fatima-olayemi/LagosExpress_Grocery-FMCG_Case_Study

LagosExpress-Grocery Analytics PostgreSQL project analyzing sales performance, customer distribution, and product profitability across Nigeria. Includes schemas and queries for customers, products, sales, and regions to support business insights. ### LagosExpress-Grocery Analytics (PostgreSQL) This repository contains a PostgreSQL-based analytics project for LagosExpress-Grocery, an FMCG company operating across multiple regions in Nigeria. The goal of this project is to analyze key business metrics such as: * Sales performance across products, regions, and time periods * Customer distribution and segmentation * Product profitability for beverages, snacks, and toiletries The database consists of four core tables: * customers – Customer demographic and profile data * products – Information on all FMCG items sold * sales – Transaction-level sales records * regions – Geographic and operational region definitions This repository includes SQL scripts, queries, and data models designed to extract insights, support reporting, and enable deeper business intelligence for LagosExpress-Grocery. As a Data Analyst that was employed at LagosExpress-Grocery, you are tasked by the stake-holders to find: **Q1. Total amount each customer spent (JOIN): Insight:** Use JOINs and SUM() to calculate total spent per customer. **Q2. All customers, even those without purchases (LEFT JOIN):** Insight: LEFT JOIN helps identify inactive customers **Q3. All products and their sales, even if not sold (RIGHT JOIN): Insight:** RIGHT JOIN shows unsold items useful for stock planning. **Q4. Total sales revenue per category (AGGREGATION):** Insight: Helps find the most profitable product categories. **Q5. Monthly sales totals for 2025 (DATE FUNCTION):** Insight: Shows revenue trends per month for 2025. **Q6. Classify customers based on spending (CASE): Insight:** Segments customers as Premium, Regular, or Low Value. **Q7. Best-performing region (WITH): Insight:** WITH statement simplifies multi-step queries. **Q8. Combine product categories and region names (UNION):** Insight: Demonstrates data merging across domains. **Q9. Beverage sales above ₦1,000 (Logical operators):** Insight: Identifies high-value transactions for analysis.