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🚀 Revenue Operations Intelligence Platform

📌 Project Highlights

  • 📈 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

Power BI PostgreSQL Python SQL DAX

📖 Executive Summary

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

🎯 Business Impact

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

Outcomes Delivered

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%

🎯 Business Problem

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.

🏗️ Solution Architecture

Architecture Flow

Python Data Generation
        ↓
Raw CSV Files
        ↓
PostgreSQL Data Warehouse
        ↓
SQL Analytics Layer
        ↓
Power BI Semantic Model
        ↓
Executive Analytics Platform

🗄️ Data Model

The platform follows a Star Schema design optimized for analytical reporting and time intelligence calculations.

Fact Tables

Table Purpose
fact_leads Lead generation and funnel activity
fact_opportunities Revenue and pipeline tracking

Dimension Tables

Table Purpose
dim_customer Customer attributes
dim_product Product catalog
dim_sales_rep Sales representative information
dim_date Time intelligence and reporting calendar

Model Design Principles

  • Star Schema architecture
  • Single-direction filtering
  • Optimized DAX performance
  • Time intelligence enabled
  • Scalable analytical structure

⚙️ Technology Stack

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

🛠 Skills Demonstrated

Revenue Operations

  • Revenue Analytics
  • Funnel Performance Analysis
  • Pipeline Management
  • Conversion Optimization
  • Territory Performance

Business Intelligence

  • Executive KPI Reporting
  • Dashboard Development
  • Data Visualization
  • Insight Generation

Data Engineering

  • ETL Development
  • PostgreSQL
  • SQL Analytics
  • Data Warehousing
  • Dimensional Modeling

Power BI

  • DAX
  • Time Intelligence
  • Star Schema Design
  • Dynamic Insights
  • Performance Optimization

🧠 Analytics Framework

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

📊 Dashboard 1 — Executive Performance Overview

Key KPIs

💰 Total Pipeline

💵 Booked Revenue

🎯 Win Rate

👥 Average Deal Size

📅 Average Sales Cycle

Business Insights

  • 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

🔄 Dashboard 2 — Funnel Performance & Conversion Analytics

Key KPIs

📈 Total Leads

🎯 Lead → MQL Conversion

🔄 MQL → SQL Conversion

🏆 SQL → Won Conversion

Business Insights

  • 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

🏆 Dashboard 3 — Sales Performance & Territory Intelligence

Key KPIs

💰 Total Revenue

🏅 Won Deals

👥 Average Deal Size

📅 Average Sales Cycle

🎯 Win Rate

Business Insights

  • 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

👥 Dashboard 4 — Product & Customer Intelligence

Key KPIs

👥 Total Customers

💰 Revenue Per Customer

📊 Average Customer Value

🏢 Enterprise Revenue Share

📦 Top Product Revenue Share

🔄 Average Products Per Customer

Business Insights

  • 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

📖 Project Case Study

Situation

Revenue leaders lacked a centralized analytics platform capable of monitoring funnel performance, sales effectiveness, customer value, and product contribution across the revenue lifecycle.

Task

Design an end-to-end Revenue Operations Intelligence Platform capable of consolidating operational data into executive-ready dashboards.

Action

  • 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

Result

Delivered an integrated analytics solution capable of analyzing:

  • 100,000 Leads
  • 25,000 Opportunities
  • 10,000 Customers
  • $2.0B Revenue Pipeline
  • $342.8M Revenue

🔍 SQL Analytics Examples

Territory Revenue Analysis

SELECT
    territory,
    SUM(actual_revenue) AS revenue
FROM fact_opportunities
WHERE won = TRUE
GROUP BY territory
ORDER BY revenue DESC;

Funnel Conversion Analysis

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;

⚡DAX Measure Examples

Win Rate

Win Rate =
DIVIDE(
    [Won Deals],
    [Total Opportunities]
)

Average Deal Size

Average Deal Size =
DIVIDE(
    [Total Revenue],
    [Won Deals]
)

Revenue YoY %

Revenue YoY % =
DIVIDE(
    [Total Revenue] - [Revenue LY],
    [Revenue LY]
)

Lead → Won Conversion

Lead to Won Conversion % =
DIVIDE(
    [Won Deals],
    [Total Leads]
)

📈 Revenue Funnel Summary

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

💡 Key Business Recommendations

🎯 Funnel Optimization

  • Increase investment in Referral acquisition channels
  • Improve Webinar lead qualification processes
  • Optimize Lead → MQL conversion workflows

🏆 Sales Performance

  • Replicate North America sales strategies across regions
  • Leverage top-performing sales reps for coaching initiatives
  • Reduce sales cycle duration in lower-performing territories

👥 Customer Growth

  • Focus on Enterprise customer acquisition
  • Expand adoption of Enterprise CRM products
  • Prioritize high-value customer segments

📊 Skills Demonstrated

Revenue Operations

  • Pipeline Analytics
  • Funnel Analysis
  • Sales Performance Measurement
  • Customer Revenue Analytics

Data Analytics

  • Business Intelligence
  • KPI Development
  • Executive Reporting
  • Dashboard Design

Data Engineering

  • ETL Development
  • Data Warehousing
  • Data Modeling
  • SQL Analytics

Power BI

  • DAX
  • Time Intelligence
  • Dynamic Insights
  • Interactive Dashboards

📂 Repository Structure

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

🚀 Future Enhancements

  • Revenue Forecasting
  • Customer Lifetime Value (CLV)
  • Marketing Mix Modeling
  • Territory Optimization
  • Sales Capacity Planning
  • Predictive Lead Scoring
  • Churn Analytics

👨‍💻 Author

Abodunrin Oketade

Business Intelligence • Revenue Analytics • Commercial Intelligence • Data Analytics

🔗 LinkedIn: www.linkedin.com/in/abodunrin-oketade

🔗 GitHub: https://github.com/Richie-Rokka

⭐ If you found this project interesting, consider starring the repository.