---
title: "Dashboard Requirements"
description: "Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs."
generated: true
source: scripts/generate-skill-pages.py
tags:
  - Measure
  - validation
---

:::note[Quick facts]
**Phase:** Measure | **Version:** 2.0.0 | **Category:** validation | **License:** Apache-2.0
:::

**Try it:** `/dashboard-requirements "Your context here"`

A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.

## When to Use

- When requesting a new dashboard from data/analytics teams
- To define KPI tracking for a product, feature, or team
- When formalizing ad-hoc reporting into a persistent dashboard
- Before quarterly planning to specify what visibility you need
- When onboarding stakeholders who need self-serve analytics

## How to Use

Use the `/dashboard-requirements` slash command:

```
/dashboard-requirements "Your context here"
```

Or reference the skill file directly: `skills/measure-dashboard-requirements/SKILL.md`

## Instructions

When asked to specify dashboard requirements, follow these steps:

1. **Define the Purpose**
   Start with the questions this dashboard should answer, not the charts it should show. What decisions will this dashboard inform? A dashboard without clear purpose becomes a vanity metrics display.

2. **Identify the Audience**
   Specify who will use this dashboard, how often, and in what context. An executive weekly review has different needs than a team's daily standup board.

3. **Specify Key Metrics**
   For each metric, document: name, business definition (in plain language), calculation formula, data source, and baseline/target values. Ambiguous metrics lead to misaligned dashboards.

4. **Design Visualizations**
   Recommend chart types based on what the data should communicate. Time trends need line charts; comparisons need bar charts; compositions need pie/treemaps. Include dimension breakdowns.

5. **Define Filters and Segments**
   Specify what drill-downs users need: date ranges, user segments, product areas, geographic regions. Anticipate the "slice and dice" questions users will ask.

6. **Document Data Sources**
   Identify where data comes from and any known data quality issues. Note latency requirements.does the dashboard need real-time data or is daily refresh sufficient?

7. **Set Permissions and Access**
   Determine who can view what. Some metrics may need restricted access. Consider both security requirements and organizational politics.

## Output Template

# Dashboard Requirements: [Dashboard Name]

## Overview

**Dashboard Name:** [Name]
**Requestor:** [Who requested this]
**Date:** [When requirements captured]
**Priority:** [High/Medium/Low]
**Target Delivery:** [When needed]

---

## Purpose and Questions

### Primary Questions This Dashboard Answers

<!-- Frame as questions, not metrics -->

1. [Question 1 - e.g., "Are users successfully completing onboarding?"]
2. [Question 2 - e.g., "Where do users drop off in the funnel?"]
3. [Question 3 - e.g., "Which cohorts have the best retention?"]

### Decisions This Will Inform

<!-- What actions will stakeholders take based on this dashboard -->

- [Decision 1]
- [Decision 2]
- [Decision 3]

### What This Dashboard Is NOT For

<!-- Clarify scope boundaries -->

- [Out of scope item 1]
- [Out of scope item 2]

---

## Audience

| Audience | Usage Frequency | Primary Questions |
|----------|-----------------|-------------------|
| [Role/Team 1] | [Daily/Weekly/Monthly] | [What they care about] |
| [Role/Team 2] | [Daily/Weekly/Monthly] | [What they care about] |
| [Role/Team 3] | [Daily/Weekly/Monthly] | [What they care about] |

### Usage Context

**When will this be viewed?**
[E.g., "Weekly team meeting review", "Daily morning check", "Monthly board prep"]

**What device/format?**
[E.g., "Desktop browser", "TV screen in office", "Mobile for on-the-go"]

---

## Key Metrics

### Metric 1: [Metric Name]

| Attribute | Value |
|-----------|-------|
| **Business Definition** | [Plain language explanation] |
| **Calculation** | [Formula: numerator / denominator, etc.] |
| **Data Source** | [Where data comes from] |
| **Granularity** | [Daily/Weekly/Monthly] |
| **Current Baseline** | [Current value if known] |
| **Target** | [Goal value] |
| **Notes** | [Edge cases, known issues] |

### Metric 2: [Metric Name]

| Attribute | Value |
|-----------|-------|
| **Business Definition** | [Plain language explanation] |
| **Calculation** | [Formula: numerator / denominator, etc.] |
| **Data Source** | [Where data comes from] |
| **Granularity** | [Daily/Weekly/Monthly] |
| **Current Baseline** | [Current value if known] |
| **Target** | [Goal value] |
| **Notes** | [Edge cases, known issues] |

### Metric 3: [Metric Name]

| Attribute | Value |
|-----------|-------|
| **Business Definition** | [Plain language explanation] |
| **Calculation** | [Formula: numerator / denominator, etc.] |
| **Data Source** | [Where data comes from] |
| **Granularity** | [Daily/Weekly/Monthly] |
| **Current Baseline** | [Current value if known] |
| **Target** | [Goal value] |
| **Notes** | [Edge cases, known issues] |

### Metrics Summary Table

| Metric | Definition | Source | Target |
|--------|------------|--------|--------|
| [Metric 1] | [Short definition] | [Source] | [Target] |
| [Metric 2] | [Short definition] | [Source] | [Target] |
| [Metric 3] | [Short definition] | [Source] | [Target] |
| [Metric 4] | [Short definition] | [Source] | [Target] |

---

## Visualization Specifications

### Chart 1: [Chart Title]

| Attribute | Value |
|-----------|-------|
| **Purpose** | [What question this answers] |
| **Chart Type** | [Line/Bar/Pie/Table/etc.] |
| **X-Axis** | [Dimension - e.g., Date, Category] |
| **Y-Axis** | [Metric(s)] |
| **Series/Breakdown** | [How data is grouped] |
| **Interactivity** | [Tooltips, drill-down, click actions] |
| **Position** | [Top-left, prominent, etc.] |

### Chart 2: [Chart Title]

| Attribute | Value |
|-----------|-------|
| **Purpose** | [What question this answers] |
| **Chart Type** | [Line/Bar/Pie/Table/etc.] |
| **X-Axis** | [Dimension - e.g., Date, Category] |
| **Y-Axis** | [Metric(s)] |
| **Series/Breakdown** | [How data is grouped] |
| **Interactivity** | [Tooltips, drill-down, click actions] |
| **Position** | [Top-left, prominent, etc.] |

### Chart 3: [Chart Title]

| Attribute | Value |
|-----------|-------|
| **Purpose** | [What question this answers] |
| **Chart Type** | [Line/Bar/Pie/Table/etc.] |
| **X-Axis** | [Dimension - e.g., Date, Category] |
| **Y-Axis** | [Metric(s)] |
| **Series/Breakdown** | [How data is grouped] |
| **Interactivity** | [Tooltips, drill-down, click actions] |
| **Position** | [Top-left, prominent, etc.] |

### Dashboard Layout Sketch

<!-- ASCII layout or link to mockup -->

```
┌─────────────────────────────────────────────────┐
│  [KPI Card 1]  [KPI Card 2]  [KPI Card 3]      │
├────────────────────────┬────────────────────────┤
│                        │                        │
│   [Chart 1: Trend]     │   [Chart 2: Funnel]   │
│                        │                        │
├────────────────────────┴────────────────────────┤
│                                                 │
│            [Chart 3: Detailed Table]            │
│                                                 │
└─────────────────────────────────────────────────┘
```

---

## Filters and Segments

### Global Filters

<!-- Filters that apply to entire dashboard -->

| Filter | Type | Default Value | Options |
|--------|------|---------------|---------|
| Date Range | Date picker | Last 30 days | Custom, presets |
| [Filter 2] | [Dropdown/Multi-select] | [Default] | [Options] |
| [Filter 3] | [Dropdown/Multi-select] | [Default] | [Options] |

### Chart-Specific Filters

<!-- Filters for individual charts -->

| Chart | Filter | Type |
|-------|--------|------|
| [Chart 1] | [Filter] | [Type] |
| [Chart 2] | [Filter] | [Type] |

### Segment Definitions

<!-- Pre-defined segments for analysis -->

| Segment Name | Definition | Use Case |
|--------------|------------|----------|
| [Segment 1] | [Criteria] | [When to use] |
| [Segment 2] | [Criteria] | [When to use] |

---

## Data Sources

### Primary Sources

| Source | Type | Owner | Latency | Quality Notes |
|--------|------|-------|---------|---------------|
| [Source 1] | [Database/API/File] | [Team] | [Real-time/Daily/etc.] | [Known issues] |
| [Source 2] | [Database/API/File] | [Team] | [Real-time/Daily/etc.] | [Known issues] |

### Data Pipeline Requirements

**Refresh Frequency:** [Real-time / Hourly / Daily / Weekly]
**Refresh Time:** [When refresh should complete, e.g., "by 6am UTC"]
**Historical Data Needed:** [How far back, e.g., "Last 12 months"]
**Data Retention:** [How long to keep, e.g., "Rolling 2 years"]

### Data Quality Considerations

- [Known data quality issue 1 and how to handle]
- [Known data quality issue 2 and how to handle]

---

## Access and Permissions

### Access Levels

| Role/Group | Access Level | Restrictions |
|------------|--------------|--------------|
| [Group 1] | Full access | None |
| [Group 2] | View only | Cannot export |
| [Group 3] | Limited | Only sees [section] |

### Sensitive Data

| Data Element | Sensitivity | Handling |
|--------------|-------------|----------|
| [Element 1] | [PII/Confidential/etc.] | [Mask/Aggregate/Restrict] |

---

## Alerts and Thresholds

<!-- Optional: automated notifications -->

| Condition | Threshold | Action | Recipients |
|-----------|-----------|--------|------------|
| [Metric 1] drops below | [Value] | Send email | [Who] |
| [Metric 2] exceeds | [Value] | Slack alert | [Channel] |

---

## Acceptance Criteria

<!-- How do we know the dashboard is complete and correct -->

- [ ] All metrics match definitions when spot-checked
- [ ] Filters work correctly across all charts
- [ ] Dashboard loads in under [X] seconds
- [ ] All users can access with correct permissions
- [ ] Data refreshes by [time] each day

---

## Open Questions

- [Question 1 for data team]
- [Question 2 needing clarification]

---

## Appendix

### Related Dashboards

- [Link to related dashboard 1]
- [Link to related dashboard 2]

### Reference Documents

- [Link to metric definitions]
- [Link to data dictionary]

---

*Requirements version 1.0. Update as needs evolve.*

## Example Output

<details>
<summary>Dashboard Requirements: Product Health Dashboard</summary>

# Dashboard Requirements: Product Health Dashboard

## Overview

**Dashboard Name:** Product Health Dashboard
**Requestor:** Maya Johnson, Product Manager
**Date:** January 2026
**Priority:** High
**Target Delivery:** End of Q1 2026

---

## Purpose and Questions

### Primary Questions This Dashboard Answers

1. **Are users healthy?** What is our overall user engagement and are users getting value from the product?
2. **Where do users struggle?** Which parts of the product have the highest friction or drop-off?
3. **What features drive retention?** Which features, when adopted, correlate with long-term retention?
4. **Are we trending up or down?** How do key metrics compare to previous periods?

### Decisions This Will Inform

- Prioritization of product improvements based on friction points
- Feature investment decisions based on retention correlation
- Resource allocation to high-impact areas
- Early warning on user health problems before they hit revenue

### What This Dashboard Is NOT For

- Deep-dive analysis on specific features (separate feature dashboards exist)
- Real-time operational monitoring (use DataDog for that)
- Individual user support (use admin tools)
- Financial/revenue metrics (finance owns that dashboard)

---

## Audience

| Audience | Usage Frequency | Primary Questions |
|----------|-----------------|-------------------|
| Product Team | Daily | Feature adoption, user friction |
| Leadership | Weekly | Overall health trends, KPIs |
| Engineering | Weekly | Error rates, performance impact on UX |
| Customer Success | Daily | Account health signals |

### Usage Context

**When will this be viewed?**
- Product team: Daily standup (quick KPI check) and weekly deep-dive
- Leadership: Weekly product review meeting
- CS: Before customer calls to assess account health

**What device/format?**
- Desktop browser (primary)
- Shared on TV in product team area
- Exported to PDF for monthly board reports

---

## Key Metrics

### Metric 1: Daily Active Users (DAU)

| Attribute | Value |
|-----------|-------|
| **Business Definition** | Unique users who performed any meaningful action in the product on a given day |
| **Calculation** | COUNT(DISTINCT user_id) WHERE action_type NOT IN ('login', 'logout') AND event_date = date |
| **Data Source** | events.user_actions table |
| **Granularity** | Daily |
| **Current Baseline** | 12,400 |
| **Target** | 15,000 by end of Q2 |
| **Notes** | Excludes bot accounts and internal users |

### Metric 2: DAU/MAU Ratio (Stickiness)

| Attribute | Value |
|-----------|-------|
| **Business Definition** | Ratio of daily active users to monthly active users, indicating how often users return |
| **Calculation** | DAU / MAU (rolling 30-day MAU) |
| **Data Source** | Derived from events.user_actions |
| **Granularity** | Daily |
| **Current Baseline** | 0.32 (32%) |
| **Target** | 0.40 (40%) |
| **Notes** | Industry benchmark for SaaS is 0.20-0.40 |

### Metric 3: Feature Adoption Rate

| Attribute | Value |
|-----------|-------|
| **Business Definition** | Percentage of MAU who have used each core feature at least once in the past 30 days |
| **Calculation** | COUNT(DISTINCT users who used feature) / MAU |
| **Data Source** | events.feature_usage table |
| **Granularity** | Daily (rolling 30-day) |
| **Current Baseline** | Varies by feature (see chart) |
| **Target** | Top 5 features > 50% adoption |
| **Notes** | Breakdown by feature; shows per-feature adoption |

### Metric 4: User Retention (Cohort-based)

| Attribute | Value |
|-----------|-------|
| **Business Definition** | Percentage of users from a signup cohort who are still active after N days |
| **Calculation** | (Active users in cohort at day N) / (Total users in cohort) |
| **Data Source** | events.user_actions + users.signups |
| **Granularity** | Weekly cohorts, measured at D7, D14, D30, D60, D90 |
| **Current Baseline** | D30: 42% |
| **Target** | D30: 55% |
| **Notes** | Compare cohorts over time to see if retention is improving |

### Metric 5: Time to First Value (TTFV)

| Attribute | Value |
|-----------|-------|
| **Business Definition** | Time from signup to completing first meaningful action (creating first project) |
| **Calculation** | MEDIAN(first_project_created_at - signup_at) |
| **Data Source** | users.signups + events.project_created |
| **Granularity** | Daily (rolling 7-day average) |
| **Current Baseline** | 2.3 days |
| **Target** | < 1 day |
| **Notes** | Users who never create a project counted as NULL/excluded |

### Metrics Summary Table

| Metric | Definition | Source | Target |
|--------|------------|--------|--------|
| DAU | Unique users with meaningful action | events.user_actions | 15,000 |
| DAU/MAU | Stickiness ratio | Derived | 40% |
| Feature Adoption | % MAU using each feature | events.feature_usage | Top 5 > 50% |
| D30 Retention | Users active 30 days post-signup | events + users | 55% |
| TTFV | Time to first project creation | events + users | < 1 day |

---

## Visualization Specifications

### Chart 1: KPI Summary Cards

| Attribute | Value |
|-----------|-------|
| **Purpose** | At-a-glance health check of key metrics |
| **Chart Type** | KPI Cards (4 cards in a row) |
| **Metrics Shown** | DAU, DAU/MAU, D30 Retention, TTFV |
| **Comparison** | Show vs. previous period and vs. target |
| **Interactivity** | Click card to see trend chart |
| **Position** | Top of dashboard, most prominent |

### Chart 2: Engagement Trend

| Attribute | Value |
|-----------|-------|
| **Purpose** | Show DAU and MAU trends over time |
| **Chart Type** | Line chart with dual axis |
| **X-Axis** | Date (daily) |
| **Y-Axis** | Left: DAU, Right: DAU/MAU ratio |
| **Series/Breakdown** | DAU line, MAU line, DAU/MAU line |
| **Interactivity** | Hover for values, zoom on date range |
| **Position** | Top-left main section |

### Chart 3: Feature Adoption Breakdown

| Attribute | Value |
|-----------|-------|
| **Purpose** | Show which features users are/aren't adopting |
| **Chart Type** | Horizontal bar chart |
| **X-Axis** | Adoption rate (%) |
| **Y-Axis** | Feature name |
| **Series/Breakdown** | Single series, sorted by adoption |
| **Interactivity** | Click bar to see feature trend over time |
| **Position** | Top-right main section |

### Chart 4: Retention Cohort Heatmap

| Attribute | Value |
|-----------|-------|
| **Purpose** | Compare retention across weekly cohorts |
| **Chart Type** | Cohort heatmap (weeks × retention periods) |
| **X-Axis** | Days since signup (D1, D7, D14, D30, D60, D90) |
| **Y-Axis** | Signup week cohort |
| **Series/Breakdown** | Color intensity = retention % |
| **Interactivity** | Hover for exact values |
| **Position** | Middle section, full width |

### Chart 5: Funnel Drop-off Analysis

| Attribute | Value |
|-----------|-------|
| **Purpose** | Identify where users struggle in key flows |
| **Chart Type** | Funnel chart |
| **X-Axis** | Funnel step |
| **Y-Axis** | Users (absolute and %) |
| **Series/Breakdown** | Steps: Signup → Onboarding Complete → First Project → Invited Team → Paid |
| **Interactivity** | Click step to see breakdown by segment |
| **Position** | Bottom-left |

### Chart 6: Detailed Metrics Table

| Attribute | Value |
|-----------|-------|
| **Purpose** | Detailed view for deep-dive analysis |
| **Chart Type** | Data table with sorting |
| **Columns** | Date, DAU, MAU, DAU/MAU, New Signups, Churned Users, Feature 1-5 adoption |
| **Interactivity** | Sort by any column, export to CSV |
| **Position** | Bottom section, collapsible |

### Dashboard Layout Sketch

```
┌─────────────────────────────────────────────────────────────────────┐
│  [DAU: 12.4K]  [Stickiness: 32%]  [D30 Ret: 42%]  [TTFV: 2.3d]    │
│     ▲ +5%          ▼ -2%              ▲ +3%           ▼ +0.2d      │
├────────────────────────────────┬────────────────────────────────────┤
│                                │                                    │
│  📈 Engagement Trend           │  📊 Feature Adoption               │
│  [Line chart: DAU/MAU]         │  [Horizontal bars by feature]     │
│                                │                                    │
├────────────────────────────────┴────────────────────────────────────┤
│                                                                     │
│  🔲 Retention Cohort Heatmap                                        │
│  [Week cohorts × D1/D7/D14/D30/D60/D90]                            │
│                                                                     │
├────────────────────────────────┬────────────────────────────────────┤
│                                │                                    │
│  ⬇️ Funnel Analysis            │  📋 Detailed Data Table            │
│  [Signup → Value funnel]       │  [Sortable metric table]          │
│                                │                                    │
└────────────────────────────────┴────────────────────────────────────┘
```

---

## Filters and Segments

### Global Filters

| Filter | Type | Default Value | Options |
|--------|------|---------------|---------|
| Date Range | Date picker | Last 30 days | Last 7/30/90 days, MTD, QTD, Custom |
| Plan Type | Multi-select | All | Free, Starter, Professional, Enterprise |
| User Segment | Multi-select | All | New (<30d), Active, At-risk, Churned |
| Platform | Dropdown | All | Web, iOS, Android |

### Chart-Specific Filters

| Chart | Filter | Type |
|-------|--------|------|
| Feature Adoption | Feature category | Dropdown (Core, Advanced, Admin) |
| Funnel | Entry point | Dropdown (Organic, Paid, Referral) |

### Segment Definitions

| Segment Name | Definition | Use Case |
|--------------|------------|----------|
| New Users | Signed up within last 30 days | Track onboarding effectiveness |
| At-Risk | No login in 14+ days but not churned | Target for re-engagement |
| Power Users | > 20 sessions per month | Understand ideal user behavior |
| Enterprise | On Enterprise plan | Compare enterprise vs. SMB health |

---

## Data Sources

### Primary Sources

| Source | Type | Owner | Latency | Quality Notes |
|--------|------|-------|---------|---------------|
| events.user_actions | Snowflake table | Data Engineering | 1 hour | 99.9% complete |
| events.feature_usage | Snowflake table | Data Engineering | 1 hour | Some features not instrumented |
| users.signups | Snowflake table | Data Engineering | Real-time | Authoritative source |
| users.subscriptions | Snowflake table | Data Engineering | Daily | Synced from Stripe |

### Data Pipeline Requirements

**Refresh Frequency:** Hourly during business hours, daily overnight
**Refresh Time:** Dashboard current as of top-of-hour; overnight refresh complete by 6am UTC
**Historical Data Needed:** Last 24 months
**Data Retention:** Aggregated data retained indefinitely; raw events 24 months

### Data Quality Considerations

- Bot traffic filtered but occasional false positives; flag if DAU spikes >20% unexpectedly
- Feature usage for "Reports" feature incomplete before Nov 2025 (instrumentation added)
- Enterprise accounts have multiple users; user_id is individual, account_id needed for account-level views

---

## Access and Permissions

### Access Levels

| Role/Group | Access Level | Restrictions |
|------------|--------------|--------------|
| Product Team | Full access | None |
| Engineering | Full access | None |
| Leadership | Full access | None |
| Customer Success | Limited | Cannot see individual user data |
| Sales | View only | Cannot export, account-level only |

### Sensitive Data

| Data Element | Sensitivity | Handling |
|--------------|-------------|----------|
| User email | PII | Not displayed; use user_id |
| Account name | Confidential | Visible to CS/Sales only |

---

## Alerts and Thresholds

| Condition | Threshold | Action | Recipients |
|-----------|-----------|--------|------------|
| DAU drops below | 10,000 | Email + Slack | Product team |
| D30 retention drops below | 35% | Email | PM + Leadership |
| TTFV exceeds | 5 days | Slack | Onboarding squad |
| Feature adoption (any) drops | >10% week-over-week | Email | Feature owner |

---

## Acceptance Criteria

- [ ] All 5 core metrics display correctly and match manual SQL verification
- [ ] Cohort retention heatmap shows at least 12 weeks of historical cohorts
- [ ] All filters work across all charts simultaneously
- [ ] Dashboard loads in under 5 seconds on standard connection
- [ ] Data refreshes correctly by 7am UTC each morning
- [ ] CS team confirms they can access account-level data
- [ ] Export to CSV works for detailed table
- [ ] Mobile-responsive for leadership checking on phones

---

## Open Questions

- Should we include revenue/MRR on this dashboard or keep it separate?
- Do we need real-time DAU or is hourly sufficient?
- Should cohorts be weekly or monthly granularity?

---

## Appendix

### Related Dashboards

- Feature Deep-Dive: Reporting (dashboard link)
- Onboarding Funnel Dashboard (dashboard link)
- Finance & Revenue Dashboard (dashboard link)

### Reference Documents

- Metric Definitions Wiki (internal link)
- Data Dictionary (internal link)
- Instrumentation Spec for Feature Tracking (internal link)

---

*Requirements version 1.0. Update as needs evolve.*

</details>

## Real-World Examples

See this skill applied to three different product contexts:

<details>
<summary>Storevine (B2B): Storevine B2B ecommerce platform . Campaigns adoption and revenue analytics dashboard requirements</summary>

**Prompt:**

```
/dashboard-requirements

Dashboard: Campaigns adoption and revenue . post-GA monitoring
Audience: Growth PM (daily), Merchant Success (weekly), Head of Product
          (monthly board prep)

Key questions to answer:
1. Are non-adopter merchants sending their first campaign?
   (primary hypothesis metric: first-send rate, 60-day window)
2. Is Campaigns driving measurable revenue for merchants?
   (7-day attributed revenue per campaign send)
3. Is the email-related churn rate declining since GA?
   (churn cohort analysis: merchants with and without Campaigns sends)

Metrics needed:
- First-send rate (60-day, non-adopter segment)
- Campaigns-attributed revenue (7-day window, rolling)
- Active Campaigns merchants (sent ≥1 campaign in last 30 days)
- Churn rate by Campaigns usage cohort
- Send failure rate and unsubscribe rate (guardrails)

Analytics platform: Amplitude (events) + Storevine order DB (revenue)

Need: full dashboard requirements doc with metric definitions,
visualizations, filters, data sources, and acceptance criteria.
```

---

**Output:**

# Dashboard Requirements: Campaigns Adoption and Revenue

</details>

<details>
<summary>Brainshelf (Consumer): Brainshelf consumer PKM app . Resurface experiment dashboard requirements for Amplitude</summary>

**Prompt:**

```
/dashboard-requirements

resurface experiment dashboard for amplitude. need it ready before
the a/b test starts (mar 9).

two audiences:
1. product team (priya, chloe, alex, jordan) . daily monitoring
   during the 4-week test
2. marco (ceo) . weekly exec check-in, needs a single-screen summary

questions the dashboard should answer:
- is the treatment group returning more than control?
- are users clicking items in the digest?
- is the unsubscribe rate within the guardrail?
- what's the opt-in funnel conversion rate?
- are there segment differences (library size, cadence)?

charts i want:
1. 7-day return rate trend (treatment vs control, weekly)
2. email CTR trend (daily)
3. opt-in funnel (card viewed → opted in)
4. unsubscribe rate trend (weekly, with guardrail line)
5. segment breakdown table (library size, cadence)

filters: date range, experiment variant, library size segment.
```

---

**Output:**

# Dashboard Requirements: Resurface Experiment Dashboard

</details>

<details>
<summary>Workbench (Enterprise): "Workbench enterprise collaboration platform: Blueprints post-launch monitoring dashboard requirements"</summary>

**Prompt:**

```
/dashboard-requirements

I need dashboard requirements for the Blueprints post-launch monitoring dashboard. Here's the context:

**Audiences:**
1. Rachel V. (PM) -- daily check: adoption trends, approval bottlenecks, template usage
2. Sandra C. (Head of Product) -- weekly review: executive summary, account growth, key health metrics
3. Karen L. (Engineering) -- real-time: system health, merge latency, error rates

**Key metrics from the PRD and experiment results:**
- Median time-to-approved (target: ≤2.5 days [fictional])
- Empty-section submission rate (target: ≤10% [fictional])
- Approval cycle count (target: ≤1.5 cycles [fictional])
- Blueprint adoption: monthly active Blueprint creators (target: 2,000 [fictional])
- Enterprise account growth (target: 500 → 650 in 12 months [fictional])

**Data sources:**
- Workbench analytics pipeline (event data from instrumentation spec)
- WebSocket provider telemetry (merge latency, connection count, error rate)
- CRM pipeline (account growth, enterprise tier)
- Support ticketing system (Blueprint-related ticket volume)

**Visualization preferences:**
- Time-to-approved: trend line over time (weekly median)
- Adoption: stacked area chart by department/template type
- Approval funnel: horizontal funnel chart
- System health: real-time gauges with alert thresholds

Please generate the full dashboard requirements including layout, filters, alerts, and acceptance criteria.
```

**Output:**

# Dashboard Requirements: Blueprints Post-Launch Monitor

</details>

## Quality Checklist

Before finalizing, verify:

- [ ] Purpose is framed as questions to answer, not charts to build
- [ ] All metrics have clear definitions and calculation formulas
- [ ] Data sources are identified and accessible
- [ ] Visualization choices match the type of insight needed
- [ ] Filters enable the drill-downs users will want
- [ ] Refresh frequency matches decision-making cadence

## Output Format

Use the template in `references/TEMPLATE.md` to structure the output.
