| name | pattern-recognition |
| description | Master pattern recognition - identify recurring themes, outliers, trends, inconsistencies across documents and data. Use for extracting meaningful patterns from interviews, feedback, and data sets. |
| allowed-tools | Read, Grep |
| metadata | {"version":"2.0","claude-code":{"context":"fork","agent":"Explore","user-invocable":true}} |
Pattern Recognition for BA
What do the numbers show? What's really happening?
What is Pattern Recognition?
Definition: Identifying recurring themes, anomalies, and trends across data sets, interviews, or documents - finding the signal in noise.
Why it matters:
- Hidden patterns: One-off observation vs actual trend
- Outlier detection: Which observations are unusual/important?
- Data-driven insights: "Many users said it" vs "A few users said it"
- Contradiction detection: "Users love feature X" but "Nobody uses feature X"
- Trend identification: Direction of change (improving/declining)
When to Use:
- ✅ Analyzing 5-8 user interviews (find common themes)
- ✅ Reviewing customer feedback (what's most mentioned?)
- ✅ Analyzing data sets (what's the trend?)
- ✅ Finding inconsistencies (why do findings conflict?)
- ✅ Identifying root causes (what's the common factor?)
3 Pattern Types
Type 1: Frequency Patterns (The 60% Rule)
What appeared in 60%+ of observations?
Interview Finding: "Users find setup confusing"
Count appearances:
Interview 1: User said "setup confusing" ✓
Interview 2: User said "takes too long" ✓
Interview 3: User said "confusing" ✓
Interview 4: User completed easily ✗
Interview 5: User said "confusing" ✓
Interview 6: User said "unclear" ✓
Interview 7: User completed in 5 min ✗
Interview 8: User said "complex" ✓
Count: 6 out of 8 mentioned confusion = 75%
→ PATTERN FOUND (60%+ threshold)
Conclusion: Setup confusion is widespread issue,
not just one user's problem
Type 2: Contradiction Patterns
What conflicts with other observations?
Observation 1: "Users love the dashboard feature"
- Quote 1: "Dashboard gives me visibility I need"
- Quote 2: "Dashboard is my favorite feature"
But Data Shows: Dashboard usage is only 15% of users
80% never open dashboard
CONTRADICTION FOUND!
→ Investigation: Maybe users love it when they use it,
but most don't discover it or use it
(awareness/discoverability problem, not feature problem)
Type 3: Outlier Patterns
What's unusual or stands out?
Sales Data:
- Sales rep A: $500K revenue (typical)
- Sales rep B: $520K revenue (typical)
- Sales rep C: $850K revenue (outlier - 60% higher!)
- Sales rep D: $480K revenue (typical)
Question: Why is Sales rep C so much higher?
Investigation:
→ Rep C focuses on Enterprise segment (higher deal size)
→ Other reps focus on SMB (smaller deals)
Insight: Enterprise segment is 60% higher value
→ Strategy implication: Shift towards Enterprise focus?
5 Pattern Recognition Techniques
1. Frequency Analysis
Count how many times something appears.
Process:
1. Review all interviews/data
2. Mark each instance of observation
3. Count total instances
4. Divide by total observations
5. Calculate percentage
Rules:
- Pattern threshold: 60%+ = significant pattern
- 30-60% = interesting trend, but not consensus
- <30% = minority view, document but don't prioritize
Example:
Pain point: "No real-time collaboration"
User 1: Mentioned ✓
User 2: Mentioned ✓
User 3: Mentioned ✓
User 4: Not mentioned ✗
User 5: Mentioned ✓
User 6: Mentioned ✓
5 of 6 mentioned = 83% → STRONG PATTERN
2. Segmentation Analysis
Does pattern appear in specific segments?
Finding: "Users frustrated with performance"
Segment analysis:
- Enterprise users: 80% frustrated with performance
- Mid-market users: 40% frustrated with performance
- SMB users: 20% frustrated with performance
Insight: Performance is enterprise problem primarily
Not universal issue, segment-specific
→ Design: Enterprise scalability first, SMB later
3. Timeline Analysis
Does pattern change over time?
User churn by cohort:
Cohort A (Jan 2026): 5% monthly churn
Cohort B (Feb 2026): 6% monthly churn
Cohort C (Mar 2026): 12% monthly churn
Cohort D (Apr 2026): 15% monthly churn
Trend: Churn increasing for newer cohorts
→ Insight: Something is getting worse (product, onboarding, market fit)
→ Action: Investigate what changed
4. Correlation Analysis
Do two things happen together?
Observation: Users with low onboarding completion have high churn
Data:
Users completing <3 steps: 70% churn
Users completing 3-5 steps: 40% churn
Users completing all steps: 5% churn
Correlation Found: Onboarding completion → Churn reduction
→ Insight: Completing onboarding indicates product fit
→ Action: Make onboarding better to improve retention
5. Root Cause Analysis via Patterns
Use patterns to trace to cause.
Pattern: "Users abandon at step 3"
Investigation:
Interview review: 6 of 8 users mentioned "password step"
→ Deeper: When asked why, got:
- "Requirements unclear"
- "Password validation error"
- "Confusing message"
Pattern: All mention password step
All have confusion/clarity issues
→ ROOT CAUSE: Password UX is broken
→ Solution: Redesign password step for clarity
Common Pattern Analysis Mistakes
❌ Small Sample Size
Problem: "All 2 users said feature X is broken"
Issue: 2 users is too small, could be coincidence
Fix: Need 5-8 observations minimum to establish pattern
❌ Confirmation Bias
Problem: "I expected users to want feature A, so I count evidence
supporting A and ignore evidence for B"
Fix: Count ALL observations objectively, not just ones you expected
❌ Selection Bias
Problem: "Interviewed only early adopters, they love feature"
Issue: Early adopters ≠ typical users, they have different needs
Fix: Interview diverse user segments
❌ Mixing Unrelated Observations
Problem: "Users mentioned: slow performance, confusing UI, needs
integration, wants mobile. These are 4 separate issues."
Issue: Looking like one big pattern when they're separate
Fix: Categorize observations into distinct patterns
Pattern Recognition Workflow
Step 1: Prepare Data
Step 2: Code Observations
Step 3: Tabulate
Step 4: Investigate
Step 5: Document
Tools & Templates
- 📋 Pattern Analysis Template:
assets/pattern-analysis-template.md
- 📋 Frequency Counting Worksheet:
assets/frequency-template.md
- 📋 Coding Framework:
assets/coding-framework-template.md
- 📋 Trend Analysis Template:
assets/trend-analysis-template.md
- 📋 Contradiction Assessment:
assets/contradiction-template.md
Usage Examples
/pattern-recognition "interview-transcripts" "Common pain points?"
/pattern-recognition "customer-feedback-data" "Recurring complaints?"
/pattern-recognition "usage-metrics" "What features get used most?"
BA Standards & References
Based on:
- Qualitative Analysis: Coding and theme identification
- Quantitative Analysis: Frequency and correlation
- Data Science: Pattern detection, outlier analysis
- User Research: Thematic analysis methodology
Detailed Frameworks:
- 📚
references/frequency-analysis.md - Counting and the 60% rule
- 📚
references/coding-framework.md - How to code observations
- 📚
references/theme-identification.md - Finding main themes
- 📚
references/outlier-detection.md - Finding and investigating anomalies
- 📚
references/correlation-causation.md - When patterns are related vs coincidence
