- Project Overview
This project presents an interactive Sales Performance Dashboard built in Microsoft Power BI using the AdventureWorks dataset. The objective is to analyze revenue, order distribution, product performance, and geographic sales trends to generate actionable business insights.
The dashboard consolidates KPIs, category-level performance, product-level sales, and geographic distribution into a single analytical view suitable for executive and operational decision-making.
- Business Objectives
Track actual sales vs target
Analyze sales distribution by product category
Identify top-selling products and customers
Evaluate regional and city-level sales performance
Support strategic decisions with data-driven insights
- Dashboard Components A. KPI Section
Target Sales Gauge
Displays actual sales against a predefined target
Example: $1.22M achieved vs $1.46M target
B. Category Analysis
Orders by Main Category (Bar Chart)
Bikes
Clothing
Components
Accessories Shows order quantity distribution.
Sales by Main Category (Donut Chart)
Revenue contribution by each category
Bikes dominate revenue share (~80%+)
C. Product-Level Analysis
Top Selling Bikes (Column Chart)
Displays highest revenue-generating bike models
Includes visual benchmark/average reference line
Helps identify premium and high-demand products
D. Customer Analysis
Top Selling Companies (Pie Chart)
Revenue distribution by top customers
Highlights key B2B contributors
E. Geographic Analysis
World Sales by City (Map Visualization)
Revenue distribution across global cities
Bubble size and color represent sales volume
Sales by State (Map Visualization)
Concentration of sales in US states and Europe
Identifies high-performing regions
- Key Insights
Bikes are the primary revenue driver, contributing the majority of total sales.
A small number of customers generate a significant portion of revenue (Pareto effect).
Sales are geographically concentrated in North America and parts of Europe.
Certain bike models consistently outperform others in total revenue.
Order volume does not always correlate directly with revenue (price mix effect).
- Data Model & Transformation
Data Source: AdventureWorks dataset
Data Cleaning: Performed in Power Query
Data Modeling:
Star schema approach
Fact table: Sales
Dimension tables: Product, Customer, Geography, Date
DAX Measures:
Total Sales
Sales YTD
Target Sales
Order Quantity
Category Contribution %
Top N filtering logic
- Tools & Technologies
Microsoft Power BI Desktop
Power Query (ETL)
DAX (Data Analysis Expressions)
Bing Maps Integration (Geospatial Analysis)
- How to Use
Download the .pbix file from this repository.
Open it in Power BI Desktop.
Use slicers and filters to explore:
Category-wise sales
Regional performance
Product-level insights
Customer contribution
- Project Structure /data -> Raw dataset (if shareable) /dashboard -> Power BI (.pbix) file /images -> Dashboard screenshots README.md -> Project documentation