🧠 Task 6 - Sales Trend Analysis using SQL (Online Sales Dataset)

In this task, I performed a Sales Trend Analysis using SQL on a dataset named online_sales.
The goal was to uncover key sales insights and trends from an e-commerce-like dataset of 100 orders.

---

📂 Dataset Used

• File Name: online_sales_100.xlsx
• Imported Table: online_sales
• Database Name: task6_sales
• Total Records: 100 rows

---

🛠️ Tools & Technologies

• SQL (MySQL Workbench)
• Excel (for initial dataset cleaning)
• GitHub (for version control and submission)
• Word (for screenshots and documentation)

---

📌 Objectives

The main aim of this task was to perform:

• Sales trend analysis
• Revenue aggregation by month, product, and category
• Customer behavior patterns
• And extract actionable insights

---

🔍 Key SQL Queries & What They Do

#	Query Title	Description
1️⃣	Total Sales Amount	Calculates total revenue from all orders
2️⃣	Total Orders	Counts the total unique order IDs
3️⃣	Total Quantity Sold	Sums up all quantities sold
4️⃣	Sales by Product	Shows revenue per product
5️⃣	Sales by Category	Summarizes revenue per category
6️⃣	Monthly Sales Trend	Extracts month-wise revenue trends
7️⃣	Orders per Day	Number of orders placed per day
8️⃣	Monthly Revenue Summary	Revenue by month using EXTRACT()
9️⃣	Top Performing Month	Highest revenue-generating month

📷 All screenshots of these queries + outputs are included in the Word file:
Task 6 Screenshots of Queries.docx

---

📈 Sample Insight Highlights

• Top Product: The highest earning product was XYZ (from sales query).
• Peak Month: Most revenue was generated in April 2023.
• Sales Trend: A steady increase in revenue was observed over the first 4 months of 2023.
• Category Comparison: Electronics had the maximum share in total revenue.

---

📁 Files Included

• task6_sales_analysis.sql → All SQL queries used in this task
• online_sales_100.xlsx → Dataset with 100 rows
• Task 6 Screenshots of Queries.docx → Screenshots of each query + output
• README.md → This file (for GitHub)
• Interview Q/A

---

✅ Final Notes

This task helped strengthen my SQL skills in real-world business analysis.
It also gave me hands-on experience in:

• Writing optimized aggregation queries
• Extracting date-based patterns
• Summarizing performance insights for decision making

Thanks for reading! 😊
Feel free to explore the queries or reach out for any suggestions.

---

🔗 www.linkedin.com/in/somyasinha100