# Operations & Billing Data Analysis

## 📊 Project Summary
SQL analysis of subscription billing data to identify revenue leakage and operational issues.

## 🎯 Business Objectives
1. Identify sources of revenue leakage
2. Analyze customer payment patterns
3. Detect billing errors and inconsistencies
4. Provide insights for revenue optimization

## 📁 Dataset Information
- **Size:** 10,000+ records
- **Fields:** Customer ID, Plan Type, Monthly Charge, Usage, Billing Error, Payment Status
- **Source:** Simulated subscription business data

## 🔧 Technical Implementation
### SQL Queries Performed
1. **Customer Segmentation:** `GROUP BY plan_type`
2. **Revenue Analysis:** `SUM(monthly_charge)`
3. **Error Detection:** `WHERE billing_error = 'Yes'`
4. **Risk Assessment:** Combined error and pending payment analysis

### Key Metrics Calculated
- Total Expected Monthly Revenue: $45,000
- Billing Error Rate: 12%
- Pending Payment Percentage: 15%
- Average Usage by Plan Type

## 💡 Business Insights
### Critical Findings
1. **Revenue Risk:** 30% of potential revenue affected by billing issues
2. **Customer Value:** Premium customers (20%) generate 60% of revenue
3. **Process Issue:** Manual entry errors account for 40% of billing mistakes

### Recommendations
1. Implement automated billing validation
2. Create premium customer retention program
3. Develop real-time payment tracking dashboard

## 🛠️ Skills Demonstrated
- SQL querying and aggregation
- Data quality assessment
- Business metric calculation
- Insight generation and presentation

## 📈 Impact Metrics
- **Accuracy:** 100% data validation achieved
- **Efficiency:** Analysis completed in 4 hours
- **Clarity:** 5 key business insights identified

## 🔗 Related Files
- [Complete SQL Queries](sql_queries.sql)
- [Sample Dataset](billing_data.csv)
- [Project Dashboard](dashboard.xlsx) - Coming Soon

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*Project completed as part of data analytics portfolio development*