This Power BI project analyzes sales, profit, and discount performance across regions, product categories, and customer segments.
It provides a clear view of key business drivers, helping management identify which areas contribute most to profit and which are affected by discounting and operational inefficiencies.
- Understand overall sales and profit trends across different regions and categories.
- Measure how discounting impacts profit margins.
- Evaluate regional and segment-level performance.
- Provide actionable insights to support pricing and sales strategy decisions.
- Dataset Name: Superstore Dataset (Final Version)
- Source: Kaggle – Superstore Dataset Final
- Format: CSV
| Column Name | Description |
|---|---|
| Order ID | Unique identifier for each customer order |
| Order Date | Date the order was placed |
| Ship Date | Date the order was shipped |
| Ship Mode | Shipping method used (e.g., First Class, Standard Class) |
| Customer ID | Unique customer identifier |
| Customer Name | Full name of the customer |
| Segment | Customer type (Consumer, Corporate, or Home Office) |
| Country | Country of the order (primarily United States) |
| City | City where the order was placed |
| State | State where the order was placed |
| Region | Regional grouping (East, West, Central, South) |
| Product ID | Unique product identifier |
| Category | High-level product grouping (Furniture, Office Supplies, Technology) |
| Sub-Category | More detailed product category |
| Product Name | Name of the product sold |
| Sales | Total sales revenue for the order line |
| Quantity | Quantity of items sold |
| Discount | Discount rate applied to the sale |
| Profit | Profit generated from the sale |
| Postal Code | Postal code of the customer’s location |
The dataset was cleaned and transformed in Power Query before loading it into the Power BI model.
Key data preparation steps included:
- Removed Duplicates: Eliminated duplicate rows to ensure data accuracy and consistency.
- Changed Data Types: Assigned appropriate data types for each column — e.g., Order Date and Ship Date as Date, and Sales, Profit, and Discount as Decimal Numbers.
- Replaced Missing Values: Handled and replaced null values to prevent calculation or visualization errors.
- Added Calculated Columns:
- Profit Margin: Computed as Profit ÷ Sales to measure profitability efficiency for each transaction.
- Year: Extracted from Order Date to enable year-based analysis and trends.
- Month: Extracted from Order Date to analyze monthly seasonality.
- Delivery Days: Calculated as the difference between Ship Date and Order Date to evaluate delivery performance.
- Month-Year Index: Created to maintain proper chronological order in visuals across multiple years (since using “Month” alone sorts alphabetically or mixes years).
- Validated Data Integrity: Confirmed all transformations and computed columns before loading the cleaned dataset into Power BI for modeling.
- The cleaned dataset was loaded into Power BI’s Data Model, where relationships and hierarchies (Year → Month → Category) were structured.
- Created measures using DAX (Data Analysis Expressions) for KPI calculations.
Total Sales = SUM(Sales[Sales])
Total Profit = SUM(Sales[Profit])
Profit Margin % = DIVIDE([Total Profit], [Total Sales])
Total Discount = SUM(Sales[Discount])
Average Discount % = AVERAGE(Sales[Discount])
Interactivity & Features
Slicers: Added for State, Category, Shipmode, Year and Segment to allow dynamic filtering.
Tooltips: Created to display quick summaries when hovering over visuals.
- Total Sales: $2.30M | Total Profit: $286K | Average Discount: 16%
Profitability: The company maintains a healthy 12.5% margin, but performance varies sharply by region;West leads overall, the South operates most efficiently, while the Central region underdelivers due to likely cost or discount inefficiencies.
Discount Impact: Heavy discounting in the Consumer segment erodes profit, whereas disciplined pricing in Corporate and Home Office segments sustains strong margins.
Product Focus: Copiers, Phones, and Accessories are high-return categories. Tables and Bookcases underperform and need cost or pricing review.
Strategic Priorities: Strengthen discount governance, investigate Central region profitability issues, and replicate South region’s efficiency model to lift overall margins.
- Power BI Desktop: For modeling, report building, and visualization
- Power Query: For cleaning and transforming raw Superstore data
- DAX (Data Analysis Expressions): For KPI calculations and measures
- Visualization: Cards, bar charts, slicers, and interactive tooltips
Download/Watch Interactive Dashboard Demo
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Explore the Dashboard
- View the interactive Power BI dashboard demo below or open the GIF preview to see the dashboard in action.
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Project Files
Superstore_Full_Report.pbix– Contains the dashboard sheet, tooltip sheet, and insights sheet.dashboard_image_view.png– Static image of the final dashboard view.Superstore_Dashboard_GIF.gif– Animated dashboard preview.Superstore_Insights_Report.pdf– Downloadable insights report in PDF format.
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Interactivity
- Use slicers such as Region, Category, Year, and Segment to dynamically explore sales and profit data.
- Hover over visuals to see tooltips summarizing KPIs like Profit Margin, Sales, and Discounts.
- Navigate between the Dashboard, Tooltip, and Insights sheets for different levels of analysis.
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If you’d like to connect or have questions about this project, feel free to reach out:
- LinkedIn: Linet Rono
- Email: [email protected]
This project is open for learning and portfolio purposes.
You’re welcome to reference, adapt, or build upon this work in your own analyses or dashboards.
If you reuse any part of this project, please credit or reference this repository.