executive-dashboard（执行仪表板）

/dm:executive-dashboard

Purpose

Design a C-suite marketing dashboard that translates marketing metrics into business outcomes for executive decision-making. Bridges the gap between marketing activity data and business impact, giving senior leaders the clarity to make faster, better-informed strategic decisions without drowning in operational detail.

Input Required

The user must provide (or will be prompted for):

• Executive role: Primary audience — CEO, CMO, CFO, VP Marketing, or board — each requires different metric emphasis and abstraction level
• Business model and revenue drivers: How the company makes money — SaaS, e-commerce, lead gen, marketplace, subscription — and the key revenue levers marketing influences
• Strategic priorities this quarter: The 2-4 business priorities the executive team is focused on that marketing should ladder up to
• Reporting frequency: How often the dashboard will be reviewed — weekly executive standup, monthly leadership meeting, quarterly board review
• Current data sources and tools: Analytics platforms, CRM, ad platforms, attribution tools, and BI systems currently in use with data freshness and reliability notes
• Existing reports being replaced: Current reporting artifacts the dashboard will consolidate or replace — helps identify gaps and redundancies
• Key decisions the dashboard should inform: Specific decisions executives make that this dashboard should support — budget allocation, channel mix, hiring, campaign scaling, market expansion
• Stakeholder data literacy level: How comfortable the audience is with marketing metrics — determines labeling, context, and narrative density needed

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for custom templates at ~/.claude-marketing/brands/{slug}/templates/. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/dm:brand-setup)?" — or proceed with defaults.
2. Identify north-star metrics: Select 5-7 metrics that directly tie marketing activity to business outcomes — revenue influenced, pipeline generated, customer acquisition cost, lifetime value, market share, brand equity indicators
3. Design metric hierarchy: Organize metrics into three tiers — leading indicators (predict future performance), lagging indicators (confirm past results), and health metrics (signal system stability and sustainability)
4. Select visualization type per metric: Choose the optimal chart type for each metric based on data shape and decision context — trend lines for trajectory, gauges for targets, bar charts for comparisons, sparklines for density
5. Define alert thresholds and anomaly triggers: Set green/yellow/red thresholds for each metric with specific trigger values, and configure anomaly detection rules for unexpected spikes or drops
6. Map data sources to each metric: Document which system provides each metric, how it is calculated, data freshness (real-time, daily, weekly), and known limitations or lag
7. Design layout for scanning speed: Structure the dashboard for F-pattern or Z-pattern scanning — most critical metrics top-left, summary before detail, consistent visual hierarchy, minimal cognitive load
8. Add narrative guidance: Write "how to read this" instructions for each section — what good looks like, what bad looks like, and what action to take in each scenario
9. Build drill-down structure: Design three levels of depth — summary view (the dashboard itself), detail view (campaign or channel breakdowns), and root cause view (diagnostic data for investigating anomalies)
10. Create mobile-friendly variant: Adapt the dashboard layout for mobile or tablet viewing — prioritize top 3-5 metrics, stack vertically, enlarge touch targets, and simplify visualizations
11. Add comparison baselines: Define what each metric is compared against — plan/target, prior period (MoM, QoQ, YoY), industry benchmark, and competitive estimate — with comparison display format

Output

A structured executive dashboard design containing:

• North-star metrics (5-7): Selected metrics with business rationale explaining why each matters to the executive audience and how it connects to strategic priorities
• Metric hierarchy diagram: Visual framework showing leading, lagging, and health metrics with causal relationships and directional influence between them
• Visualization recommendations: Chart type, scale, color coding, and annotation style for each metric with rationale for the design choice
• Alert threshold definitions: Green/yellow/red boundaries for each metric with specific trigger values, anomaly detection rules, and notification routing
• Data source mapping: Metric-by-metric documentation of source system, calculation method, refresh frequency, data latency, and known quality issues
• Dashboard wireframe layout: Spatial layout showing metric placement, section grouping, visual hierarchy, and scanning flow optimized for the target audience
• Narrative guide: Section-by-section presentation guide explaining how to read each area, what questions it answers, and what actions to consider based on the data shown
• Drill-down structure: Three-level depth design — summary (dashboard), detail (channel/campaign breakdown), and root cause (diagnostic investigation) with navigation flow
• Mobile layout variant: Adapted design for mobile viewing with prioritized metrics, vertical stacking, simplified charts, and touch-optimized interactions
• Comparison baseline definitions: For each metric, the comparison standard (target, prior period, benchmark, competitive) with display format and context notes
• Refresh cadence and data latency notes: Documentation of how often each metric updates, expected data lag, and implications for decision timing
• Executive summary template: A 3-sentence written narrative template that synthesizes dashboard findings into a verbal briefing — what happened, why it matters, what to do next
• Glossary of terms: Plain-language definitions of all metrics and marketing terminology for non-marketing stakeholders with examples and context

Agents Used

• analytics-analyst — Metric selection, hierarchy design, visualization recommendations, data source mapping, alert thresholds, drill-down architecture, refresh cadence, and dashboard layout optimization