# E-Commerce High-Value Customer & Fraud Analytics

A lightweight SQL case study focused on extracting business intelligence, identifying customer engagement gaps, and surfacing transactional risks using intermediate-to-advanced relational query patterns.

## 🛠️ Advanced SQL Concepts Covered
* **Common Table Expressions (CTEs):** Decoupling and structural modularity for global aggregations.
* **Multi-Value Subqueries (`IN` / `NOT IN`):** Cross-table data validation and list-based exclusion filters.
* **Data Auditing & Analysis:** Writing production-grade reporting queries for risk management and growth marketing sectors.

## 📂 Project Architecture
* `schema.sql`: Sets up the relational schema boundaries (`CUSTOMERS`, `ORDERS`, `PAYMENTS`) and initializes sample records.
* `analytics_queries.sql`: Houses the structural SQL queries dedicated to fixing specific target business issues.

## 🚀 Execution & Summary Results

### 1. High Value VIP Identification
Identifies orders generating higher revenue than the calculated platform benchmark average.
* **Result:** `CUSTOMER_ID: 2` generated a high-tier order of `$2500.00`.

### 2. Customer Re-engagement List
Extracts profiles who signed up but have not committed to their first platform transaction.
* **Result:** Identified `David` (`France`) as an inactive account.

### 3. Payment Failure Risk Report
Pulls order values mapping directly to unsuccessful payment gateways for financial auditing.
* **Result:** Flagged `ORDER_ID: 1004` (`$150.00`) due to a transaction state of `FAILED`.