Insight Synthesis

Quick Start

Convert raw analysis findings into clear, actionable insights that drive business decisions by connecting data patterns to business impact.

Context Requirements

1. Analysis Findings: Raw results, statistics, patterns discovered
2. Business Context: What the business cares about, current priorities
3. Audience: Who needs these insights and what they'll do with them
4. Decision Framework: What decisions these insights should inform
5. Constraints: Limitations, caveats, confidence levels

Context Gathering

For Findings:

"Share your analysis results:

• Key statistics and metrics
• Patterns or trends found
• Correlations discovered
• Anomalies identified
• Hypothesis test results

Example: 'Churn rate increased 15% in Q4. Mobile users churning 2x more than desktop. Correlation with recent app update.'"

For Business Context:

"Help me understand business priorities:

• Current goals/OKRs
• Pain points being addressed
• Strategic initiatives
• Budget/resources available
• Timeline for action

This helps frame insights as actionable, not just interesting."

For Audience:

"Who will act on these insights?

• Executives: High-level strategic implications
• Product team: Feature/UX implications
• Marketing: Campaign/targeting implications
• Operations: Process improvement implications

Different audiences need different framing."

Workflow

Step 1: Structure Findings

import pandas as pd
import numpy as np

class InsightSynthesizer:
    def __init__(self, analysis_name):
        self.analysis_name = analysis_name
        self.findings = []
        self.insights = []
    
    def add_finding(self, finding_type, metric, value, context):
        """Log an analysis finding"""
        self.findings.append({
            'type': finding_type,  # trend, comparison, correlation, anomaly
            'metric': metric,
            'value': value,
            'context': context,
            'business_impact': None,
            'recommendation': None
        })
    
    def convert_to_insight(self, finding_idx, impact, recommendation, confidence):
        """Convert finding to actionable insight"""
        finding = self.findings[finding_idx]
        
        insight = {
            'finding': finding,
            'impact': impact,
            'recommendation': recommendation,
            'confidence': confidence,
            'priority': self._assess_priority(impact, confidence)
        }
        
        self.insights.append(insight)
        return insight
    
    def _assess_priority(self, impact, confidence):
        """Determine insight priority"""
        if 'high' in impact.lower() and confidence == 'high':
            return 'critical'
        elif 'high' in impact.lower() or confidence == 'high':
            return 'high'
        elif 'medium' in impact.lower():
            return 'medium'
        else:
            return 'low'

# Initialize
synthesizer = InsightSynthesizer("Q4 Churn Analysis")

# Add findings
synthesizer.add_finding(
    finding_type='trend',
    metric='Churn Rate',
    value='15% increase in Q4',
    context='Previous quarter was 8%, now 23%'
)

synthesizer.add_finding(
    finding_type='comparison',
    metric='Mobile vs Desktop Churn',
    value='2x higher on mobile',
    context='Mobile: 35% churn, Desktop: 17% churn'
)

print("✅ Findings structured")

Step 2: Connect to Business Impact

# Convert finding to insight with business context

insight_1 = synthesizer.convert_to_insight(
    finding_idx=0,
    impact="High revenue risk: 15% churn increase = $2M ARR at risk. Accelerating trend suggests worsening if unaddressed.",
    recommendation="Immediate win-back campaign for at-risk customers. Investigate root cause (recent product changes, competitor moves).",
    confidence='high'
)

insight_2 = synthesizer.convert_to_insight(
    finding_idx=1,
    impact="Mobile represents 40% of user base. 2x churn rate means disproportionate loss of mobile users. Mobile-first strategy at risk.",
    recommendation="Audit mobile app experience. Recent app update (v3.2.0) correlated with spike - consider rollback or hotfix.",
    confidence='high'
)

print(f"✅ Created {len(synthesizer.insights)} actionable insights")

Step 3: Apply Insight Framework

def apply_insight_framework(finding, business_context):
    """
    Use So What? - Why? - Now What? framework
    """
    
    insight = {}
    
    # SO WHAT? (Why it matters)
    insight['so_what'] = f"This means {finding['value']} which affects {business_context['impact_area']}"
    
    # WHY? (Root cause hypothesis)
    insight['why'] = f"Likely driven by {business_context['hypothesis']}"
    
    # NOW WHAT? (Action)
    insight['now_what'] = f"We should {business_context['action']}"
    
    # Expected outcome
    insight['expected_outcome'] = business_context.get('expected_outcome')
    
    return insight

# Example
finding = synthesizer.findings[0]
context = {
    'impact_area': 'monthly recurring revenue and growth targets',
    'hypothesis': 'recent app update degrading mobile UX, competitor launched similar product',
    'action': 'immediately investigate app update, run win-back campaign, monitor competitor',
    'expected_outcome': 'Reduce churn to <10% within 60 days, recover $500K ARR'
}

structured_insight = apply_insight_framework(finding, context)

print("\n📊 Structured Insight:")
print(f"  SO WHAT: {structured_insight['so_what']}")
print(f"  WHY: {structured_insight['why']}")
print(f"  NOW WHAT: {structured_insight['now_what']}")

Step 4: Prioritize Insights

def prioritize_insights(insights):
    """Rank insights by impact and urgency"""
    
    priority_map = {'critical': 4, 'high': 3, 'medium': 2, 'low': 1}
    
    # Sort by priority
    sorted_insights = sorted(
        insights,
        key=lambda x: priority_map[x['priority']],
        reverse=True
    )
    
    print("\n🎯 Prioritized Insights:\n")
    for i, insight in enumerate(sorted_insights, 1):
        print(f"{i}. [{insight['priority'].upper()}] {insight['finding']['metric']}")
        print(f"   Impact: {insight['impact'][:80]}...")
        print(f"   Action: {insight['recommendation'][:80]}...")
        print()
    
    return sorted_insights

prioritized = prioritize_insights(synthesizer.insights)

Step 5: Generate Executive Summary

def generate_executive_summary(insights, analysis_name):
    """Create concise executive summary"""
    
    summary = f"# Executive Summary: {analysis_name}\n\n"
    
    # Top 3 insights
    summary += "## Key Insights\n\n"
    for i, insight in enumerate(insights[:3], 1):
        summary += f"**{i}. {insight['finding']['metric']}**\n"
        summary += f"- Finding: {insight['finding']['value']}\n"
        summary += f"- Impact: {insight['impact']}\n"
        summary += f"- Action: {insight['recommendation']}\n\n"
    
    # Bottom line
    summary += "## Bottom Line\n\n"
    summary += f"Immediate action required on {len([i for i in insights if i['priority'] in ['critical', 'high']])} high-priority issues. "
    summary += f"Estimated business impact: $2M+ ARR at risk if unaddressed.\n"
    
    return summary

exec_summary = generate_executive_summary(prioritized, "Q4 Churn Analysis")
print(exec_summary)

Context Validation

• Findings are factually accurate
• Business impact is realistic
• Recommendations are actionable
• Confidence levels are honest
• Audience needs considered

Output Template

# Key Insights: Q4 Churn Analysis

## Critical Insight #1: Mobile Churn Crisis

**Finding:** Mobile users churning at 2x rate of desktop (35% vs 17%)

**So What:**
- Mobile = 40% of user base, disproportionate loss
- Threatens mobile-first strategy
- Concentrated in recent cohorts (post-app update)

**Why:**
- App update v3.2.0 introduced performance issues
- Competitors launched improved mobile apps
- Mobile onboarding less effective

**Now What:**
1. IMMEDIATE: Rollback app to v3.1.9 (stable version)
2. Week 1: Emergency UX audit of mobile experience
3. Week 2: Launch mobile win-back campaign
4. Month 1: Redesign mobile onboarding

**Expected Outcome:**
Reduce mobile churn to <20% within 60 days, save $500K ARR

**Confidence:** HIGH (validated with user feedback, correlates with app release)

---

## High Priority Insight #2: Churn Accelerating

**Finding:** 15% increase in churn (8% → 23% in one quarter)

**Impact:** $2M ARR at risk, undermines growth targets

**Action:** Root cause investigation + retention programs

**Confidence:** HIGH

Common Scenarios

Scenario 1: "Turn analysis into exec presentation"

→ Extract top 3-5 insights → Frame with business impact → Lead with recommendations → Support with data → Include next steps

Scenario 2: "Why should stakeholders care?"

→ Connect to OKRs/goals → Quantify business impact ($, customers, time) → Show urgency (what happens if we don't act) → Make recommendations concrete → Provide confidence levels

Scenario 3: "Too many findings to present"

→ Prioritize by impact → Group related findings → Focus on actionable insights → Save rest for appendix → Tailor to audience

Scenario 4: "Findings conflict or unclear"

→ Acknowledge uncertainty → Present alternative interpretations → Recommend validation approach → Provide provisional insights → Set up next analysis

Scenario 5: "Create insight library"

→ Document insight patterns → Template common insight types → Build metrics → insights mapping → Enable self-service insights → Track insight outcomes

Handling Missing Context

Just raw numbers, no context: "I can see the data changed, but need business context:

• Why do we care about this metric?
• What's an acceptable range?
• What can we actually do about it? Let's connect findings to business impact."

Unclear what action to take: "Let's work backwards:

• Who has the power to act?
• What levers can they pull?
• What's a realistic timeline? Then frame insights as actionable recommendations."

Low confidence in findings: "Be transparent about uncertainty:

• State what we know vs. suspect
• Provide confidence levels
• Recommend validation
• Avoid overconfident claims"

Advanced Options

Insight Scoring Model: Weight insights by impact × confidence × actionability

Insight Templates: Pre-built frameworks for common analysis types

Automated Insights: Scan for statistical significance, anomalies, patterns

Insight Tracking: Monitor which insights led to action and outcomes

Competitive Insights: Compare to industry benchmarks, competitors