Marketing Dashboard Builder AI Prompt
Analytics & ReportingintermediateClaude 3.5 Sonnet or GPT-4o. Both excel at structured output and can handle complex specifications. Claude is slightly better at creating detailed implementation frameworks; GPT-4o is faster for quick iterations.
When to Use This Prompt
Use this prompt when you're redesigning your marketing reporting infrastructure, implementing a new analytics platform, or need to justify dashboard investments to stakeholders. It's particularly valuable when your team is drowning in disconnected reports and needs a single source of truth for marketing performance.
The Prompt
You are a marketing analytics expert helping me design a custom marketing dashboard. I need you to create a comprehensive dashboard specification that aligns with my business goals and reporting needs.
## Business Context
- Industry: [YOUR INDUSTRY]
- Company size: [NUMBER OF EMPLOYEES]
- Primary business model: [B2B/B2C/HYBRID]
- Current marketing tech stack: [LIST YOUR TOOLS: e.g., HubSpot, Google Analytics, Salesforce]
- Key business objectives: [LIST 2-3 PRIMARY GOALS]
## Dashboard Requirements
- Primary audience: [ROLE: e.g., CMO, VP Marketing, Marketing Manager]
- Reporting frequency: [DAILY/WEEKLY/MONTHLY]
- Time period to track: [e.g., Last 90 days, YTD, Last 12 months]
- Current pain points: [DESCRIBE WHAT'S MISSING IN CURRENT REPORTING]
## Metrics Priority
Rank these metric categories by importance (1=most important):
- Lead generation and pipeline metrics
- Customer acquisition cost and ROI
- Content performance and engagement
- Email marketing effectiveness
- Social media performance
- Website traffic and conversion
- Customer retention and lifetime value
- Marketing team productivity
## Output Requirements
Provide a dashboard specification that includes:
1. Dashboard name and purpose (1-2 sentences)
2. Recommended layout (grid structure with 8-12 key metrics)
3. Specific KPIs with definitions and calculation methods
4. Data sources for each metric
5. Visualization recommendations (charts, gauges, tables)
6. Refresh frequency and data latency requirements
7. Drill-down capabilities and filters needed
8. Alerts or thresholds to monitor
9. Implementation priority (Phase 1, 2, 3)
10. Tools/platforms needed to build this dashboard
Format the output as a structured specification document that can be handed to a data analyst or marketing ops team for implementation.
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Tips for Best Results
- 1.Fill in all bracketed fields with specific data—generic inputs produce generic dashboards. The more context about your business model and pain points, the more tailored the specification.
- 2.Rank your metric priorities honestly. Resist the urge to include every possible metric; focus on 8-12 that directly tie to business outcomes and decisions you actually make.
- 3.Ask the AI to specify data sources and calculation methods explicitly. This prevents ambiguity when handing the spec to your analytics team and catches data availability issues early.
- 4.Request implementation phases rather than one massive dashboard. Phase 1 should be buildable in 2-4 weeks with existing tools; phases 2-3 can include more sophisticated metrics and integrations.
Example Output
# Marketing Dashboard Specification: SaaS Growth Dashboard
**Purpose:** Provide executive visibility into lead generation pipeline, customer acquisition efficiency, and marketing ROI across all channels for weekly leadership reviews.
**Recommended Layout:**
- Top row: Total MQLs (gauge), Pipeline value (number), CAC (trend), Marketing ROI (gauge)
- Middle row: Lead source breakdown (pie), Conversion funnel (waterfall), Email performance (line chart), Website traffic (area chart)
- Bottom row: Campaign performance table, Content engagement heatmap, Team productivity scorecard, Forecast vs. actual (bar chart)
**Key Metrics:**
1. Marketing Qualified Leads (MQLs) - Definition: Leads meeting lead scoring criteria; Source: HubSpot; Calculation: Count of contacts with score >50; Visualization: Gauge with 30-day trend
2. Sales Accepted Opportunities (SAOs) - Definition: MQLs converted by sales team; Source: Salesforce; Calculation: Opportunities created from MQL source; Visualization: Line chart with monthly trend
3. Customer Acquisition Cost - Definition: Total marketing spend ÷ new customers; Source: Salesforce + Marketing spend data; Calculation: Monthly spend divided by closed deals; Visualization: Trend line with target threshold
4. Marketing-influenced Revenue - Definition: Revenue from accounts touched by marketing; Source: Salesforce attribution; Calculation: Sum of deal amounts with marketing touchpoint; Visualization: Stacked bar chart by channel
5. Content Engagement Rate - Definition: Average engagement per content piece; Source: Google Analytics + HubSpot; Calculation: Total interactions ÷ content pieces; Visualization: Ranked table
**Data Sources:** HubSpot (leads, email), Salesforce (opportunities, revenue), Google Analytics 4 (web traffic), LinkedIn Ads (social), email platform (campaign metrics)
**Refresh Frequency:** Daily for real-time metrics; Weekly for attribution and revenue data
**Phase 1 Priority:** MQLs, SAOs, CAC, website traffic, email performance
**Tools Needed:** Looker Studio or Tableau for visualization; data warehouse or ETL tool for consolidation
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Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
Trusted by 10,000+ Directors and CMOs.