¶

Quick facts

Phase: Measure | Version: 2.0.0 | Category: validation | License: Apache-2.0

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

Dashboard Requirements¶

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:

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.
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.
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.
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.
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.
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?
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¶

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

Decisions This Will Inform¶

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

What This Dashboard Is NOT For¶

[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¶

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

Filters and Segments¶

Global Filters¶

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¶

Chart	Filter	Type
[Chart 1]	[Filter]	[Type]
[Chart 2]	[Filter]	[Type]

Segment Definitions¶

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¶

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

Acceptance Criteria¶

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¶

[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¶

Dashboard Requirements: Product Health Dashboard

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¶

Are users healthy? What is our overall user engagement and are users getting value from the product?
Where do users struggle? Which parts of the product have the highest friction or drop-off?
What features drive retention? Which features, when adopted, correlate with long-term retention?
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¶

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.

Real-World Examples¶

See this skill applied to three different product contexts:

Storevine (B2B): Storevine B2B ecommerce platform — Campaigns adoption and revenue analytics dashboard requirements

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¶

Brainshelf (Consumer): Brainshelf consumer PKM app — Resurface experiment dashboard requirements for Amplitude

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¶

Workbench (Enterprise): Workbench enterprise collaboration platform: Blueprints post-launch monitoring dashboard requirements

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¶

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.

¶

Dashboard Requirements¶

When to Use¶

How to Use¶

Instructions¶

Output Template¶

Dashboard Requirements: [Dashboard Name]¶

Overview¶

Purpose and Questions¶

Primary Questions This Dashboard Answers¶

Decisions This Will Inform¶

What This Dashboard Is NOT For¶

Audience¶

Usage Context¶

Key Metrics¶

Metric 1: [Metric Name]¶

Metric 2: [Metric Name]¶

Metric 3: [Metric Name]¶

Metrics Summary Table¶

Visualization Specifications¶

Chart 1: [Chart Title]¶

Chart 2: [Chart Title]¶

Chart 3: [Chart Title]¶

Dashboard Layout Sketch¶

Filters and Segments¶

Global Filters¶

Chart-Specific Filters¶

Segment Definitions¶

Data Sources¶

Primary Sources¶

Data Pipeline Requirements¶

Data Quality Considerations¶

Access and Permissions¶

Access Levels¶

Sensitive Data¶

Alerts and Thresholds¶

Acceptance Criteria¶

Open Questions¶

Appendix¶

Related Dashboards¶

Reference Documents¶

Example Output¶

Dashboard Requirements: Product Health Dashboard¶

Overview¶

Purpose and Questions¶

Primary Questions This Dashboard Answers¶

Decisions This Will Inform¶

What This Dashboard Is NOT For¶

Audience¶

Usage Context¶

Key Metrics¶

Metric 1: Daily Active Users (DAU)¶

Metric 2: DAU/MAU Ratio (Stickiness)¶

Metric 3: Feature Adoption Rate¶

Metric 4: User Retention (Cohort-based)¶

Metric 5: Time to First Value (TTFV)¶

Metrics Summary Table¶

Visualization Specifications¶

Chart 1: KPI Summary Cards¶

Chart 2: Engagement Trend¶

Chart 3: Feature Adoption Breakdown¶

Chart 4: Retention Cohort Heatmap¶

Chart 5: Funnel Drop-off Analysis¶

Chart 6: Detailed Metrics Table¶

Dashboard Layout Sketch¶

Filters and Segments¶

Global Filters¶

Chart-Specific Filters¶

Segment Definitions¶

Data Sources¶

Primary Sources¶

Data Pipeline Requirements¶

Data Quality Considerations¶

Access and Permissions¶

Access Levels¶

Sensitive Data¶

Alerts and Thresholds¶

Acceptance Criteria¶

Open Questions¶

Appendix¶