--- updatedAt: 2025-07-18T03:11:51.000Z --- Fetch the complete documentation index at: https://docs.scoopanalytics.com/llms.txt. Use this file to discover all available pages before exploring further. # Understanding Scoop AI # 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: | Query Type | What It Does | Example Questions | | -------------------- | ---------------------------------- | ------------------------------------------------- | | **DATASET** | Basic data retrieval and filtering | "Show me sales last month", "List top customers" | | **VISUALIZATION** | Creates charts and graphs | "Graph revenue over time", "Chart by region" | | **TEXT** | General explanations and help | "What does churn mean?", "How do I export?" | | **ML\_RELATIONSHIP** | Finds factors that drive outcomes | "What predicts churn?", "What drives sales?" | | **ML\_CLUSTER** | Discovers natural groups | "Segment our customers", "Find behavior patterns" | | **ML\_GROUP** | Compares populations | "Compare gold vs silver customers" | | **ML\_PERIOD** | Analyzes changes over time | "What changed after the launch?" | | **DEEP\_REASONING** | Multi-step investigation | "Why did revenue drop?", "How can we improve?" | ## 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.