An end-to-end Revenue Operations (RevOps) analytics platform that transforms lead, opportunity, customer, product, and sales data into executive insights across the revenue lifecycle.
Built with Python, PostgreSQL, SQL, Power BI, and DAX, the platform provides a unified view of funnel performance, pipeline health, sales effectiveness, customer value, and product contribution.
Note on Data: This project uses a synthetic dataset generated to simulate a realistic B2B revenue operations environment. All companies, customers, sales activity, pipeline values, revenue figures, and business outcomes are simulated for analytical and portfolio demonstration purposes.
| Metric | Value |
|---|---|
| Leads Analyzed | 100,000 |
| Opportunities Evaluated | 25,000 |
| Customers Modeled | 10,000 |
| Revenue Pipeline | $2.0B |
| Revenue Generated | $342.8M |
| Win Rate | 17.0% |
| Average Deal Size | $80.5K |
| Average Sales Cycle | 96 Days |
| Executive Dashboards | 4 |
Revenue organizations often operate with fragmented reporting across Marketing, Sales, and Customer teams.
This makes it difficult to answer critical questions such as:
- Where is revenue leaking through the funnel?
- Which acquisition channels produce the highest-quality opportunities?
- Which territories and sales representatives drive the strongest performance?
- Which customers and products contribute the most revenue?
- How healthy is the current revenue pipeline?
The objective of this project was to build a centralized analytics platform that brings these perspectives together into a single Revenue Operations view.
The platform integrates the revenue lifecycle from lead generation through opportunity conversion and customer revenue analysis.
It enables stakeholders to:
- Monitor pipeline health and revenue performance
- Analyze funnel conversion from Lead to Won
- Identify acquisition channels with stronger conversion outcomes
- Evaluate sales representative and territory performance
- Understand customer and product revenue concentration
- Track executive KPIs through interactive Power BI dashboards
The result is a unified analytics environment designed to support more informed revenue decisions.
Python Data Generation
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Raw CSV Datasets
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PostgreSQL Data Warehouse
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SQL Analytics Layer
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Power BI Semantic Model
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Executive Analytics & Insights
The architecture separates data generation, storage, analytics, semantic modeling, and visualization into distinct layers.
The platform uses a star schema designed for analytical reporting and Power BI performance.
| Table | Purpose |
|---|---|
fact_leads |
Lead generation and funnel activity |
fact_opportunities |
Pipeline, opportunity, and revenue tracking |
| Table | Purpose |
|---|---|
dim_customer |
Customer attributes and segmentation |
dim_product |
Product information |
dim_sales_rep |
Sales representative attributes |
dim_date |
Time intelligence and reporting calendar |
- Star schema architecture
- Single-direction filtering
- Analytical performance optimization
- DAX-based time intelligence
- Scalable reporting structure
The Power BI solution is organized into four analytical views.
Provides a high-level view of overall revenue performance and pipeline health.
- Total Pipeline
- Booked Revenue
- Win Rate
- Average Deal Size
- Average Sales Cycle
- Pipeline exceeds $2.0B
- Generated revenue exceeds $342M
- North America is the highest-performing territory
- Enterprise CRM is the largest product contributor to booked revenue
- Win-rate performance highlights opportunities for conversion improvement
Analyzes movement through the revenue funnel from lead acquisition to won opportunities.
- Total Leads
- Lead → MQL Conversion
- MQL → SQL Conversion
- SQL → Won Conversion
- Organic Search generates the highest lead volume
- Referral leads demonstrate the strongest win rate
- Webinar leads underperform relative to other acquisition channels
- Funnel leakage opportunities are visible between Lead and SQL stages
Evaluates sales effectiveness across territories and representatives.
- Total Revenue
- Won Deals
- Average Deal Size
- Average Sales Cycle
- Win Rate
- North America is the strongest-performing territory
- Leonard Rice is the highest revenue-producing sales representative
- Average deal size exceeds $80K
- Average sales cycle is approximately 96 days
- Performance varies significantly across territories
Analyzes customer value, industry contribution, and product revenue concentration.
- Total Customers
- Revenue per Customer
- Average Customer Value
- Enterprise Revenue Share
- Top Product Revenue Share
- Average Products per Customer
- Enterprise customers contribute 55.9% of total revenue
- Enterprise CRM contributes 70.5% of total revenue
- Technology is the highest revenue-generating industry
- Customer acquisition remains consistently positive
- Increase investment in high-performing referral channels
- Improve webinar lead qualification and conversion processes
- Focus on reducing leakage between Lead and SQL stages
- Analyze and replicate successful North American sales practices
- Use top-performing representatives as benchmarks for coaching
- Investigate opportunities to reduce sales cycle duration in lower-performing territories
- Prioritize high-value enterprise customer segments
- Expand adoption of the highest-performing products
- Monitor customer and product revenue concentration to identify growth opportunities
| Layer | Technology |
|---|---|
| Data Generation | Python |
| Data Storage | CSV |
| Data Warehouse | PostgreSQL |
| Data Modeling | Star Schema |
| Analytics Layer | SQL |
| Business Intelligence | Power BI |
| Business Logic | DAX |
| Time Intelligence | DAX Calendar Table |
- Pipeline Analytics
- Funnel Performance Analysis
- Conversion Analysis
- Sales Performance Measurement
- Territory Intelligence
- Customer Revenue Analytics
- Product Performance Analysis
- Python Data Generation
- ETL Concepts
- PostgreSQL Data Warehousing
- SQL Analytics
- Dimensional Modeling
- Star Schema Design
- Executive KPI Reporting
- Power BI Dashboard Development
- DAX Measures
- Time Intelligence
- Interactive Reporting
- Business Insight Generation
Revenue-Operations-Intelligence-Platform/
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├── assets/
│ ├── architecture.png
│ ├── data_model.png
│ ├── page1.png
│ ├── page2.png
│ ├── page3.png
│ └── page4.png
│
├── data/
│ └── raw/
│
├── powerbi/
│ └── Revenue_Operations_Intelligence.pbix
│
├── sql/
│ ├── schema.sql
│ ├── create_views.sql
│ └── revenue_kpis.sql
│
├── src/
│ ├── generate_customers.py
│ ├── generate_leads.py
│ ├── generate_opportunities.py
│ └── etl/
│
├── .gitignore
└── README.md
Potential extensions to the platform include:
- Revenue Forecasting
- Customer Lifetime Value (CLV)
- Territory Optimization
- Sales Capacity Planning
- Predictive Lead Scoring
- Churn Analytics
Abodunrin Oketade
I'm interested in opportunities and conversations around Business Intelligence, Data Analytics, Commercial Analytics, Revenue Analytics, and Operational Performance.
📍 Ontario, Canada 🔗
