End-to-end business intelligence project demonstrating SQL proficiency, data visualization, and business insight generation.

→ View Interactive Dashboards on Tableau Public

This project transforms a PostgreSQL e-commerce database into actionable business intelligence dashboards, showcasing:

• Advanced SQL: Window functions, aggregations, CTEs, and data transformations
• Business Analytics: Revenue analysis, customer segmentation, and churn detection
• Data Visualization: Interactive Tableau dashboards with professional design
• Insight Generation: Actionable recommendations backed by data

🚨 88.72% revenue drop in February 2025 requires immediate investigation
⚠️ 62% one-time buyers indicate severe retention challenge
⚠️ 46% revenue concentration in just 2 customers creates business risk
📈 Actionable recommendations for retention, growth, and risk mitigation

├── 00-Data-Sources/           # Raw and processed CSV data
│   ├── raw/                   # Direct database exports
│   ├── processed/             # Pre-aggregated views for Tableau
│   ├── data-dictionary.md     # Complete data reference
│   └── README.md
├── 01-SQL-Queries/            # All SQL code
│   ├── export-queries.sql     # Database to CSV exports
│   ├── aggregation-queries.sql # Pre-aggregation logic
│   ├── calculated-metrics.sql # Business metric definitions
│   └── README.md
├── 02-Tableau-Workbooks/      # Dashboard specifications
│   ├── dashboard-specs.md     # Technical reproduction guide
│   ├── tableau-calculations.md # Calculated field documentation
│   └── README.md
├── 03-Dashboards/             # Visual documentation & insights
│   ├── 01-Revenue-Overview/   # Revenue dashboard screenshots
│   ├── 02-Customer-Analytics/ # Customer dashboard screenshots
│   └── README.md
├── 04-Documentation/          # Process & methodology
│   ├── sql-to-tableau-workflow.md # Step-by-step guide
│   ├── methodology.md         # Analysis framework
│   ├── business-context.md    # Real-world applications
│   ├── technical-implementation.md # Architecture decisions
│   └── README.md
├── 05-Resources/              # Additional resources
│   ├── tableau-public-profile.md # Optimizing Tableau Socials
│   └── related-projects.md    # Portfolio context
├── README.md                  # This file
├── QUICKSTART.md             # 5-minute quick start guide
├── LICENSE                   # MIT License
└── .gitignore

Want to jump right in? See QUICKSTART.md for a 5-minute overview.

1. Visit Tableau Public Link
2. Explore interactive visualizations
3. Review insights in 03-Dashboards/

1. Browse SQL queries in 01-SQL-Queries/
2. Check dashboard specifications in 02-Tableau-Workbooks/
3. Read documentation in 04-Documentation/

Prerequisites:

• PostgreSQL installed OR VS Code with PostgreSQL extension
• Tableau Public installed
• SQL-Portfolio-105-Problems database loaded

Steps:

1. Follow 04-Documentation/sql-to-tableau-workflow.md
2. Estimated time: 6-8 hours (experienced analyst)

Business Question: How is the business performing financially?

Key Features:

• 📊 KPI cards: Revenue, orders, AOV, customers
• 📉 Monthly revenue trend with MoM growth annotations
• 📂 Revenue breakdown by product category
• 👥 Top 10 customers by lifetime value
• 📆 Customer acquisition trend over time

Critical Insight: 88.72% revenue drop from January to February 2025 signals operational crisis

→ View Full Dashboard

Business Question: Who are our customers and what drives their value?

Key Features:

• 🎯 Customer LTV scatter plot (segmented by tier)
• 🍩 Customer tier distribution (Platinum/Gold/Silver/Bronze)
• 🔥 Churn risk heatmap (by recency and frequency)
• 📋 Top customers table with risk indicators

Critical Insight: 62% of customers made only one purchase—severe retention problem

→ View Full Dashboard

This project applies solutions from SQL-Portfolio-105-Problems:

• Problem I11: Month-over-month growth with LAG()
• Problem A32: Customer lifetime value calculation
• Problem A17: Revenue percentile ranking

• Problem B9: Total revenue (SUM)
• Problem B10: Average order value
• Problem B13: Units sold per product

• Problem A6: Category revenue pivot (CROSSTAB)
• Problem I3: Customer segmentation logic
• Problem B26: Churn detection (date intervals)
• Problem B29: Order frequency analysis

→ See Complete SQL Problem Mapping

→ View Detailed Insights

✅ SQL Proficiency

• Complex queries with window functions
• Data aggregation and transformation
• Performance optimization strategies

✅ Data Visualization

• Dashboard design principles
• Interactive visualizations
• Color theory and visual hierarchy

✅ Business Acumen

• Insight generation from data
• Actionable recommendations
• Risk identification and quantification

✅ Communication Skills

• Technical and business documentation
• Visual storytelling
• Stakeholder-ready deliverables

This project shows real-world readiness:

• End-to-end analytics workflow (database → insights)
• Professional documentation standards
• Reproducible and transparent process
• Business value focus (not just technical skills)

→ Read Business Context

Comprehensive documentation covering every aspect of the project:

• sql-to-tableau-workflow.md - Step-by-step reproduction guide
• methodology.md - Analysis framework and approach
• business-context.md - Real-world applications and use cases
• technical-implementation.md - Architecture and design decisions

• SQL-Portfolio-105-Problems - Foundation SQL skills
• More Analytics Projects - Other portfolio work

Lenny Success Humphrey

• Tableau Public: View Profile
• GitHub: @LennySHumphrey
• LinkedIn: Connect with me

This project is licensed under the MIT License - see the LICENSE file for details.

• Data Source: Synthetic e-commerce data from SQL-Portfolio-105-Problems
• Tools: PostgreSQL, Tableau Public
• Inspiration: Real-world business intelligence challenges

Questions about this project? Want to discuss analytics?

• Email: humphreysuccess4@gmail.com
• LinkedIn: Connect With Me
• Tableau Public: Visit My Tableau Public Profile
• Portfolio: Visit My Portfolio

⭐ If you found this project helpful, please give it a star on GitHub!

Last updated: January 2026