This Power BI project analyzes the Superstore dataset to deliver actionable insights across sales, profit, profit margin, orders, returns, products, markets, and employee performance.
The report is designed to help business users:
- 📈 Track KPI performance over time
- 🔁 Compare current results with last year
- 🌍 Analyze market and country performance
- 🛍️ Evaluate product profitability and return behavior
- 👥 Assess employee sales contribution
- 💡 Identify opportunities for operational improvement
- Monitor key KPIs such as Sales, Profit, Profit Margin, Orders, and Return Rate
- Perform year-over-year (YoY) comparison using time intelligence measures
- Explore performance by market, country, region, segment, and category
- Detect high-sales but low-margin products
- Analyze return patterns across markets and categories
- Evaluate employee contribution by person and market
This dashboard helps answer the following questions:
- What is the overall business performance of Superstore?
- How do Sales, Profit, and Orders compare with last year?
- Which markets and countries contribute the most to revenue and profit?
- Which products or subcategories drive the highest sales?
- Which products have weak or negative profitability?
- What is the return rate, and where are returns concentrated?
- Which employees perform best in sales and profit contribution?
- For a selected customer, which country generates the highest sales?
The report is built on a Superstore-style business dataset with the following main tables:
- Orders
- Returns
- dimDate
Orders[Sales]Orders[Profit]Orders[Order ID]Orders[Customer ID]Orders[Country]Returns[Order ID]dimDate[Date]
The report uses a model centered around:
- A transactional Orders table
- A Returns table for returned orders
- A Date dimension table for time intelligence calculations
This structure supports analysis by:
- ⏳ Time
- 🛒 Product
- 🌍 Geography
- 👤 Customer
- 👥 Employee
- 🔁 Return behavior
📌 Core KPI Measures
Total Sales = SUM(Orders[Sales])
Total Profit = SUM(Orders[Profit])
Total Order = DISTINCTCOUNT(Orders[Order ID])
Total Return = DISTINCTCOUNT(Returns[Order ID])
Profit Margin = DIVIDE([Total Profit], [Total Sales])
Return rate = DIVIDE(DISTINCTCOUNT(Returns[Order ID]), [Total Order])
📅 Time Intelligence Measures
Revenue LY =
CALCULATE(
[Total Sales],
SAMEPERIODLASTYEAR(dimDate[Date])
)
YoY % = DIVIDE([Total Sales] - [Revenue LY], [Revenue LY], 0)
Profit LY =
CALCULATE(
[Total Profit],
SAMEPERIODLASTYEAR(dimDate[Date])
)
YoY % Profit = DIVIDE([Total Profit] - [Profit LY], [Profit LY], 0)
Order LY =
CALCULATE(
[Total Order],
SAMEPERIODLASTYEAR(dimDate[Date])
)
Return rate LY =
CALCULATE(
[Return rate],
SAMEPERIODLASTYEAR(dimDate[Date])
)
Revenue 3M Avg =
AVERAGEX(
DATESINPERIOD(dimDate[Date], MAX(dimDate[Date]), -3, MONTH),
[Total Sales]
)
Profit Margin SPLY =
CALCULATE(
[Profit Margin],
SAMEPERIODLASTYEAR(dimDate[Date])
)
🚀 Advanced Measures
Top Country by Sales =
VAR _Customer = SELECTEDVALUE('Orders'[Customer ID])
VAR _Table =
SUMMARIZE(
FILTER(
ALL('Orders'),
'Orders'[Customer ID] = _Customer
),
'Orders'[Country],
"SalesAmt", [Total Sales]
)
VAR _TopCountry =
TOPN(1, _Table, [SalesAmt], DESC)
RETURN
MAXX(_TopCountry, 'Orders'[Country])
Sales of Top Country =
VAR _TopCountry = [Top Country by Sales]
RETURN
CALCULATE(
[Total Sales],
'Orders'[Country] = _TopCountry
)
Profit of Top Country =
VAR _TopCountry = [Top Country by Sales]
RETURN
CALCULATE(
[Total Profit],
'Orders'[Country] = _TopCountry
)
Profit Margin of Top Country =
DIVIDE(
[Profit of Top Country],
[Sales of Top Country]
)
Total Order of Top Country =
VAR _TopCountry = [Top Country by Sales]
RETURN
CALCULATE(
[Total Order],
'Orders'[Country] = _TopCountry
)
Zero Target = 0
Note: The screenshots below use the current file names you provided. If you later move them into an
images/folder, just update the paths in this README.
High-level summary of sales, profit, customer base, orders, market contribution, and key business observations.
Tracks core KPIs and business performance trends by quarter, market, segment, category, and top product subcategories.
Focuses on product performance, profitability, monthly trend, subcategory analysis, and the distribution of sales vs. profit.
Explores customer, market, country, region, return, and profitability performance across geographies.
Analyzes employee-level sales, profit, YoY performance, and return rate contribution by market.
Based on the dashboard screenshots and measures:
- 💰 Total sales are approximately $13M
- 📈 Total profit is around $1M
- 📊 Overall profit margin is about 11.61%
- 🧾 Total orders reached approximately 25K
- 🔁 Overall return rate is around 4.68%
- 👤 The business serves approximately 1.59K customers
- 🌎 The company operates across seven markets: APAC, EU, US, LATAM, EMEA, Africa, and Canada
- 📱 Phones appear to be among the top-selling subcategories
⚠️ Some products generate strong sales but weak or negative margins- 🧠 Employee performance differs significantly across markets and individuals
-
Improve profitability
- Review products with high sales but low or negative profit margin
- Reassess pricing, discounting, and fulfillment costs
-
Reduce return rate
- Investigate categories and markets with high returns
- Identify root causes such as product quality, shipping issues, or customer mismatch
-
Strengthen high-performing markets
- Prioritize markets with strong sales growth and healthy margins
- Replicate successful practices in weaker regions
-
Optimize product portfolio
- Focus on top-performing subcategories
- Reevaluate low-margin, high-return products
-
Use employee insights for performance coaching
- Benchmark top performers
- Share selling strategies across teams
- Power BI Desktop
- DAX
- Power Query
- Data Modeling
- Superstore Dataset
- Dashboard design and storytelling
- KPI modeling
- Time intelligence with DAX
- Product performance analysis
- Market and geographic analysis
- Return analysis
- Employee performance tracking
- Business insight communication
- Open the
project_superstore.pbixfile in Power BI Desktop - Refresh the dataset if necessary
- Navigate through the report pages
- Use filters/slicers such as:
- Year
- Person
- Market
- Country
- Category
- Customer
Vuong Minh Toan
- This project is intended for portfolio and learning purposes.
- Some values in the screenshots may vary depending on filters and page interactions.
- If you rename or move the image files, remember to update the image paths in
README.md.