This task focuses on analyzing monthly sales trends using SQL queries on the online_sales dataset. The goal is to extract meaningful business insights by calculating the total revenue and number of orders placed each month. 📊 Task 6: Sales Trend Analysis Using Aggregations
📝 Overview
This task aims to perform Sales Trend Analysis using SQL to understand monthly trends in revenue and order volume. The objective is to group transactional data by month and year, apply aggregations like SUM() and COUNT(), and sort the results for insightful visualization.
🔧 Tools Used
Database: PostgreSQL / MySQL / SQLite
Language: SQL
Output Format: PDF
Key SQL Functions Used:
EXTRACT(YEAR FROM order_date)
EXTRACT(MONTH FROM order_date)
SUM(amount)
COUNT(DISTINCT order_id)
GROUP BY, ORDER BY
📁 Files Included
File Name Description
TASK_6_Sales_Trend_Analysis.pdf Final report including SQL script and sample results table README.md (this file) Project description, tools, and instructions
🧾 SQL Script Summary
The SQL script performs the following:
Extracts the year and month from each order's order_date.
Aggregates the total revenue using SUM(amount).
Counts unique orders using COUNT(DISTINCT order_id).
Groups the data by year and month to observe trends.
Sorts the final output by chronological order.
📊 Sample Output Table
Year Month Total Revenue Total Orders
2023 1 INR 1,25,000 230 2023 2 INR 1,45,500 280 2023 3 INR 1,67,200 310 2023 4 INR 1,80,000 340
📌 Learning Outcome
Through this task, I have:
Learned how to group data using date parts (month, year).
Used aggregate functions like SUM() and COUNT() to generate business insights.
Gained confidence in writing clean and optimized SQL queries for reporting purposes.
Understood how to present SQL output in a professional PDF format for stakeholders.
*Prachee *