- 📈 100,000 Leads Analyzed
- 🎯 25,000 Opportunities Evaluated
- 👥 10,000 Customers Modeled
- 💰 $2.0B Revenue Pipeline
- 🏆 $342.8M Revenue Generated
- 📊 4 Executive Dashboards
- ⚡ PostgreSQL Data Warehouse
- 📈 Power BI Executive Analytics Platform
The Revenue Operations Intelligence Platform is an end-to-end analytics solution designed to provide complete visibility across the revenue lifecycle—from lead acquisition and pipeline creation to sales performance, customer value, and product contribution.
Built using Python, PostgreSQL, SQL, and Power BI, this project simulates a modern Revenue Operations (RevOps) environment by integrating Marketing, Sales, and Customer Intelligence into a unified analytics platform.
The solution enables executives to:
✅ Monitor pipeline health and revenue growth
✅ Analyze funnel conversion efficiency
✅ Evaluate sales rep and territory performance
✅ Understand customer profitability
✅ Assess product contribution to revenue
✅ Support data-driven revenue decisions
The Revenue Operations Intelligence Platform provides a single source of truth across Marketing, Sales, and Customer Intelligence.
The solution enables revenue leaders to:
- Identify funnel leakage across the Lead → MQL → SQL → Won lifecycle
- Evaluate sales rep productivity and conversion effectiveness
- Benchmark territory-level performance
- Understand customer and product revenue concentration
- Monitor executive KPIs through interactive dashboards
| 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% |
Revenue organizations often struggle with fragmented reporting across Marketing, Sales, and Customer Success teams.
Common challenges include:
- Limited visibility into funnel conversion performance
- Difficulty identifying top-performing territories
- Lack of customer profitability insights
- Disconnected pipeline and sales reporting
- Inability to quickly identify revenue growth opportunities
This project solves these challenges through a centralized Revenue Operations Intelligence Platform.
Python Data Generation
↓
Raw CSV Files
↓
PostgreSQL Data Warehouse
↓
SQL Analytics Layer
↓
Power BI Semantic Model
↓
Executive Analytics Platform
The platform follows a Star Schema design optimized for analytical reporting and time intelligence calculations.
| Table | Purpose |
|---|---|
| fact_leads | Lead generation and funnel activity |
| fact_opportunities | Revenue and pipeline tracking |
| Table | Purpose |
|---|---|
| dim_customer | Customer attributes |
| dim_product | Product catalog |
| dim_sales_rep | Sales representative information |
| dim_date | Time intelligence and reporting calendar |
- Star Schema architecture
- Single-direction filtering
- Optimized DAX performance
- Time intelligence enabled
- Scalable analytical structure
| Layer | Technology |
|---|---|
| Data Generation | Python |
| Data Storage | CSV |
| Data Warehouse | PostgreSQL |
| Data Modeling | Star Schema |
| Analytics Layer | SQL |
| BI & Visualization | Power BI |
| Business Logic | DAX |
| Time Intelligence | DAX Calendar Table |
- Revenue Analytics
- Funnel Performance Analysis
- Pipeline Management
- Conversion Optimization
- Territory Performance
- Executive KPI Reporting
- Dashboard Development
- Data Visualization
- Insight Generation
- ETL Development
- PostgreSQL
- SQL Analytics
- Data Warehousing
- Dimensional Modeling
- DAX
- Time Intelligence
- Star Schema Design
- Dynamic Insights
- Performance Optimization
The platform is structured into four executive dashboards:
| Dashboard | Purpose |
|---|---|
| Executive Overview | Revenue health monitoring |
| Funnel Analytics | Conversion optimization |
| Sales Intelligence | Territory & rep performance |
| Customer Intelligence | Product and customer analytics |
💰 Total Pipeline
💵 Booked Revenue
🎯 Win Rate
👥 Average Deal Size
📅 Average Sales Cycle
- Enterprise CRM contributes approximately 70% of booked revenue
- North America generates the highest revenue contribution
- Win rate indicates opportunity for conversion optimization
- Pipeline exceeds $2 Billion
- Revenue exceeds $342 Million
📈 Total Leads
🎯 Lead → MQL Conversion
🔄 MQL → SQL Conversion
🏆 SQL → Won Conversion
- Referral leads deliver the highest win rate
- Organic Search drives the highest lead volume
- Webinar leads underperform relative to other sources
- Funnel leakage opportunities identified between Lead and SQL stages
💰 Total Revenue
🏅 Won Deals
👥 Average Deal Size
📅 Average Sales Cycle
🎯 Win Rate
- North America is the highest-performing territory
- Leonard Rice is the top revenue-producing sales representative
- Average deal size exceeds $80K
- Sales cycle averages 96 days
- Territory performance varies significantly across regions
👥 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
Revenue leaders lacked a centralized analytics platform capable of monitoring funnel performance, sales effectiveness, customer value, and product contribution across the revenue lifecycle.
Design an end-to-end Revenue Operations Intelligence Platform capable of consolidating operational data into executive-ready dashboards.
- Generated realistic RevOps datasets using Python
- Built a PostgreSQL dimensional warehouse
- Designed analytical SQL views
- Created a Power BI semantic model
- Developed DAX measures and KPIs
- Built four executive dashboards
- Implemented dynamic business insights
Delivered an integrated analytics solution capable of analyzing:
- 100,000 Leads
- 25,000 Opportunities
- 10,000 Customers
- $2.0B Revenue Pipeline
- $342.8M Revenue
SELECT
territory,
SUM(actual_revenue) AS revenue
FROM fact_opportunities
WHERE won = TRUE
GROUP BY territory
ORDER BY revenue DESC;SELECT
lead_source,
COUNT(*) AS leads,
SUM(CASE WHEN mql_flag THEN 1 ELSE 0 END) AS mqls,
SUM(CASE WHEN sql_flag THEN 1 ELSE 0 END) AS sqls,
SUM(CASE WHEN converted THEN 1 ELSE 0 END) AS converted
FROM fact_leads
GROUP BY lead_source;
Win Rate =
DIVIDE(
[Won Deals],
[Total Opportunities]
)
Average Deal Size =
DIVIDE(
[Total Revenue],
[Won Deals]
)
Revenue YoY % =
DIVIDE(
[Total Revenue] - [Revenue LY],
[Revenue LY]
)
Lead to Won Conversion % =
DIVIDE(
[Won Deals],
[Total Leads]
)
| Metric | Value |
|---|---|
| Total Leads | 100,000 |
| Total Opportunities | 25,000 |
| Total Customers | 10,000 |
| Total Pipeline | $2.0B |
| Total Revenue | $342.8M |
| Win Rate | 17.0% |
| Average Deal Size | $80.5K |
| Average Sales Cycle | 96 Days |
- Increase investment in Referral acquisition channels
- Improve Webinar lead qualification processes
- Optimize Lead → MQL conversion workflows
- Replicate North America sales strategies across regions
- Leverage top-performing sales reps for coaching initiatives
- Reduce sales cycle duration in lower-performing territories
- Focus on Enterprise customer acquisition
- Expand adoption of Enterprise CRM products
- Prioritize high-value customer segments
- Pipeline Analytics
- Funnel Analysis
- Sales Performance Measurement
- Customer Revenue Analytics
- Business Intelligence
- KPI Development
- Executive Reporting
- Dashboard Design
- ETL Development
- Data Warehousing
- Data Modeling
- SQL Analytics
- DAX
- Time Intelligence
- Dynamic Insights
- Interactive Dashboards
Revenue-Operations-Intelligence-Platform/
│
├── assets/
│ ├── architecture.png
│ ├── data_model.png
│ ├── page1.png
│ ├── page2.png
│ ├── page3.png
│ └── page4.png
│
├── dashboard/
│ └── Revenue_Operations_Intelligence.pbix
│
├── sql/
│ ├── schema.sql
│ ├── create_views.sql
│ └── revenue_kpis.sql
│
├── src/
│ ├── generate_customers.py
│ ├── generate_leads.py
│ ├── generate_opportunities.py
│ └── etl/
│
├── data/
│ ├── raw/
│ └── processed/
│
├── requirements.txt
└── README.md
- Revenue Forecasting
- Customer Lifetime Value (CLV)
- Marketing Mix Modeling
- Territory Optimization
- Sales Capacity Planning
- Predictive Lead Scoring
- Churn Analytics
Business Intelligence • Revenue Analytics • Commercial Intelligence • Data Analytics
🔗 LinkedIn: www.linkedin.com/in/abodunrin-oketade
🔗 GitHub: https://github.com/Richie-Rokka