📊 Customer Profitability Analysis

📌 Business Context

Understanding which customers drive the most value is critical for sustainable business growth. This project performs a full customer profitability analysis on a real UK based retail dataset, identifying high value customers, revenue concentration, and actionable segmentation insights using the Pareto principle.

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🎯 Project Objectives

• Identify top revenue generating customers
• Apply Pareto analysis to find revenue concentration
• Segment customers into High, Mid and Low value tiers
• Analyze profitability by country and product
• Build executive level dashboard for business decision making

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📊 Financial Summary

Metric	Value
Total Revenue	$8,911,407.90
Total Customers	4,338
Top 20% Customer Revenue Contribution	74.59%
Top Customer Revenue	$280,206.02
Top Country	United Kingdom
Average Order Value	$480.87
High Value Customers	867
Mid Value Customers	1,302
Low Value Customers	2,169

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📈 Key KPIs Analyzed

• Revenue per Customer
• Top 20% Customer Revenue Contribution
• Customer Segmentation (High / Mid / Low Value)
• Revenue by Country
• Revenue by Product
• Average Order Value

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🛠️ Analytical Approach

1️⃣ Data Preparation

• Removed cancelled transactions and null customers
• Removed negative quantities and prices
• Calculated revenue per transaction
• Extracted date features

2️⃣ Pareto Analysis

• Ranked customers by total revenue
• Calculated cumulative revenue contribution
• Identified top 20% customers driving 74.59% of revenue

3️⃣ Customer Segmentation

• Segmented 4,338 customers into High, Mid and Low value tiers
• Analyzed revenue contribution per segment
• Identified key revenue drivers

4️⃣ Executive Dashboard (Power BI)

• Customer overview page
• Pareto and segmentation page
• Geographic analysis page

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🔍 Key Insights

💰 Revenue Concentration

• Total revenue of $8.9M generated across the analysis period
• Top 20% of customers contribute 74.59% of total revenue
• Strong revenue concentration risk — top customer alone generates $280K

🏆 Customer Segments

• 867 High Value customers driving majority of revenue
• 1,302 Mid Value customers represent growth opportunity
• 2,169 Low Value customers require cost vs value assessment

🌍 Geographic Performance

• United Kingdom dominates revenue generation
• International expansion opportunity exists in secondary markets
• Geographic diversification could reduce revenue concentration risk

📦 Order Patterns

• Average order value of $480.87 indicates B2B style purchasing
• High value customers show significantly higher order frequency
• Mid value segment shows potential for upsell and cross sell

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💡 Business Recommendations

• Protect top 20% — implement dedicated account management for high value customers
• Grow mid value segment — targeted loyalty and upsell programs for 1,302 mid value customers
• Review low value customers — assess acquisition cost vs lifetime value for 2,169 low value customers
• Reduce concentration risk — top customer at $280K represents significant single customer dependency
• Expand internationally — leverage UK success model in secondary markets

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🛠️ Tools & Technologies

Tool	Usage
Python	Data cleaning, analysis, visualization
SQL	Customer KPI queries
Power BI	Interactive customer dashboard
pandas & matplotlib	Data manipulation and visualization

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📂 Repository Structure

customer-profitability-analysis/
│
├── data/
│   ├── raw/                          # UCI Online Retail Dataset
│   └── processed/                    # Cleaned data and segments
├── sql/
│   └── customer_kpi_queries.sql      # Customer profitability queries
├── python/
│   ├── data_cleaning.ipynb           # Data preparation
│   └── customer_analysis.ipynb       # Profitability analysis
├── powerbi/
│   └── customer_dashboard.pbix       # Executive dashboard
├── reports/
│   └── *.png                         # Exported visualizations
└── README.md

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🚀 Outcome

This project demonstrates customer analytics expertise combined with business thinking, delivering segmentation insights suitable for Business Analyst, Data Analyst and Marketing Analytics roles.

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Dataset sourced from UCI Machine Learning Repository via Kaggle.

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Commit message:

Update README with real customer profitability insights