Dashboard Design

Dashboard Design Workflow

Purpose

Design a structured set of metrics across the user lifecycle that gives a complete picture of product health, not just a single North Star. Creates recurring dashboards for team reviews, early problem detection, and strategic decision-making.

When to Use This Workflow

Use this workflow when:

• Standing up a new product team and need recurring dashboard
• Existing dashboard feels incomplete or unfocused
• Preparing for quarterly business reviews
• Onboarding to a product and want to understand what "healthy" looks like
• Establishing metrics for ongoing product monitoring
• Need to rally team around clear success indicators

Skills Sequence

This workflow orchestrates 4 core skills:

1. North Star Alignment
   ↓ (Anchor dashboard to company mission and business model)
2. Funnel-Based Metric Mapping
   ↓ (Ensure coverage across all lifecycle stages)
3. Proxy Metric Selection
   ↓ (Pick measurable indicators for each stage)
4. Trade-off Evaluation
   ↓ (Include counter-metrics to catch unintended effects)
   
OUTPUT: Dashboard structure by funnel, 5-10 metrics with definitions,
        counter-metrics, review cadence, alert thresholds

Required Inputs

Gather this information before starting:

Product Context

• Company/product mission statement
  • What's the overarching goal?
• Business model type
  • One of 5 categories (ads, freemium, SaaS, marketplace, e-commerce)
• Strategic priorities
  • Growth, retention, monetization, quality?

Product Lifecycle

• User lifecycle stages for this product
  • How do users progress through your product?
  • What's the journey from awareness to retained power user?

Current State

• Existing metrics (if any)
  • What are you currently tracking?
  • What gaps exist?
• Key stakeholder questions
  • What questions should dashboard answer?
  • What decisions does it inform?

Operational Constraints

• Review cadence desired
  • Daily, weekly, monthly?
  • Different cadences for different audiences?
• Alert capability
  • Can you set automated alerts?
  • What thresholds trigger escalation?

Workflow Steps

Step 1: North Star Anchoring (15 minutes)

Use the north-star-alignment skill

Ground the dashboard in company-level goals:

Activities:

1. Identify business model and corresponding North Star metrics
2. Articulate how this product serves company mission
3. Define "healthy" for this product relative to North Star

Questions to answer:

• What company-level metrics does this product impact?
• How does product health translate to company health?
• What would "great" look like for this product?
• What's the connection between product and company success?

Output:

## North Star Anchoring

**Business Model:** [Type]

**Company North Star Metrics:**
- [Metric 1]: [Definition]
- [Metric 2]: [Definition]

**Product's North Star Connection:**
- This product contributes to [Company North Star] by [mechanism]
- "Healthy" product = [Description tied to North Star]

**Mission Alignment:**
- Product serves mission: [How]
- Strategic priority: [Growth/Retention/Monetization/Quality]

Step 2: Funnel Structure Mapping (20 minutes)

Use the funnel-metric-mapping skill

Decompose user journey into stages and identify metrics per stage:

Activities:

1. Define lifecycle stages (typically 4-5 stages)
2. List 1-3 key metrics per stage
3. Identify transition conversion rates
4. Map any flywheel dynamics

Funnel template:

Reach → Activation → Engagement (Breadth) → Engagement (Depth) → Retention

For each stage, ask:

• What defines success at this stage?
• What volume metric matters?
• What quality metric matters?
• What's the conversion rate to next stage?

Output:

## Funnel Structure

**Stage 1: Reach**
- Definition: [When users become aware/access product]
- Key Metrics:
  1. [Metric]: [Definition + why it matters]
  2. [Metric]: [Definition + why it matters]
- Conversion to Activation: [%]

**Stage 2: Activation**
- Definition: [When users complete setup and reach first value]
- Key Metrics:
  1. [Metric]: [Definition + why it matters]
  2. [Metric]: [Definition + why it matters]
- Conversion to Engagement: [%]

**Stage 3: Engagement (Breadth)**
- Definition: [Regular product usage]
- Key Metrics:
  1. [Metric]: [Definition + why it matters]
  2. [Metric]: [Definition + why it matters]

**Stage 4: Engagement (Depth)**
- Definition: [Value-creating actions]
- Key Metrics:
  1. [Metric]: [Definition + why it matters]
  2. [Metric]: [Definition + why it matters]

**Stage 5: Retention**
- Definition: [Long-term repeat usage]
- Key Metrics:
  1. [Metric]: [Definition + why it matters]
  2. [Metric]: [Definition + why it matters]

**Flywheel Dynamics:**
- [If applicable, describe virtuous cycles]

Step 3: Proxy Metric Selection (20 minutes)

Use the proxy-metric-selection skill

For each funnel stage, define precise measurable indicators:

Activities:

1. For each metric, define mathematical formula (numerator/denominator)
2. Create simplified alternatives where needed
3. Validate each metric is actionable by the team
4. Ensure leading indicators (not just lagging)

Criteria for dashboard metrics:

• Actionable: Team can directly influence
• Understandable: Explainable in one sentence
• Measurable: Clear data source
• Leading: Provides early signal, not just hindsight

Output:

## Metric Definitions

**Reach Metrics:**

**1. [Metric Name]**
- Formula: [Numerator] / [Denominator]
- Data Source: [Where to measure]
- Actionability: [How team influences]
- Why it matters: [Connection to funnel stage goal]

**2. [Metric Name]**
[Same structure]

**Activation Metrics:**
[1-2 metrics with same detail]

**Engagement (Breadth) Metrics:**
[1-2 metrics with same detail]

**Engagement (Depth) Metrics:**
[1-2 metrics with same detail]

**Retention Metrics:**
[1-2 metrics with same detail]

Step 4: Counter-Metric Identification (15 minutes)

Use the tradeoff-evaluation skill

Identify metrics that could indicate unintended consequences:

Activities:

1. For each primary metric, ask "what could go wrong?"
2. Identify cannibalization risks
3. Define acceptable ranges
4. Plan monitoring approach

Counter-metric categories:

1. Cannibalization Metrics

   • What other products/features might suffer?
   • Example: New feature adoption hurting core feature usage

2. Quality Degradation Metrics

   • What quality indicators could decline?
   • Example: Growth at expense of user satisfaction

3. Sustainability Metrics

   • What could indicate unsustainable growth?
   • Example: High churn masked by high acquisition

4. Balance Metrics (for marketplaces)

   • Supply vs. demand balance
   • Example: Too many drivers, not enough riders

Output:

## Counter-Metrics

**For Primary Metric: [Name]**
- Counter-metric 1: [Name]
  - What it catches: [Unintended effect]
  - Acceptable range: [Threshold]
  - Alert if: [Condition]

**For Primary Metric: [Name]**
- Counter-metric 2: [Name]
  - What it catches: [Unintended effect]
  - Acceptable range: [Threshold]
  - Alert if: [Condition]

[2-3 counter-metrics total]

**Cannibalization Watch:**
- [Product/feature to monitor for impact]

**Quality Indicators:**
- [Metric to ensure quality maintained]

Step 5: Dashboard Assembly and Review Cadence (15 minutes)

Activities:

1. Prioritize metrics (not all are equal)
2. Organize into dashboard sections
3. Define review cadence
4. Set alert thresholds
5. Assign ownership

Dashboard structure:

# [Product Name] Health Dashboard

## 🎯 North Star (Company-Level)
[1-2 company metrics this product impacts]

## 📊 Product North Star
[1-2 top-line product metrics]

## 🔄 Funnel Health

### Reach
- [Metric 1]: [Current value] [Trend ↑↓→]
- [Metric 2]: [Current value] [Trend ↑↓→]

### Activation
- [Metric 1]: [Current value] [Trend ↑↓→]
- Reach → Activation: [Conversion %]

### Engagement (Breadth)
- [Metric 1]: [Current value] [Trend ↑↓→]
- Activation → Engagement: [Conversion %]

### Engagement (Depth)
- [Metric 1]: [Current value] [Trend ↑↓→]

### Retention
- [Metric 1]: [Current value] [Trend ↑↓→]
- [Metric 2]: [Current value] [Trend ↑↓→]

## ⚠️ Counter-Metrics & Health Checks
- [Counter-metric 1]: [Current value] [Status: ✓ Healthy / ⚠️ Warning / 🚨 Alert]
- [Counter-metric 2]: [Current value] [Status: ✓ Healthy / ⚠️ Warning / 🚨 Alert]

## 📈 Key Insights (Updated Weekly)
- [Insight 1]
- [Insight 2]
- [Action items]

Review cadence definition:

## Dashboard Review Cadence

**Daily Review (5 minutes):**
- Audience: Product team
- Metrics: [2-3 most critical metrics]
- Purpose: Early problem detection
- Action threshold: [What triggers immediate investigation]

**Weekly Review (30 minutes):**
- Audience: Product team + stakeholders
- Metrics: Full dashboard
- Purpose: Trend analysis, prioritization
- Format: [Standup / Presentation / Async doc]

**Monthly Deep-Dive (60 minutes):**
- Audience: Product team + leadership
- Metrics: Full dashboard + segmentation analysis
- Purpose: Strategic review, goal setting
- Format: [Meeting / Written review]

**Quarterly Business Review:**
- Audience: Executives
- Metrics: North Star + key highlights
- Purpose: Alignment on strategy and resources

Alert thresholds:

## Alert Configuration

**Critical Alerts (Immediate attention):**
- [Metric] drops below [threshold]: [Who to notify]
- [Counter-metric] exceeds [threshold]: [Who to notify]

**Warning Alerts (Next-day review):**
- [Metric] trends down for [X days]: [Who to notify]

**Monitoring (Weekly review):**
- [Metric ranges to track]

Ownership:

## Metric Ownership

| Metric | Owner | Data Source | Update Frequency |
|--------|-------|-------------|------------------|
| [Metric 1] | [Name/Team] | [Tool/Table] | Real-time |
| [Metric 2] | [Name/Team] | [Tool/Table] | Daily |
| [Metric 3] | [Name/Team] | [Tool/Table] | Weekly |

Dashboard Design Principles

Principle 1: Comprehensive but Focused

Balance:

• Cover all lifecycle stages (comprehensive)
• Limit to 5-10 metrics total (focused)
• Prioritize metrics by impact and actionability

Avoid:

• Single-metric dashboards (miss problems elsewhere)
• 20+ metric dashboards (overwhelming, unfocused)

Principle 2: Leading + Lagging Indicators

Leading indicators (early signals):

• Activation rate (predicts retention)
• Engagement frequency (predicts habit formation)
• NPS/satisfaction (predicts churn)

Lagging indicators (confirm outcomes):

• Retention rate (confirms product-market fit)
• Revenue (confirms monetization)
• Lifetime value (confirms unit economics)

Balance: Include both for complete picture

Principle 3: Volume + Quality

Volume metrics (quantity):

• Total users
• Total transactions
• Total content created

Quality metrics (value):

• User satisfaction scores
• Transaction value
• Content engagement rate

Balance: Prevent optimizing for wrong thing

Principle 4: Segment Where It Matters

Standard view:

• Aggregate metrics for whole product

Segmented views:

• By user type (power users, new users, paying users)
• By geography (if relevant)
• By cohort (when they joined)

When to segment:

• Behavior varies significantly by segment
• Different strategies for different segments
• Need to track specific initiatives

Common Mistakes

Success Criteria

Dashboard design succeeds when:

• Anchored to company North Star explicitly
• Covers all major lifecycle stages (4-5 stages)
• 5-10 primary metrics with precise definitions
• 2-3 counter-metrics included
• Review cadence established (daily, weekly, monthly)
• Alert thresholds defined
• Ownership assigned for each metric
• Stakeholders understand and accept dashboard
• Dashboard answers key product questions
• Team can explain why each metric matters

Real-World Example: Uber Driver Quality Dashboard

Step 1: North Star Anchoring (15 min)

Business Model: Two-sided marketplace
Company North Star: Monthly Active Drivers + Monthly Active Riders
Product (Driver Quality): Contributes to driver retention and rider satisfaction
"Healthy" = High-quality drivers staying active long-term
Strategic Priority: Quality + Retention (sustainable supply)

Step 2: Funnel Structure (20 min)

Reach: All active drivers (baseline)
  - Total active drivers (monthly)
  
Activation: Drivers engage with quality program
  - % viewing quality dashboard (target: 80%)
  - % reading quality tips (target: 50%)

Engagement (Breadth): Drivers aware of ratings
  - % checking ratings weekly (target: 60%)
  
Engagement (Depth): Drivers improve quality
  - Tips received per active driver
  - Rating improvement trend

Retention: Drivers maintain high quality
  - % drivers in 4.8+ bucket month-over-month
  - Hours driven by quality tier

Step 3: Proxy Metrics (20 min)

PRIMARY METRICS:

1. Driver Quality Distribution
   - Formula: Hours driven by rating bucket / Total hours
   - X-axis: 4.5-4.74, 4.75-5.0, 5.0+ with tips
   - Y-axis: Hours driven
   - Goal: Maximize hours in 5.0+ bucket

2. Quality Program Engagement
   - Formula: Drivers viewing dashboard weekly / Total active drivers
   - Target: 80%
   - Leading indicator of quality awareness

3. Tip Rate
   - Formula: Drivers receiving ≥1 tip per week / Total active drivers
   - Target: 40%
   - Quality indicator beyond ratings

4. Rating Stability
   - Formula: Drivers maintaining/improving rating MoM / Total
   - Target: 85%
   - Retention proxy

Step 4: Counter-Metrics (15 min)

COUNTER-METRICS:

1. Driver Churn Rate
   - What it catches: Quality standards too strict
   - Current: 8%/month
   - Acceptable: <10%
   - Alert if: >12%

2. Ride Acceptance Rate
   - What it catches: Drivers becoming too picky
   - Current: 92%
   - Acceptable: >85%
   - Alert if: <85%

3. Surge Pricing Frequency
   - What it catches: Insufficient supply
   - Current: 15% of rides
   - Acceptable: <20%
   - Alert if: >25%

Step 5: Dashboard Assembly (15 min)

# Uber Driver Quality Dashboard

## 🎯 Company North Star
- Monthly Active Drivers: 500K (↑ 2%)
- Hours Driven (Total): 8M (↑ 3%)

## 📊 Product North Star
- Hours Driven in 4.8+ Bucket: 4.8M / 60% of total (↑ 5%) [GOAL: 65%]
- Quality Program Engagement: 78% (↑ 3%)

## 🔄 Funnel Health

### Activation (Quality Program)
- Dashboard Views: 78% of drivers (target: 80%)
- Tips Read: 52% of drivers (target: 50%) ✓

### Engagement (Quality Awareness)
- Check Ratings Weekly: 58% (target: 60%)
- Tips Received: 38% of drivers (target: 40%)

### Retention (Quality Maintenance)
- Rating Stability MoM: 84% (target: 85%)
- Hours by Quality Tier:
  - 4.5-4.74: 1.5M / 19% (↓ 2%) [Good]
  - 4.75-5.0: 1.7M / 21% (→)
  - 5.0+ tips: 4.8M / 60% (↑ 5%) [Great]

## ⚠️ Counter-Metrics
- Driver Churn: 9.2%/month ✓ (threshold: <10%)
- Acceptance Rate: 90% ✓ (threshold: >85%)
- Surge Frequency: 17% ✓ (threshold: <20%)

## 📈 Key Insights (Week of Dec 1)
- Strong progress toward 65% quality goal (on track for Q1)
- Tip rate slightly below target; testing new prompts
- Churn elevated but within acceptable range
- Action: Launch tip prompt experiment next week

---

## Review Cadence

**Daily (5 min):** Churn rate, acceptance rate (critical alerts)
**Weekly (30 min):** Full dashboard, trend review
**Monthly (60 min):** Deep-dive, segmentation analysis
**Quarterly:** Strategic review with leadership

## Alert Configuration

**Critical:**
- Churn >12%: Alert product lead + ops
- Acceptance <85%: Alert product lead + ops

**Warning:**
- Quality goal progress <2%/month: Weekly review
- Counter-metric approaching threshold: Flag in review

Time to complete: 90 minutes

Related Skills

This workflow orchestrates these skills:

• north-star-alignment (Step 1)
• funnel-metric-mapping (Step 2)
• proxy-metric-selection (Step 3)
• tradeoff-evaluation (Step 4)

Related Workflows

• metrics-definition: Similar process but for one-time metric selection
• goal-setting: Uses dashboard metrics to set OKR targets
• tradeoff-decision: Uses dashboard to monitor trade-offs

Time Estimate

Total: 85-100 minutes

• Step 1 (North Star): 15 min
• Step 2 (Funnel): 20 min
• Step 3 (Proxy): 20 min
• Step 4 (Counter-metrics): 15 min
• Step 5 (Assembly): 15 min
• Buffer: 10 min