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feat: add 5 new specialized agents with 20 skills
Add domain expert agents with comprehensive skill sets: - service-mesh-expert (cloud-infrastructure): Istio/Linkerd patterns, mTLS, observability - event-sourcing-architect (backend-development): CQRS, event stores, projections, sagas - vector-database-engineer (llm-application-dev): embeddings, similarity search, hybrid search - monorepo-architect (developer-essentials): Nx, Turborepo, Bazel, pnpm workspaces - threat-modeling-expert (security-scanning): STRIDE, attack trees, security requirements Update all documentation to reflect correct counts: - 67 plugins, 99 agents, 107 skills, 71 commands
This commit is contained in:
Seth Hobson
423
plugins/business-analytics/skills/data-storytelling/SKILL.md
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423
plugins/business-analytics/skills/data-storytelling/SKILL.md
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---
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name: data-storytelling
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description: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
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---
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# Data Storytelling
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Transform raw data into compelling narratives that drive decisions and inspire action.
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## When to Use This Skill
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- Presenting analytics to executives
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- Creating quarterly business reviews
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- Building investor presentations
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- Writing data-driven reports
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- Communicating insights to non-technical audiences
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- Making recommendations based on data
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## Core Concepts
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### 1. Story Structure
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```
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Setup → Conflict → Resolution
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Setup: Context and baseline
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Conflict: The problem or opportunity
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Resolution: Insights and recommendations
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```
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### 2. Narrative Arc
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```
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1. Hook: Grab attention with surprising insight
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2. Context: Establish the baseline
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3. Rising Action: Build through data points
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4. Climax: The key insight
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5. Resolution: Recommendations
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6. Call to Action: Next steps
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```
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### 3. Three Pillars
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| Pillar | Purpose | Components |
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|--------|---------|------------|
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| **Data** | Evidence | Numbers, trends, comparisons |
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| **Narrative** | Meaning | Context, causation, implications |
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| **Visuals** | Clarity | Charts, diagrams, highlights |
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## Story Frameworks
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### Framework 1: The Problem-Solution Story
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```markdown
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# Customer Churn Analysis
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## The Hook
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"We're losing $2.4M annually to preventable churn."
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## The Context
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- Current churn rate: 8.5% (industry average: 5%)
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- Average customer lifetime value: $4,800
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- 500 customers churned last quarter
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## The Problem
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Analysis of churned customers reveals a pattern:
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- 73% churned within first 90 days
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- Common factor: < 3 support interactions
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- Low feature adoption in first month
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## The Insight
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[Show engagement curve visualization]
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Customers who don't engage in the first 14 days
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are 4x more likely to churn.
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## The Solution
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1. Implement 14-day onboarding sequence
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2. Proactive outreach at day 7
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3. Feature adoption tracking
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## Expected Impact
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- Reduce early churn by 40%
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- Save $960K annually
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- Payback period: 3 months
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## Call to Action
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Approve $50K budget for onboarding automation.
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```
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### Framework 2: The Trend Story
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```markdown
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# Q4 Performance Analysis
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## Where We Started
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Q3 ended with $1.2M MRR, 15% below target.
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Team morale was low after missed goals.
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## What Changed
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[Timeline visualization]
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- Oct: Launched self-serve pricing
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- Nov: Reduced friction in signup
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- Dec: Added customer success calls
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## The Transformation
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[Before/after comparison chart]
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| Metric | Q3 | Q4 | Change |
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|----------------|--------|--------|--------|
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| Trial → Paid | 8% | 15% | +87% |
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| Time to Value | 14 days| 5 days | -64% |
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| Expansion Rate | 2% | 8% | +300% |
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## Key Insight
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Self-serve + high-touch creates compound growth.
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Customers who self-serve AND get a success call
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have 3x higher expansion rate.
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## Going Forward
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Double down on hybrid model.
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Target: $1.8M MRR by Q2.
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```
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### Framework 3: The Comparison Story
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```markdown
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# Market Opportunity Analysis
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## The Question
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Should we expand into EMEA or APAC first?
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## The Comparison
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[Side-by-side market analysis]
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### EMEA
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- Market size: $4.2B
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- Growth rate: 8%
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- Competition: High
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- Regulatory: Complex (GDPR)
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- Language: Multiple
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### APAC
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- Market size: $3.8B
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- Growth rate: 15%
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- Competition: Moderate
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- Regulatory: Varied
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- Language: Multiple
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## The Analysis
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[Weighted scoring matrix visualization]
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| Factor | Weight | EMEA Score | APAC Score |
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|-------------|--------|------------|------------|
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| Market Size | 25% | 5 | 4 |
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| Growth | 30% | 3 | 5 |
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| Competition | 20% | 2 | 4 |
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| Ease | 25% | 2 | 3 |
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| **Total** | | **2.9** | **4.1** |
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## The Recommendation
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APAC first. Higher growth, less competition.
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Start with Singapore hub (English, business-friendly).
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Enter EMEA in Year 2 with localization ready.
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## Risk Mitigation
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- Timezone coverage: Hire 24/7 support
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- Cultural fit: Local partnerships
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- Payment: Multi-currency from day 1
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```
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## Visualization Techniques
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### Technique 1: Progressive Reveal
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```markdown
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Start simple, add layers:
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Slide 1: "Revenue is growing" [single line chart]
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Slide 2: "But growth is slowing" [add growth rate overlay]
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Slide 3: "Driven by one segment" [add segment breakdown]
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Slide 4: "Which is saturating" [add market share]
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Slide 5: "We need new segments" [add opportunity zones]
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```
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### Technique 2: Contrast and Compare
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```markdown
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Before/After:
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┌─────────────────┬─────────────────┐
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│ BEFORE │ AFTER │
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│ │ │
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│ Process: 5 days│ Process: 1 day │
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│ Errors: 15% │ Errors: 2% │
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│ Cost: $50/unit │ Cost: $20/unit │
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└─────────────────┴─────────────────┘
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This/That (emphasize difference):
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┌─────────────────────────────────────┐
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│ CUSTOMER A vs B │
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│ ┌──────────┐ ┌──────────┐ │
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│ │ ████████ │ │ ██ │ │
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│ │ $45,000 │ │ $8,000 │ │
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│ │ LTV │ │ LTV │ │
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│ └──────────┘ └──────────┘ │
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│ Onboarded No onboarding │
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└─────────────────────────────────────┘
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```
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### Technique 3: Annotation and Highlight
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```python
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import matplotlib.pyplot as plt
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import pandas as pd
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fig, ax = plt.subplots(figsize=(12, 6))
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# Plot the main data
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ax.plot(dates, revenue, linewidth=2, color='#2E86AB')
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# Add annotation for key events
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ax.annotate(
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'Product Launch\n+32% spike',
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xy=(launch_date, launch_revenue),
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xytext=(launch_date, launch_revenue * 1.2),
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fontsize=10,
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arrowprops=dict(arrowstyle='->', color='#E63946'),
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color='#E63946'
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)
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# Highlight a region
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ax.axvspan(growth_start, growth_end, alpha=0.2, color='green',
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label='Growth Period')
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# Add threshold line
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ax.axhline(y=target, color='gray', linestyle='--',
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label=f'Target: ${target:,.0f}')
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ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold')
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ax.legend()
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```
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## Presentation Templates
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### Template 1: Executive Summary Slide
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```
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┌─────────────────────────────────────────────────────────────┐
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│ KEY INSIGHT │
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│ ══════════════════════════════════════════════════════════│
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│ │
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│ "Customers who complete onboarding in week 1 │
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│ have 3x higher lifetime value" │
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│ │
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├──────────────────────┬──────────────────────────────────────┤
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│ │ │
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│ THE DATA │ THE IMPLICATION │
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│ │ │
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│ Week 1 completers: │ ✓ Prioritize onboarding UX │
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│ • LTV: $4,500 │ ✓ Add day-1 success milestones │
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│ • Retention: 85% │ ✓ Proactive week-1 outreach │
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│ • NPS: 72 │ │
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│ │ Investment: $75K │
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│ Others: │ Expected ROI: 8x │
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│ • LTV: $1,500 │ │
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│ • Retention: 45% │ │
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│ • NPS: 34 │ │
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│ │ │
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└──────────────────────┴──────────────────────────────────────┘
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```
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### Template 2: Data Story Flow
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```
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Slide 1: THE HEADLINE
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"We can grow 40% faster by fixing onboarding"
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Slide 2: THE CONTEXT
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Current state metrics
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Industry benchmarks
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Gap analysis
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Slide 3: THE DISCOVERY
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What the data revealed
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Surprising finding
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Pattern identification
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Slide 4: THE DEEP DIVE
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Root cause analysis
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Segment breakdowns
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Statistical significance
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Slide 5: THE RECOMMENDATION
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Proposed actions
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Resource requirements
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Timeline
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Slide 6: THE IMPACT
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Expected outcomes
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ROI calculation
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Risk assessment
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Slide 7: THE ASK
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Specific request
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Decision needed
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Next steps
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```
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### Template 3: One-Page Dashboard Story
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```markdown
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# Monthly Business Review: January 2024
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## THE HEADLINE
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Revenue up 15% but CAC increasing faster than LTV
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## KEY METRICS AT A GLANCE
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┌────────┬────────┬────────┬────────┐
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│ MRR │ NRR │ CAC │ LTV │
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│ $125K │ 108% │ $450 │ $2,200 │
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│ ▲15% │ ▲3% │ ▲22% │ ▲8% │
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└────────┴────────┴────────┴────────┘
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## WHAT'S WORKING
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✓ Enterprise segment growing 25% MoM
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✓ Referral program driving 30% of new logos
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✓ Support satisfaction at all-time high (94%)
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## WHAT NEEDS ATTENTION
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✗ SMB acquisition cost up 40%
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✗ Trial conversion down 5 points
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✗ Time-to-value increased by 3 days
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## ROOT CAUSE
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[Mini chart showing SMB vs Enterprise CAC trend]
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SMB paid ads becoming less efficient.
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CPC up 35% while conversion flat.
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## RECOMMENDATION
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1. Shift $20K/mo from paid to content
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2. Launch SMB self-serve trial
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3. A/B test shorter onboarding
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## NEXT MONTH'S FOCUS
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- Launch content marketing pilot
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- Complete self-serve MVP
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- Reduce time-to-value to < 7 days
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```
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## Writing Techniques
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### Headlines That Work
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```markdown
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BAD: "Q4 Sales Analysis"
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GOOD: "Q4 Sales Beat Target by 23% - Here's Why"
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BAD: "Customer Churn Report"
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GOOD: "We're Losing $2.4M to Preventable Churn"
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BAD: "Marketing Performance"
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GOOD: "Content Marketing Delivers 4x ROI vs. Paid"
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Formula:
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[Specific Number] + [Business Impact] + [Actionable Context]
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```
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### Transition Phrases
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```markdown
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Building the narrative:
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• "This leads us to ask..."
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• "When we dig deeper..."
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• "The pattern becomes clear when..."
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• "Contrast this with..."
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Introducing insights:
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• "The data reveals..."
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• "What surprised us was..."
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• "The inflection point came when..."
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• "The key finding is..."
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Moving to action:
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• "This insight suggests..."
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• "Based on this analysis..."
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• "The implication is clear..."
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• "Our recommendation is..."
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```
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### Handling Uncertainty
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```markdown
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Acknowledge limitations:
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• "With 95% confidence, we can say..."
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• "The sample size of 500 shows..."
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• "While correlation is strong, causation requires..."
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• "This trend holds for [segment], though [caveat]..."
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Present ranges:
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• "Impact estimate: $400K-$600K"
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• "Confidence interval: 15-20% improvement"
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• "Best case: X, Conservative: Y"
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```
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## Best Practices
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### Do's
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- **Start with the "so what"** - Lead with insight
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- **Use the rule of three** - Three points, three comparisons
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- **Show, don't tell** - Let data speak
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- **Make it personal** - Connect to audience goals
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- **End with action** - Clear next steps
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### Don'ts
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- **Don't data dump** - Curate ruthlessly
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- **Don't bury the insight** - Front-load key findings
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- **Don't use jargon** - Match audience vocabulary
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- **Don't show methodology first** - Context, then method
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- **Don't forget the narrative** - Numbers need meaning
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## Resources
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- [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/)
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- [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516)
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- [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)
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426
plugins/business-analytics/skills/kpi-dashboard-design/SKILL.md
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426
plugins/business-analytics/skills/kpi-dashboard-design/SKILL.md
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---
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name: kpi-dashboard-design
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description: Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
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---
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# KPI Dashboard Design
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Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.
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## When to Use This Skill
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|
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- Designing executive dashboards
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- Selecting meaningful KPIs
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- Building real-time monitoring displays
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- Creating department-specific metrics views
|
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- Improving existing dashboard layouts
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- Establishing metric governance
|
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|
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## Core Concepts
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|
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### 1. KPI Framework
|
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| Level | Focus | Update Frequency | Audience |
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|-------|-------|------------------|----------|
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| **Strategic** | Long-term goals | Monthly/Quarterly | Executives |
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| **Tactical** | Department goals | Weekly/Monthly | Managers |
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| **Operational** | Day-to-day | Real-time/Daily | Teams |
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### 2. SMART KPIs
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|
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```
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Specific: Clear definition
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||||
Measurable: Quantifiable
|
||||
Achievable: Realistic targets
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||||
Relevant: Aligned to goals
|
||||
Time-bound: Defined period
|
||||
```
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||||
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||||
### 3. Dashboard Hierarchy
|
||||
|
||||
```
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├── Executive Summary (1 page)
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||||
│ ├── 4-6 headline KPIs
|
||||
│ ├── Trend indicators
|
||||
│ └── Key alerts
|
||||
├── Department Views
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||||
│ ├── Sales Dashboard
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||||
│ ├── Marketing Dashboard
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│ ├── Operations Dashboard
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│ └── Finance Dashboard
|
||||
└── Detailed Drilldowns
|
||||
├── Individual metrics
|
||||
└── Root cause analysis
|
||||
```
|
||||
|
||||
## Common KPIs by Department
|
||||
|
||||
### Sales KPIs
|
||||
|
||||
```yaml
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||||
Revenue Metrics:
|
||||
- Monthly Recurring Revenue (MRR)
|
||||
- Annual Recurring Revenue (ARR)
|
||||
- Average Revenue Per User (ARPU)
|
||||
- Revenue Growth Rate
|
||||
|
||||
Pipeline Metrics:
|
||||
- Sales Pipeline Value
|
||||
- Win Rate
|
||||
- Average Deal Size
|
||||
- Sales Cycle Length
|
||||
|
||||
Activity Metrics:
|
||||
- Calls/Emails per Rep
|
||||
- Demos Scheduled
|
||||
- Proposals Sent
|
||||
- Close Rate
|
||||
```
|
||||
|
||||
### Marketing KPIs
|
||||
|
||||
```yaml
|
||||
Acquisition:
|
||||
- Cost Per Acquisition (CPA)
|
||||
- Customer Acquisition Cost (CAC)
|
||||
- Lead Volume
|
||||
- Marketing Qualified Leads (MQL)
|
||||
|
||||
Engagement:
|
||||
- Website Traffic
|
||||
- Conversion Rate
|
||||
- Email Open/Click Rate
|
||||
- Social Engagement
|
||||
|
||||
ROI:
|
||||
- Marketing ROI
|
||||
- Campaign Performance
|
||||
- Channel Attribution
|
||||
- CAC Payback Period
|
||||
```
|
||||
|
||||
### Product KPIs
|
||||
|
||||
```yaml
|
||||
Usage:
|
||||
- Daily/Monthly Active Users (DAU/MAU)
|
||||
- Session Duration
|
||||
- Feature Adoption Rate
|
||||
- Stickiness (DAU/MAU)
|
||||
|
||||
Quality:
|
||||
- Net Promoter Score (NPS)
|
||||
- Customer Satisfaction (CSAT)
|
||||
- Bug/Issue Count
|
||||
- Time to Resolution
|
||||
|
||||
Growth:
|
||||
- User Growth Rate
|
||||
- Activation Rate
|
||||
- Retention Rate
|
||||
- Churn Rate
|
||||
```
|
||||
|
||||
### Finance KPIs
|
||||
|
||||
```yaml
|
||||
Profitability:
|
||||
- Gross Margin
|
||||
- Net Profit Margin
|
||||
- EBITDA
|
||||
- Operating Margin
|
||||
|
||||
Liquidity:
|
||||
- Current Ratio
|
||||
- Quick Ratio
|
||||
- Cash Flow
|
||||
- Working Capital
|
||||
|
||||
Efficiency:
|
||||
- Revenue per Employee
|
||||
- Operating Expense Ratio
|
||||
- Days Sales Outstanding
|
||||
- Inventory Turnover
|
||||
```
|
||||
|
||||
## Dashboard Layout Patterns
|
||||
|
||||
### Pattern 1: Executive Summary
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ EXECUTIVE DASHBOARD [Date Range ▼] │
|
||||
├─────────────┬─────────────┬─────────────┬─────────────────┤
|
||||
│ REVENUE │ PROFIT │ CUSTOMERS │ NPS SCORE │
|
||||
│ $2.4M │ $450K │ 12,450 │ 72 │
|
||||
│ ▲ 12% │ ▲ 8% │ ▲ 15% │ ▲ 5pts │
|
||||
├─────────────┴─────────────┴─────────────┴─────────────────┤
|
||||
│ │
|
||||
│ Revenue Trend │ Revenue by Product │
|
||||
│ ┌───────────────────────┐ │ ┌──────────────────┐ │
|
||||
│ │ /\ /\ │ │ │ ████████ 45% │ │
|
||||
│ │ / \ / \ /\ │ │ │ ██████ 32% │ │
|
||||
│ │ / \/ \ / \ │ │ │ ████ 18% │ │
|
||||
│ │ / \/ \ │ │ │ ██ 5% │ │
|
||||
│ └───────────────────────┘ │ └──────────────────┘ │
|
||||
│ │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ 🔴 Alert: Churn rate exceeded threshold (>5%) │
|
||||
│ 🟡 Warning: Support ticket volume 20% above average │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Pattern 2: SaaS Metrics Dashboard
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ SAAS METRICS Jan 2024 [Monthly ▼] │
|
||||
├──────────────────────┬──────────────────────────────────────┤
|
||||
│ ┌────────────────┐ │ MRR GROWTH │
|
||||
│ │ MRR │ │ ┌────────────────────────────────┐ │
|
||||
│ │ $125,000 │ │ │ /── │ │
|
||||
│ │ ▲ 8% │ │ │ /────/ │ │
|
||||
│ └────────────────┘ │ │ /────/ │ │
|
||||
│ ┌────────────────┐ │ │ /────/ │ │
|
||||
│ │ ARR │ │ │ /────/ │ │
|
||||
│ │ $1,500,000 │ │ └────────────────────────────────┘ │
|
||||
│ │ ▲ 15% │ │ J F M A M J J A S O N D │
|
||||
│ └────────────────┘ │ │
|
||||
├──────────────────────┼──────────────────────────────────────┤
|
||||
│ UNIT ECONOMICS │ COHORT RETENTION │
|
||||
│ │ │
|
||||
│ CAC: $450 │ Month 1: ████████████████████ 100% │
|
||||
│ LTV: $2,700 │ Month 3: █████████████████ 85% │
|
||||
│ LTV/CAC: 6.0x │ Month 6: ████████████████ 80% │
|
||||
│ │ Month 12: ██████████████ 72% │
|
||||
│ Payback: 4 months │ │
|
||||
├──────────────────────┴──────────────────────────────────────┤
|
||||
│ CHURN ANALYSIS │
|
||||
│ ┌──────────┬──────────┬──────────┬──────────────────────┐ │
|
||||
│ │ Gross │ Net │ Logo │ Expansion │ │
|
||||
│ │ 4.2% │ 1.8% │ 3.1% │ 2.4% │ │
|
||||
│ └──────────┴──────────┴──────────┴──────────────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Pattern 3: Real-time Operations
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ OPERATIONS CENTER Live ● Last: 10:42:15 │
|
||||
├────────────────────────────┬────────────────────────────────┤
|
||||
│ SYSTEM HEALTH │ SERVICE STATUS │
|
||||
│ ┌──────────────────────┐ │ │
|
||||
│ │ CPU MEM DISK │ │ ● API Gateway Healthy │
|
||||
│ │ 45% 72% 58% │ │ ● User Service Healthy │
|
||||
│ │ ███ ████ ███ │ │ ● Payment Service Degraded │
|
||||
│ │ ███ ████ ███ │ │ ● Database Healthy │
|
||||
│ │ ███ ████ ███ │ │ ● Cache Healthy │
|
||||
│ └──────────────────────┘ │ │
|
||||
├────────────────────────────┼────────────────────────────────┤
|
||||
│ REQUEST THROUGHPUT │ ERROR RATE │
|
||||
│ ┌──────────────────────┐ │ ┌──────────────────────────┐ │
|
||||
│ │ ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅ │ │ │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ │ │
|
||||
│ └──────────────────────┘ │ └──────────────────────────┘ │
|
||||
│ Current: 12,450 req/s │ Current: 0.02% │
|
||||
│ Peak: 18,200 req/s │ Threshold: 1.0% │
|
||||
├────────────────────────────┴────────────────────────────────┤
|
||||
│ RECENT ALERTS │
|
||||
│ 10:40 🟡 High latency on payment-service (p99 > 500ms) │
|
||||
│ 10:35 🟢 Resolved: Database connection pool recovered │
|
||||
│ 10:22 🔴 Payment service circuit breaker tripped │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Implementation Patterns
|
||||
|
||||
### SQL for KPI Calculations
|
||||
|
||||
```sql
|
||||
-- Monthly Recurring Revenue (MRR)
|
||||
WITH mrr_calculation AS (
|
||||
SELECT
|
||||
DATE_TRUNC('month', billing_date) AS month,
|
||||
SUM(
|
||||
CASE subscription_interval
|
||||
WHEN 'monthly' THEN amount
|
||||
WHEN 'yearly' THEN amount / 12
|
||||
WHEN 'quarterly' THEN amount / 3
|
||||
END
|
||||
) AS mrr
|
||||
FROM subscriptions
|
||||
WHERE status = 'active'
|
||||
GROUP BY DATE_TRUNC('month', billing_date)
|
||||
)
|
||||
SELECT
|
||||
month,
|
||||
mrr,
|
||||
LAG(mrr) OVER (ORDER BY month) AS prev_mrr,
|
||||
(mrr - LAG(mrr) OVER (ORDER BY month)) / LAG(mrr) OVER (ORDER BY month) * 100 AS growth_pct
|
||||
FROM mrr_calculation;
|
||||
|
||||
-- Cohort Retention
|
||||
WITH cohorts AS (
|
||||
SELECT
|
||||
user_id,
|
||||
DATE_TRUNC('month', created_at) AS cohort_month
|
||||
FROM users
|
||||
),
|
||||
activity AS (
|
||||
SELECT
|
||||
user_id,
|
||||
DATE_TRUNC('month', event_date) AS activity_month
|
||||
FROM user_events
|
||||
WHERE event_type = 'active_session'
|
||||
)
|
||||
SELECT
|
||||
c.cohort_month,
|
||||
EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) AS months_since_signup,
|
||||
COUNT(DISTINCT a.user_id) AS active_users,
|
||||
COUNT(DISTINCT a.user_id)::FLOAT / COUNT(DISTINCT c.user_id) * 100 AS retention_rate
|
||||
FROM cohorts c
|
||||
LEFT JOIN activity a ON c.user_id = a.user_id
|
||||
AND a.activity_month >= c.cohort_month
|
||||
GROUP BY c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month))
|
||||
ORDER BY c.cohort_month, months_since_signup;
|
||||
|
||||
-- Customer Acquisition Cost (CAC)
|
||||
SELECT
|
||||
DATE_TRUNC('month', acquired_date) AS month,
|
||||
SUM(marketing_spend) / NULLIF(COUNT(new_customers), 0) AS cac,
|
||||
SUM(marketing_spend) AS total_spend,
|
||||
COUNT(new_customers) AS customers_acquired
|
||||
FROM (
|
||||
SELECT
|
||||
DATE_TRUNC('month', u.created_at) AS acquired_date,
|
||||
u.id AS new_customers,
|
||||
m.spend AS marketing_spend
|
||||
FROM users u
|
||||
JOIN marketing_spend m ON DATE_TRUNC('month', u.created_at) = m.month
|
||||
WHERE u.source = 'marketing'
|
||||
) acquisition
|
||||
GROUP BY DATE_TRUNC('month', acquired_date);
|
||||
```
|
||||
|
||||
### Python Dashboard Code (Streamlit)
|
||||
|
||||
```python
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
st.set_page_config(page_title="KPI Dashboard", layout="wide")
|
||||
|
||||
# Header with date filter
|
||||
col1, col2 = st.columns([3, 1])
|
||||
with col1:
|
||||
st.title("Executive Dashboard")
|
||||
with col2:
|
||||
date_range = st.selectbox(
|
||||
"Period",
|
||||
["Last 7 Days", "Last 30 Days", "Last Quarter", "YTD"]
|
||||
)
|
||||
|
||||
# KPI Cards
|
||||
def metric_card(label, value, delta, prefix="", suffix=""):
|
||||
delta_color = "green" if delta >= 0 else "red"
|
||||
delta_arrow = "▲" if delta >= 0 else "▼"
|
||||
st.metric(
|
||||
label=label,
|
||||
value=f"{prefix}{value:,.0f}{suffix}",
|
||||
delta=f"{delta_arrow} {abs(delta):.1f}%"
|
||||
)
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
with col1:
|
||||
metric_card("Revenue", 2400000, 12.5, prefix="$")
|
||||
with col2:
|
||||
metric_card("Customers", 12450, 15.2)
|
||||
with col3:
|
||||
metric_card("NPS Score", 72, 5.0)
|
||||
with col4:
|
||||
metric_card("Churn Rate", 4.2, -0.8, suffix="%")
|
||||
|
||||
# Charts
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.subheader("Revenue Trend")
|
||||
revenue_data = pd.DataFrame({
|
||||
'Month': pd.date_range('2024-01-01', periods=12, freq='M'),
|
||||
'Revenue': [180000, 195000, 210000, 225000, 240000, 255000,
|
||||
270000, 285000, 300000, 315000, 330000, 345000]
|
||||
})
|
||||
fig = px.line(revenue_data, x='Month', y='Revenue',
|
||||
line_shape='spline', markers=True)
|
||||
fig.update_layout(height=300)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
with col2:
|
||||
st.subheader("Revenue by Product")
|
||||
product_data = pd.DataFrame({
|
||||
'Product': ['Enterprise', 'Professional', 'Starter', 'Other'],
|
||||
'Revenue': [45, 32, 18, 5]
|
||||
})
|
||||
fig = px.pie(product_data, values='Revenue', names='Product',
|
||||
hole=0.4)
|
||||
fig.update_layout(height=300)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# Cohort Heatmap
|
||||
st.subheader("Cohort Retention")
|
||||
cohort_data = pd.DataFrame({
|
||||
'Cohort': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
|
||||
'M0': [100, 100, 100, 100, 100],
|
||||
'M1': [85, 87, 84, 86, 88],
|
||||
'M2': [78, 80, 76, 79, None],
|
||||
'M3': [72, 74, 70, None, None],
|
||||
'M4': [68, 70, None, None, None],
|
||||
})
|
||||
fig = go.Figure(data=go.Heatmap(
|
||||
z=cohort_data.iloc[:, 1:].values,
|
||||
x=['M0', 'M1', 'M2', 'M3', 'M4'],
|
||||
y=cohort_data['Cohort'],
|
||||
colorscale='Blues',
|
||||
text=cohort_data.iloc[:, 1:].values,
|
||||
texttemplate='%{text}%',
|
||||
textfont={"size": 12},
|
||||
))
|
||||
fig.update_layout(height=250)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# Alerts Section
|
||||
st.subheader("Alerts")
|
||||
alerts = [
|
||||
{"level": "error", "message": "Churn rate exceeded threshold (>5%)"},
|
||||
{"level": "warning", "message": "Support ticket volume 20% above average"},
|
||||
]
|
||||
for alert in alerts:
|
||||
if alert["level"] == "error":
|
||||
st.error(f"🔴 {alert['message']}")
|
||||
elif alert["level"] == "warning":
|
||||
st.warning(f"🟡 {alert['message']}")
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Do's
|
||||
- **Limit to 5-7 KPIs** - Focus on what matters
|
||||
- **Show context** - Comparisons, trends, targets
|
||||
- **Use consistent colors** - Red=bad, green=good
|
||||
- **Enable drilldown** - From summary to detail
|
||||
- **Update appropriately** - Match metric frequency
|
||||
|
||||
### Don'ts
|
||||
- **Don't show vanity metrics** - Focus on actionable data
|
||||
- **Don't overcrowd** - White space aids comprehension
|
||||
- **Don't use 3D charts** - They distort perception
|
||||
- **Don't hide methodology** - Document calculations
|
||||
- **Don't ignore mobile** - Ensure responsive design
|
||||
|
||||
## Resources
|
||||
|
||||
- [Stephen Few's Dashboard Design](https://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf)
|
||||
- [Edward Tufte's Principles](https://www.edwardtufte.com/tufte/)
|
||||
- [Google Data Studio Gallery](https://datastudio.google.com/gallery)
|
||||
Reference in New Issue
Block a user