End-to-end analytics framework for connecting marketing spend to revenue, with governed metrics, centralized data modeling, and executive-ready dashboards.
Many organizations operate with strong systems in isolation - marketing platforms, CRM, ERP - but struggle to connect them into a single, trusted view of performance.
This project demonstrates a practical approach to:
- Integrating marketing, sales, and financial data
- Defining and governing core business metrics
- Eliminating conflicting reports and manual workflows
- Enabling clear visibility into marketing ROI and revenue drivers
Organizations often face:
- Fragmented data across systems (Google Ads, HubSpot, NetSuite)
- Manual Excel-based reporting and ad hoc analysis
- Conflicting KPI definitions across departments
- Limited visibility into true marketing ROI
- Inefficient marketing spend
- Missed revenue opportunities
- Low trust in reporting
A centralized, governed analytics foundation that:
- Establishes a single source of truth
- Standardizes KPI definitions across teams
- Connects marketing spend to revenue through consistent joins
- Delivers trusted, executive-ready dashboards
- Disconnected systems (Marketing, CRM, ERP)
- Manual integration via Excel
- Inconsistent joins (UTM, email, etc.)
- Conflicting KPIs and low trust
- Centralized data layer
- Governed joins and metric definitions
- Standardized reporting layer
- Trusted dashboards and decision-making
(See Data Flow Diagram in /docs or project files)
Days 1-30: Understand & Map
- Inventory data sources and workflows
- Identify inconsistencies and gaps
- Map current data flows
Days 30-60: Define & Align
- Define core KPIs (revenue, ROI, customer)
- Establish metric dictionary
- Reconcile conflicting fields
Days 60-90: Build & Deliver
- Create integrated dataset
- Reduce Excel-based workflows
- Deliver executive dashboards
Revenue
- Total Revenue
- Revenue Growth %
- Revenue by Product / Channel
Profitability
- Gross Margin %
- Margin by Product
Efficiency
- ROAS (Return on Ad Spend)
- CAC (Customer Acquisition Cost)
- Conversion Rate
Operations
- Inventory Turnover
This framework emphasizes:
- Metric Definitions → Clear, documented KPI logic
- Field Reconciliation → Source-of-truth alignment across systems
- Data Validation → Ongoing checks for accuracy and completeness
Together, these ensure trust, consistency, and usability.
Executive Performance Dashboard
- Revenue, Growth, Margin, ROAS
- Revenue trends and channel performance
- Product and profitability insights
Marketing ROI & Optimization Dashboard
- Spend vs Revenue (ROAS)
- Campaign performance and conversion funnel
- Optimization recommendations (scale, reduce, test)
Repository Structure
.
├── data/ # Generated datasets (ignored in git)
├── executive_data.py # Executive dashboard dataset generator
├── marketing_data.py # Marketing ROI dataset generator
├── docs/ # Diagrams, PDFs, and supporting materials
└── README.md
Generate Data
python executive_data.py
python marketing_data.py
Load into Tableau Public
- Open Tableau Public
- Connect to CSV
- Build dashboards using provided schema
- Single source of truth for core metrics
- Standardized definitions across teams
- Minimize manual workflows
- Prioritize high-impact business questions
- Build for trust and usability, not just accuracy
- Trusted, consistent reporting across departments
- Reduced manual effort and data discrepancies
- Clear visibility into marketing ROI
- Faster, more confident decision-making
This project reflects a practical, business-focused approach to analytics - combining data engineering, BI, and governance to deliver real decision-making value, not just reporting.
Author
Vesper Annstas
Data Analytics Manager | Business Intelligence | Data Strategy