Amazon Sales Data Analysis and Visualization

A project helping to understand and visualize the Amazon Sales data, resulting in better decision making. Business Objective: Our company is looking to optimize product sales and enhance customer satisfaction by analyzing customer purchase behavior, product performance, and user-generated reviews. We want to identify key trends in product sales, customer preferences, and areas where we can improve our product offerings. The sales data, reviews, and ratings play a crucial role in making informed decisions regarding pricing strategies, marketing efforts, and inventory management.

Requirements: We need to conduct a comprehensive analysis of the sales data, with a focus on the following key areas: 1. Product Performance Analysis 2. Customer Behavior Analysis 3. Review and Feedback Insights 4. Please suggest other visuals which will help in decision making for the business.

Data Model: To create a data model for the sales data, we'll split the data into two parts: fact tables and dimension tables. The fact table will capture transactional data (e.g., sales, product reviews, etc.), while the dimension tables will provide the context (descriptions) for those transactions (e.g., product details, users, etc.).

Data Model Design: 1. Fact Table(s) - FactProductSales (or FactProductReviews) - This fact table will store data related to the sale and review of products. - Columns will reference dimensions via foreign keys.

2. Dimension Tables - DimProduct - Contains details about products such as product name, category, and pricing. - DimUser - Contains information about the users, such as user ID, user name, etc. - DimReview - Contains details about individual reviews, including review title, content, and rating. - DimCategory - Information about product categories.

3. Relationships - The FactProductSales table will have foreign keys to: - DimProduct (Product details) - DimUser (User details) - DimReview (Review details, if relevant) - DimCategory (Product category)