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Investigation Patterns - What to Ask Your AI Analyst

Investigation Patterns: What to Ask Your AI Analyst

The Secret: Think Like an Executive, Not a Query Writer

Don't ask for data. Ask for insights, investigations, and understanding.

Core Investigation Patterns

1. The "Why" Investigation

Purpose: Uncover root causes of changes or issues

Pattern: "Why did [metric] [change]?"

Examples:

  • "Why did revenue drop last month?"
  • "Why are conversion rates improving?"
  • "Why did customer satisfaction decrease?"
  • "Why are costs rising faster than revenue?"

What Scoop Does: Multi-probe investigation → root cause identification → action plan

2. The "Analyze" Investigation

Purpose: Discover patterns and segments you don't know exist

Pattern: "Analyze my [entity/area]"

Examples:

  • "Analyze my customer base"
  • "Analyze sales performance"
  • "Analyze product usage patterns"
  • "Analyze team productivity"

What Scoop Does: ML-powered discovery → hidden segments → strategic insights

3. The "What Drives" Investigation

Purpose: Find non-obvious factors influencing outcomes

Pattern: "What drives [outcome]?"

Examples:

  • "What drives customer retention?"
  • "What drives deal size?"
  • "What drives product adoption?"
  • "What drives team performance?"

What Scoop Does: Statistical analysis → correlation discovery → predictive factors

4. The "Find Patterns" Investigation

Purpose: Uncover hidden patterns and anomalies

Pattern: "Find patterns in [data/behavior]"

Examples:

  • "Find patterns in customer churn"
  • "Find patterns in sales cycles"
  • "Find patterns in support tickets"
  • "Find patterns in usage data"

What Scoop Does: Pattern recognition → anomaly detection → business implications

5. The "Compare" Investigation

Purpose: Understand differences and changes

Pattern: "Compare [A] to [B]"

Examples:

  • "Compare this quarter to last quarter"
  • "Compare high-value customers to others"
  • "Compare successful deals to lost deals"
  • "Compare top performers to average"

What Scoop Does: Multi-dimensional comparison → key differentiators → insights

Advanced Investigation Patterns

The Evolution Investigation

Purpose: Track how things change over time

Examples:

  • "How has customer behavior evolved?"
  • "How are our sales patterns changing?"
  • "Show me pipeline evolution over time"
  • "Track how usage patterns have shifted"

Power: Uses snapshot data to show progression, not just current state

The Prediction Investigation

Purpose: Anticipate future outcomes

Examples:

  • "Predict which customers might churn"
  • "What will revenue look like next quarter?"
  • "Which deals are likely to close?"
  • "Forecast demand by product"

Power: ML models trained on your specific patterns

The Segmentation Investigation

Purpose: Find natural groupings in your data

Examples:

  • "Segment my customers"
  • "Group products by performance patterns"
  • "Cluster sales territories"
  • "Find user behavior segments"

Power: Discovers segments you didn't know existed

The Diagnostic Investigation

Purpose: Diagnose complex business issues

Examples:

  • "Diagnose pipeline health"
  • "Why is growth slowing?"
  • "What's wrong with our conversion funnel?"
  • "Diagnose customer satisfaction issues"

Power: Systematic investigation across all factors

Industry-Specific Investigation Starters

SaaS / Technology

  • "Why is MRR growth slowing?"
  • "Analyze churn patterns by cohort"
  • "What drives expansion revenue?"
  • "Find early indicators of account risk"
  • "Compare feature adoption across segments"

E-Commerce / Retail

  • "Why did cart abandonment increase?"
  • "Analyze customer lifetime value patterns"
  • "What drives repeat purchase behavior?"
  • "Find seasonal patterns I should plan for"
  • "Compare channel performance deeply"

Sales Organizations

  • "Why are sales cycles lengthening?"
  • "Analyze win/loss patterns"
  • "What differentiates top performers?"
  • "Find bottlenecks in our sales process"
  • "Predict which deals will close"

Marketing Teams

  • "Why did CAC increase?"
  • "Analyze campaign performance patterns"
  • "What drives conversion at each funnel stage?"
  • "Find the optimal channel mix"
  • "Compare audience segment behavior"

Financial Analysis

  • "Why are margins shrinking?"
  • "Analyze expense patterns"
  • "What drives profitability by segment?"
  • "Find cost optimization opportunities"
  • "Compare budget vs actual deeply"

The Power of Vague Questions

Don't Be Too Specific!

Scoop's AI thrives on investigative freedom.

Too Specific: "Show me revenue by product by month for Q1" Better: "Why did Q1 revenue disappoint?"

Too Specific: "List customers who churned last month" Better: "Analyze recent churn patterns"

Too Specific: "Calculate average deal size by rep" Better: "What drives sales performance?"

Conversation Patterns

The Progressive Investigation

Start broad, then narrow based on discoveries:

  1. "Analyze my business performance"
  2. (Scoop finds issue in customer segment)
  3. "Why is that segment struggling?"
  4. (Scoop finds product-related cause)
  5. "What should we do about it?"

The Multi-Angle Investigation

Approach problems from different angles:

  1. "Why are we losing customers?"
  2. "What do churned customers have in common?"
  3. "Compare churned vs retained customers"
  4. "Predict who might churn next"
  5. "What interventions would help most?"

The Time-Based Investigation

Understand evolution and trends:

  1. "How has our business changed over time?"
  2. "What's different about recent months?"
  3. "When did these changes start?"
  4. "What coincided with these changes?"
  5. "Project these trends forward"

Power User Secrets

1. Let Scoop Surprise You

  • "What's interesting in my data?"
  • "What should I know about my business?"
  • "Find something surprising"
  • "What am I missing?"

2. Use Business Language

  • Not: "SELECT SUM(revenue) GROUP BY..."
  • But: "How's business doing?"
  • Not: "Join customers with orders where..."
  • But: "Tell me about customer behavior"

3. Ask About Problems, Not Metrics

  • Not: "Show me the churn rate"
  • But: "Why are we losing customers?"
  • Not: "Calculate revenue by segment"
  • But: "Which segments drive growth?"

4. Trust the Investigation Process

  • Don't pre-filter or constrain
  • Let Scoop determine what's relevant
  • Ask follow-ups on discoveries
  • Build on what you learn

Common Mistakes to Avoid

❌ Thinking Like SQL

"SELECT * FROM customers WHERE..." → "Analyze my customer patterns"

❌ Being Too Prescriptive

"Show me exactly X grouped by Y filtered by Z" → "Help me understand what's happening with [area]"

❌ Asking for Single Metrics

"What's the churn rate?" → "Why are customers leaving?"

❌ Ignoring Suggestions

Scoop suggests follow-ups for a reason - they lead to insights

Your First Week of Investigations

Day 1: Start with Why

Pick your biggest current concern and ask why it's happening

Day 2: Analyze Something

Choose a business area and let Scoop analyze it completely

Day 3: Find Drivers

Ask what drives your most important metric

Day 4: Compare Periods

Have Scoop compare your recent performance to historical

Day 5: Discover Segments

Let Scoop find hidden segments in your customers/products/data

Day 6: Predict Something

Ask Scoop to predict an important future outcome

Day 7: Get Strategic

Ask: "What should I focus on to improve performance?"

The Bottom Line

Stop thinking about queries. Start thinking about questions that matter to your business. Scoop isn't waiting for perfect syntax - it's waiting to investigate your most important business challenges.

Your AI analyst is ready. What mystery should it solve first?

[Start Your Investigation] | [Watch Investigation Demo]

Updated about 24 hours ago