THIERRY-DEV-ENG/-plsql_window_functions_27884_NISHIMWE

A comprehensive business intelligence solution for a Rwanda-based e-commerce platform, implementing advanced SQL JOINs and Window Functions to analyze sales data, optimize inventory, segment customers, and drive data-informed business decisions across Rwanda's five provinces. # Rwanda E-Commerce Sales Analytics Project ### Advanced SQL Window Functions & JOINs Analysis **INSY 8311 - Database Development with PL/ SQL** **Assignment I: Window Functions & SQL JOINs** • Documentation • SQL Queries • Analysis --- ## 📋 Table of Contents - Overview - Step 1: Problem Definition - Step 2: Success Criteria - Step 3: Database Schema Design - Step 4: SQL JOINs Implementation - Step 5: Window Functions Implementation - Step 6: GitHub Repository - Step 7: Results Analysis - Step 8: References - Installation & Setup - Key Insights - Integrity Statement --- ## 🎯 Overview This project demonstrates advanced SQL analytics techniques applied to a real-world e-commerce scenario in Rwanda. Using PostgreSQL window functions and various JOIN operations, we analyze sales patterns, customer behavior, and regional performance to drive data-driven business decisions. **Project Goals:** - ✅ Master SQL window functions (Ranking, Aggregate, Navigation, Distribution) - ✅ Implement all JOIN types for comprehensive data analysis - ✅ Generate actionable business insights from sales data - ✅ Support inventory optimization and customer segmentation strategies --- ## 📊 Step 1: Problem Definition ### Business Context **Company Profile:** - **Type:** Online retail e-commerce platform - **Department:** Sales and Marketing Analytics Team - **Industry:** Retail & E-Commerce - **Specialization:** Electronics, Appliances, and Books tailored to Rwanda's digital market ### Data Challenge The company accumulates vast transaction data from customers across Rwanda's five provinces (Kigali, Northern, Southern, Eastern, and Western) but faces significant challenges: 🔴 **Current Pain Points:** - Difficulty joining customer profiles with product sales data - Unable to identify regional performance trends effectively - Inefficient stock distribution (e.g., overstocking unpopular items in rural Eastern …