Design a Marketing Data Warehouse Architecture for AI-Driven Insights
Analytics & ReportingadvancedClaude 3.5 Sonnet or GPT-4o. Both excel at architectural thinking and can produce detailed, multi-layered technical recommendations. Claude is slightly better at creating coherent frameworks; GPT-4o is faster for iterative refinement. For very large organizations, use Claude with extended thinking enabled.
When to Use This Prompt
Use this prompt when your marketing team is drowning in operational debt from disconnected data sources and manual reporting, and you need a strategic blueprint for a data warehouse that will support AI initiatives while proving ROI to the CFO. This is ideal for CMOs planning a 12-18 month data transformation or evaluating whether to build vs. buy a CDP/data platform.
The Prompt
You are a senior marketing data architect advising a CMO who needs to build a data warehouse foundation that supports AI-driven decision-making while reducing operational debt and proving marketing ROI.
## Context
Our marketing team currently operates with [NUMBER] team members across [DEPARTMENTS/FUNCTIONS]. We use approximately [NUMBER] marketing tools and data sources. Our biggest operational bottlenecks are [DESCRIBE: e.g., manual data consolidation, siloed campaign reporting, slow attribution analysis]. We need to prove AI ROI within [TIMEFRAME] by improving [PRIMARY GOAL: e.g., campaign efficiency, lead quality, revenue attribution].
## Current State Assessment
- Primary data sources: [LIST: CRM, ad platforms, email, analytics, etc.]
- Current reporting cadence: [DAILY/WEEKLY/MONTHLY]
- Team's technical capability: [BEGINNER/INTERMEDIATE/ADVANCED]
- Budget constraint: [RANGE]
- Governance requirements: [COMPLIANCE NEEDS: GDPR, CCPA, etc.]
## Design Requirements
Create a marketing data warehouse architecture that:
1. **Consolidates** customer journey data from [SPECIFIC SOURCES] into a single source of truth
2. **Enables** AI/ML models to predict [SPECIFIC OUTCOME: churn, LTV, conversion probability]
3. **Reduces** manual reporting by [TARGET %] within 6 months
4. **Supports** real-time decisioning for [USE CASE: personalization, budget allocation, audience targeting]
5. **Maintains** data governance and compliance without creating approval bottlenecks
## Deliverables
Provide:
- Architecture diagram (describe in text) showing data ingestion, transformation, storage, and activation layers
- Data model for [PRIMARY ENTITY: customer, campaign, touchpoint] with key dimensions and metrics
- Implementation roadmap: Phase 1 (quick win), Phase 2 (scale), Phase 3 (AI-ready)
- Recommended tech stack with rationale for [CLOUD PLATFORM: AWS, GCP, Azure]
- Governance framework that balances security with speed
- Success metrics tied to reducing operational debt and proving ROI
## Constraints
- Must integrate with existing [SPECIFIC TOOLS/SYSTEMS]
- Team has [SKILL LEVEL] technical expertise
- Implementation timeline: [MONTHS]
- Avoid over-engineering; prioritize quick wins that compound
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Tips for Best Results
- 1.Fill in all [BRACKETED] fields with your specific context—generic answers will be generic. The more detail on current tools and pain points, the more tailored the architecture.
- 2.Ask follow-up questions: 'What's our biggest bottleneck in campaign attribution?' or 'Which AI use case would prove ROI fastest?' to refine the roadmap.
- 3.Request a cost-benefit analysis comparing build vs. buy (Segment, mParticle, Treasure Data) to validate the DIY approach before committing.
- 4.Use the output to create a 90-day pilot focused on one high-friction workflow (e.g., weekly reporting) to prove operational debt reduction before full rollout.
Example Output
# Marketing Data Warehouse Architecture
## Recommended Architecture
**Ingestion Layer**: Implement cloud-native connectors (Fivetran, Stitch) to pull data daily from Salesforce, Google Analytics 4, HubSpot, and paid media platforms into cloud object storage (S3/GCS).
**Transformation Layer**: Use dbt (data build tool) to create dimensional models. Core tables: dim_customer (unified customer view), dim_campaign, dim_touchpoint, fact_conversion, fact_engagement. This eliminates manual consolidation and creates audit trails.
**Storage Layer**: Cloud data warehouse (Snowflake or BigQuery) optimized for both analytical queries and real-time activation. Partition by date and customer segment for performance.
**Activation Layer**: Connect warehouse to marketing automation (segment audiences), personalization engines, and BI tools (Looker, Tableau) for self-service reporting.
## Phase 1 (Months 1-3): Quick Win
Consolidate CRM + GA4 + email data. Build single customer view and campaign performance dashboard. Reduce weekly reporting time by 40%. Cost: ~$15K setup + $3K/month.
## Phase 2 (Months 4-9): Scale
Add paid media data, implement attribution modeling, enable real-time audience activation. Unlock predictive churn model. Reduce operational overhead by 60%.
## Phase 3 (Months 10-18): AI-Ready
Build customer LTV prediction, campaign response propensity models, budget optimization engine. Prove 15-25% ROI improvement through better targeting and reduced waste.
## Governance
Data stewardship council (quarterly), automated PII masking, role-based access control. No approval bottlenecks—clear ownership of each data domain.
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Get the Full AI Marketing Learning Path
Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
Trusted by 10,000+ Directors and CMOs.