Welcome to the Amazon Sales Analysis project! In this project, we delve into analyzing sales data from Amazon to extract insights and trends that can help optimize sales strategies, understand customer behavior, and improve business operations.
This project focuses on analyzing a dataset containing Amazon sales records, including information such as sales dates, customer details, product categories, and revenue figures.
The dataset used in this project consists of [insert number] rows of data, representing Amazon sales transactions. Along with the sales data, the dataset includes information about customers, products, orders, seller , category , order_items , payments , shipping , inventory . Before analysis, the dataset underwent preprocessing to handle missing values and ensure data quality.
An Entity-Relationship Diagram (ERD) has been created to visualize the relationships between the tables in the dataset. This diagram provides a clear understanding of the data structure and helps in identifying key entities and their attributes.
During the analysis, the following key questions were addressed using SQL queries and data analysis techniques:
- Top Selling Products
- Revenue by Category
- Average Order Value (AOV)
- Monthly Sales Trend
- Customers with No Purchases
- Least-Selling Categories by State
- Customer Lifetime Value (CLTV)
- Inventory Stock Alerts
- Shipping Delays
- Payment Success Rate
- Top Performing Sellers
- Product Profit Margin
- Most Returned Products
- Inactive Sellers
- IDENTITY customers into returning or new