Understanding Scoop AI

Scoop transforms natural language questions into powerful data analysis using advanced AI. This guide explains how our AI works and how to get the most from it.

Natural Language Processing

How Scoop Understands You

Scoop uses sophisticated language models to understand intent, not just keywords:

• Context Awareness: Remembers previous questions in the conversation
• Business Terminology: Understands industry-specific terms
• Flexible Phrasing: Many ways to ask the same question work equally well
• Intent Recognition: Knows when you want a chart vs. a number vs. an explanation

Query Classification System

When you ask a question, Scoop instantly classifies it:

When Deep Reasoning Activates

Automatic Triggers

Scoop automatically initiates deep reasoning for:

1. Causal Questions

   • "Why did..."
   • "What caused..."
   • "What's driving..."

2. Complex Analysis

   • "How can we improve..."
   • "What should we do about..."
   • "What explains..."

3. Multi-Part Questions

   • "Why did X happen and what should we do?"
   • "What's the impact across all metrics?"

The Analysis Choice Interface

For certain complex questions, Scoop presents you with analysis options:

💡 This question might benefit from deeper analysis. Choose how to proceed:

[⚡ Quick Analysis] [🧠 Deep Analysis-beta (>1min)]

When You'll See This:

• Questions with multiple valid approaches
• Queries that could benefit from thorough investigation
• Scenarios where time vs. depth is a tradeoff

⚡ Quick Analysis

• Results in 5-10 seconds
• Single-pass analysis
• Direct answers to your question
• Best for: Routine queries, time-sensitive decisions

🧠 Deep Analysis-beta

• Takes 1-3 minutes
• Multi-step investigation
• Explores multiple hypotheses
• Tests various correlations
• Provides comprehensive findings
• Best for: Root cause analysis, strategic decisions

Example Scenario:

You: Why did our conversion rate drop?

Scoop: 💡 This question might benefit from deeper analysis. Choose how to proceed:

[⚡ Quick Analysis] - Basic trend and comparison
[🧠 Deep Analysis-beta] - Full investigation with root causes

What Happens During Reasoning

🧠 Initiating deep analysis...

Breaking down your question into investigative steps:
1. Analyze historical trends
2. Compare segments and cohorts
3. Check for correlations
4. Test statistical relationships
5. Synthesize findings

🔍 Investigating... [progress indicators]
✓ Each step completed with findings
📊 Results synthesized into coherent insights

Understanding Confidence Levels

What Confidence Means

Scoop provides confidence levels for all insights:

• 🟢 High Confidence (above 80%)

  • Strong statistical evidence
  • Multiple supporting data points
  • Consistent patterns
  • You can act on these insights

• 🟡 Medium Confidence (50-80%)

  • Probable relationship
  • Some supporting evidence
  • Worth investigating further
  • Consider additional validation

• 🔴 Low Confidence (below 50%)

  • Weak correlation
  • Limited data
  • Hypothesis stage
  • Needs more investigation

Statistical Transparency

Every ML finding includes:

• Accuracy: How often the model is correct
• Sample Size: How much data supports this
• P-value: Statistical significance
• Effect Size: Practical importance

AI Transparency Features

Following the Evidence Trail

Click "see why" on any finding to view:

• Supporting data points
• Statistical calculations
• Alternative explanations considered
• Confidence breakdown

Understanding "No Pattern Found"

When Scoop reports no pattern, this means:

• ✅ Valuable Information: Rules out false assumptions
• ✅ Statistical Rigor: Prevents seeing patterns that don't exist
• ✅ Action Insight: Look for other factors
• ❌ Not an Error: This is a valid analytical result

Model Selection Transparency

Scoop automatically selects the best approach:

• Decision Trees (J48): For clear, explainable rules
• Rule Induction (JRip): For if-then patterns
• Clustering (K-means): For natural groupings
• Statistical Tests: For correlations and significance

Getting the Most from Scoop AI

Best Practices

1. Start with Why

   • ✅ "Why are sales declining?"
   • ❌ "Show sales" (too basic for AI features)

2. Be Specific About Outcomes

   • ✅ "What drives customer retention?"
   • ❌ "Analyze customers" (unclear goal)

3. Include Context

   • ✅ "Why did churn increase after our price change?"
   • ❌ "Analyze churn" (missing context)

4. Trust the Process

   • Let reasoning complete (10-30 seconds for complex questions)
   • Review all findings, not just the summary
   • Check confidence levels
   • Ask follow-up questions

Progressive Analysis

Build your analysis progressively:

1. Start Broad: "How is the business performing?"
2. Identify Issues: "Revenue seems down in the West"
3. Dig Deeper: "Why is Western revenue declining?"
4. Find Actions: "What can we do to improve Western sales?"

Combining AI Capabilities

Get comprehensive insights by combining features:

"Why did churn increase [reasoning] and what predicts it [ML]?"

This triggers both:

• Deep reasoning to understand the why
• ML analysis to predict future churn

Common AI Patterns

Daily Intelligence

"What should I know about yesterday?"
→ AI summarizes key changes, anomalies, and insights

Problem Diagnosis

"Why are customers unhappy?"
→ Multi-step analysis across support, usage, and survey data

Predictive Planning

"What will happen if we raise prices?"
→ ML models predict impact based on historical patterns

Optimization

"How can we reduce costs without hurting growth?"
→ Complex reasoning balancing multiple objectives

Privacy and Security

How AI Handles Your Data

• Processing: All analysis happens in secure cloud environment
• Learning: Models don't train on your data
• Privacy: Each workspace is completely isolated
• Security: SOC2 compliant, encrypted at rest and in transit

AI Limitations

Scoop AI is powerful but has boundaries:

• Cannot access external data not provided
• Won't make decisions requiring human judgment
• Confidence levels reflect uncertainty
• Patterns found are correlations, not always causation

Next Steps

Now that you understand Scoop's AI:

1. Try Deep Reasoning: Ask a "why" question about your data
2. Explore ML Features: Use "what predicts" or "segment" queries
3. Check Confidence: Click "see why" to understand findings
4. Combine Approaches: Use multiple AI features together

Remember: Scoop AI is your analytical partner, augmenting your expertise with data-driven insights you can trust.