The online shopping industry is changing quickly, and using data analysis is important to stay competitive. In our example, we show how Power BI can help businesses understand e-commerce data better. This helps them make smart choices and grow. Power BI makes it easy to get data ready, see it in graphs, and share it with others. It's a great tool for studying e-commerce info and getting ahead in business.
Loading an Excel file with 5 tables:
- Customers
- Orders
- Order Details
- Categories
- Products
The data is being cleaned and transformed to prepare for modeling and analysis. This involves:
- Checking Data Types
- Managing Outliers
- Dealing with Null Values
- Splitting Columns
- Creating New Columns
- Replace Values
- Creating a New Table containing Order Date Details
Here are the specific analysis points we focused on:
Sales Patterns:
• Analyze the sales data to identify any seasonal or periodic trends.
• Determine the days, weeks, or months with the highest sales volume.
• Provide recommendations for leveraging these patterns for targeted marketing or promotional
activities.
Products:
• Analyze sales revenue, units sold, and average order value for each product.
• Identify top-selling products or categories based on sales volume.
• Explore any trends or patterns in product performance and suggest strategies for
improvement.
Customers:
• Calculate the conversion rate by dividing the number of purchases by the total number of
app visitors.
• Identify customer demographics, preferences, and behavior based on the available data.
• Highlight key insights or patterns to help us better understand our customers.
