This repository contains a Power BI dashboard named Test Sales Analysis Report designed to visualize and analyze sales data. The dashboard provides insights into key sales performance metrics, trends, and customer behavior to help stakeholders make informed business decisions.
Overview
The Test Sales Analysis Report dashboard enables users to:
Monitor overall sales performance over time.
Identify top-performing products and sales regions.
Analyze customer demographics and purchasing patterns.
Track key performance indicators (KPIs) such as revenue growth, average order value, and sales targets.
Dataset
The dataset used in this Power BI dashboard includes the following key attributes:
Date: The date of each sales transaction.
Product: The name or category of the product sold.
Region: The geographic region where the sale occurred.
Customer ID: Unique identifier for each customer.
Revenue: Total revenue generated by each transaction.
Quantity: Number of units sold in each transaction.
Discount: Discount applied to each transaction (if any).
NOTE: You may need to modify the above dataset attributes to match the exact data used in your file.
Key Visualizations
The dashboard contains several visualizations to provide a comprehensive view of sales performance:
Sales Over Time: A line chart showing sales trends over specific time periods.
Top Products by Revenue: A bar chart highlighting the highest-selling products.
Sales by Region: A map visualization displaying sales distribution across different regions.
Customer Segmentation: A pie chart or bar chart illustrating customer segments by demographics or purchasing behavior.
KPIs: Key metrics like Total Sales, Average Order Value, and Conversion Rate displayed at the top of the dashboard.
Usage
Prerequisites
Power BI Desktop: Make sure you have Power BI Desktop installed to open and view the .pbix file. Download it from Microsoft Power BI.
Customization
Feel free to customize the dashboard by:
Adding or modifying visualizations to fit your specific needs.
Creating new measures, calculated columns, or custom visuals.
Updating data sources or datasets for more comprehensive analysis.