multiple Criteria

Ask complex questions in one query.

Example: "Show me products with low stock and high demand"

5. Use Comparisons

Compare time periods, categories, or metrics.

Example: "Compare revenue between January and February"

6. Ask for Summaries

Get quick insights without detailed data.

Example: "Summarize the key trends in this data"

💡 Pro Tip: Use the Query History feature (PRO) to save your best queries and re-run them with one click!

🚀 Advanced Query Techniques

Aggregations & Calculations

  • "What's the average order value?"
  • "Calculate the total revenue by product category"
  • "Show me the median salary by department"
  • "What's the percentage change in sales month-over-month?"

Filtering & Sorting

  • "Show me only active customers"
  • "List products sorted by price, highest first"
  • "Filter orders from the last 30 days"
  • "Show me employees with salaries above $75,000"

Pattern Recognition

  • "Which products are frequently bought together?"
  • "Show me seasonal trends in sales"
  • "What patterns do you see in customer behavior?"
  • "Identify outliers in this dataset"

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