Online Grocer Transaction Analysis

This project explores a sample of transaction data from an online grocer using SQL and R. The goal is to perform exploratory data analysis (EDA) to generate insights into customer behavior, product performance, and sales trends.

Project Motivation

Understanding transaction patterns helps identify high-performing products, top customers, and emerging trends to inform marketing, inventory, and business strategy.

Data Overview

Dataset	Description
order_header_sample.csv	Order-level summary information
order_detail_sample.csv	Line-level order details
customer_sample.csv	Customer demographic information
lookup.csv	Product lookup table with brand info

Analysis Tasks

• Link datasets and create an Entity-Relationship Diagram (ERD).
• Calculate key metrics: total customers, total orders, mean spend per order.
• Identify top 25 products by units sold and by sales.
• Join customer and order data to analyze purchase behavior and find highest-spending customers.
• Subquery to extract order-line details for top 5 orders by sales.
• Connect product lookup to order-line details to find top brands overall and for the most recent 6 months.

Key Methods & Tools

• Methods: SQL joins, subqueries, aggregation, EDA
• Tools: R, SQL, SQLDF