Marketing Reporting Automation Framework
Marketing AutomationintermediateClaude 3.5 Sonnet or GPT-4o. Claude excels at structured frameworks and implementation planning; GPT-4o provides faster responses and strong technical recommendations. Both handle complex multi-step workflows well.
When to Use This Prompt
Use this prompt when your marketing team spends excessive time on manual reporting and you want to free up capacity for strategy work. It's ideal when you have multiple data sources, frequent reporting cycles, or stakeholders requesting custom cuts of the same data repeatedly.
The Prompt
You are a marketing analytics expert helping me automate and standardize our monthly reporting process.
## Current Situation
I manage marketing across [NUMBER] channels including [LIST CHANNELS: e.g., paid search, social media, email, organic]. My team spends [TIME ESTIMATE] hours monthly compiling data from different platforms into reports for [STAKEHOLDER TITLES].
## Reporting Requirements
Our stakeholders need:
- Key performance metrics: [LIST YOUR KPIs: e.g., CAC, ROAS, conversion rate, pipeline influence]
- Reporting frequency: [WEEKLY/MONTHLY/QUARTERLY]
- Audience: [DESCRIBE AUDIENCE: e.g., C-suite, board, regional managers]
- Current format: [DESCRIBE: e.g., PowerPoint deck, Google Sheets, Tableau dashboard]
- Comparison periods: [e.g., month-over-month, year-over-year, vs. target]
## Data Sources
Our data comes from: [LIST TOOLS: e.g., Google Analytics 4, HubSpot, Salesforce, Meta Ads Manager, LinkedIn Campaign Manager]
## Your Task
Create a detailed automation strategy that includes:
1. **Data Integration Plan**: Recommend specific tools or APIs to consolidate data from our sources with minimal manual work. Consider [BUDGET CONSTRAINTS if any].
2. **Metrics Dashboard Template**: Design a dashboard structure showing which metrics go where, refresh frequency, and alert thresholds for anomalies.
3. **Report Generation Workflow**: Outline step-by-step how to automate report creation—from data pull to final delivery. Include tools like Zapier, Make, or native integrations.
4. **Narrative Layer**: Suggest how to add automated insights and explanations to raw data (e.g., "Traffic dropped 15% due to [identified reason]").
5. **Implementation Roadmap**: Break this into phases with realistic timelines and resource requirements.
6. **Cost-Benefit Analysis**: Estimate time saved monthly and tool costs.
Be specific and actionable. Assume my team has [TECHNICAL SKILL LEVEL: basic/intermediate/advanced] technical skills.
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Tips for Best Results
- 1.Fill in all bracketed placeholders with your actual data sources, KPIs, and team constraints—generic answers produce generic strategies. Be specific about your current pain points.
- 2.If you have a technical person on your team, ask them to review the tool recommendations before implementation to ensure compatibility with your existing stack.
- 3.Start with one automated report (e.g., weekly dashboard) rather than automating everything at once. Build confidence and refine before scaling.
- 4.Request the prompt include specific alert thresholds and anomaly definitions relevant to your business—this prevents alert fatigue and makes automation actionable.
Example Output
## Automation Strategy for Your Marketing Reporting
### 1. Data Integration Plan
Recommend implementing Google Analytics 4 + HubSpot as your core data layer, connected via Zapier or native integrations. For paid channels, use platform APIs (Meta Conversions API, Google Ads API) to pull data hourly into a central warehouse like Google Sheets, Airtable, or Supermetrics.
**Recommended Stack**: Supermetrics (consolidates GA4, Ads, social) → Google Sheets or Looker Studio (visualization) → Zapier (automation triggers)
### 2. Metrics Dashboard Template
Create a tiered dashboard:
- **Executive Dashboard**: 5-7 headline metrics (pipeline generated, CAC, ROAS, MQL volume, conversion rate)
- **Channel Dashboard**: Performance by channel with spend, impressions, conversions, CPC
- **Anomaly Alerts**: Automated flags when metrics deviate >20% from 30-day average
### 3. Report Generation Workflow
- **Daily**: Automated data refresh from all platforms (8 AM)
- **Weekly**: Looker Studio report auto-generates and emails stakeholders every Monday
- **Monthly**: Zapier triggers a Google Docs template population with month-over-month comparisons
### 4. Narrative Layer
Use ChatGPT API or Claude integration to auto-generate 2-3 sentence insights: "Paid search ROAS increased 12% due to keyword optimization implemented on [date]. Email open rates declined 8%, likely from [segment] unsubscribes."
### 5. Implementation Roadmap
- **Phase 1 (Week 1-2)**: Audit all data sources, document current metrics
- **Phase 2 (Week 3-4)**: Set up Supermetrics + Looker Studio dashboard
- **Phase 3 (Week 5-6)**: Build Zapier workflows for weekly/monthly reports
- **Phase 4 (Week 7-8)**: Test with stakeholders, refine
### 6. Cost-Benefit Analysis
- **Time Saved**: 12-15 hours/month (estimated)
- **Tool Costs**: $200-400/month (Supermetrics, Zapier, Looker Studio)
- **ROI**: Pays for itself in 1-2 months through freed-up analyst time
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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.