E-commerce Sales Analysis

Table of contents

• Project Overview
• Data Source
• Tools
• Data Cleaning Process
• Exploratory Data Analysis
• Data Analysis
• Result
• Recommendations

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📝Project Overview

This project focuses on analyzing and visualizing restaurant sales data to uncover trends, measure performance, and provide actionable business insights

Data Source

The dataset used for this project contains restaurant sales transaction records, including:

• Order details (Order ID, Date, Product Category, Item Name, Quantity)

• Pricing information (Unit Price, Total Price)

• Customer information (Customer ID)

The data was provided as a CSV file and served as the basis for cleaning, transformation, and visualization.

🛠Tools

• MySQL – For data cleaning, transformation, and exploratory analysis.

• Tableau – For creating interactive dashboards and visualizations.

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🧹 Data Cleaning Process

The raw dataset underwent several cleaning and preparation steps to ensure accuracy and consistency:

1. Data Import & Inspection
2. Handling Missing & Invalid Data
3. Data Type Formatting
4. Duplicate Removal
5. Standardization
6. Calculated Fields Creation

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🔍Exploratory Data Analysis

• Which category of products contributes the most to total sales?
• What is the monthly trend of total sales and total orders?
• Who are the top 10 customers by total purchase amount?

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📊Data Analysis

Code worked with

SELECT 
    SUM(Price) AS total_sales,
    AVG(Price) AS avg_price_per_item,
    SUM(Price) / COUNT(DISTINCT ID) AS avg_order_value
FROM restaurant_sales_data2;

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📈Result

This query calculates three key metrics from the restaurant_sales_data2 table:

• Total Sales – The sum of all item prices (SUM(Price)), representing total revenue.

• Average Price per Item – The mean price of individual items (AVG(Price)).

• Average Order Value (AOV) – The total sales divided by the number of unique orders (SUM(Price) / COUNT(DISTINCT ID)), showing the average revenue per order.

It’s basically giving you an overall revenue snapshot with both pricing and order value insights.

💡Recommendations

Based on the analysis of the sales dataset, the following strategies are recommended to

• Introduce bundle offers and combo deals to encourage higher spending per order
• Offer free delivery or discounts for orders above a specific purchase threshold.
• Identify and remove low-performing items to focus resources on high-demand, high-margin products.
• Implement a loyalty program to reward repeat customers.
• Train staff to recommend add-ons and upgrades during the ordering process.
• Integrate “You may also like” suggestions into online ordering platforms.