🚀 Data Analyst Project Series – Part 6

E-Commerce Sales Analysis Project

🎯 Project Goal
The goal of this project is to analyze e-commerce business data and discover insights related to:
- Sales performance
- Customer behavior
- Product performance
- Revenue trends
- Profitability
- Order patterns

This is one of the MOST important real-world Data Analytics projects because almost every online business depends on sales analytics.

This project is widely used in:
- Amazon-like platforms
- Shopify stores
- Retail companies
- D2C brands
- Online marketplaces

🛠 STEP 1: Choose the Dataset
Recommended Dataset Types
Search on Kaggle:
- E-Commerce Sales Dataset
- Online Retail Dataset
- Superstore Sales Dataset
- Amazon Product Sales Dataset

📂 STEP 2: Understand the Dataset
Common Columns
Order ID : Unique order number
Customer ID : Unique customer identifier
Order Date : Purchase date
Product Name : Product purchased
Category : Product category
Quantity : Number of items
Sales : Revenue generated
Profit : Profit earned
Discount : Discount applied
Region : Customer region
Payment Mode : Payment method

🧹 STEP 3: Data Cleaning
E-commerce data often contains:
- Duplicate orders
- Missing customer details
- Incorrect product categories
- Invalid sales values

✔ Cleaning Tasks
Remove Duplicate Orders
Check:
- Duplicate Order IDs

Handle Missing Values
Common missing fields:
- Customer ID
- Region
- Payment Mode

Methods:
- Replace values
- Remove incomplete records

Standardize Categories
Example:
- “Electronics”
- “electronic”
- “ELEC”

Convert into one consistent format.

Correct Numeric Data
Examples:
- Sales → Decimal
- Quantity → Integer
- Discount → Percentage

📊 STEP 4: Define E-Commerce KPIs

Essential KPIs
✔ Total Sales
SUM(Sales)

✔ Total Profit
SUM(Profit)

✔ Total Orders
COUNT(Order_ID)

✔ Average Order Value (AOV)
Purpose:
Measures average customer spending.

✔ Profit Margin
Purpose:
Shows business profitability.

🗄 STEP 5: Analyze E-Commerce Data Using SQL
📌 SQL Query Examples

1. Top Selling Products

SELECT Product_Name,
       SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Product_Name
ORDER BY Total_Sales DESC
LIMIT 10;

2. Sales by Category

SELECT Category,
       SUM(Sales) AS Category_Sales
FROM Orders
GROUP BY Category
ORDER BY Category_Sales DESC;

3. Monthly Revenue Trend

SELECT MONTH(Order_Date) AS Month,
       SUM(Sales) AS Revenue
FROM Orders
GROUP BY MONTH(Order_Date)
ORDER BY Month;

4. Region-wise Profit

SELECT Region,
       SUM(Profit) AS Total_Profit
FROM Orders
GROUP BY Region
ORDER BY Total_Profit DESC;

5. Most Used Payment Methods

SELECT Payment_Mode,
       COUNT(*) AS Usage_Count
FROM Orders
GROUP BY Payment_Mode
ORDER BY Usage_Count DESC;

📈 STEP 6: Build E-Commerce Dashboard
Use:
- Power BI
- Tableau

🎨 Dashboard Layout
Section 1: KPI Cards
Display:
- Total Sales
- Total Profit
- Total Orders
- Average Order Value

Section 2: Visualizations
✔ Line Chart
Use for:
- Monthly Revenue Trends

✔ Bar Chart
Use for:
- Top Products

✔ Donut/Pie Chart
Use for:
- Sales by Category

✔ Map Visualization
Use for:
- Region-wise Sales

✔ Funnel Chart
Use for:
- Customer Purchase Journey

🎛 STEP 7: Add Dashboard Filters
Add:
✔ Region
✔ Product Category
✔ Payment Mode
✔ Date Range
✔ Customer Segment

Interactive dashboards improve business analysis.

🎨 STEP 8: Improve Dashboard Design
Design Tips
✔ Highlight important KPIs
✔ Use consistent colors
✔ Avoid cluttered visuals
✔ Keep spacing clean
✔ Add icons where needed