Online shopping has become increasingly important, especially post COVID-19 pandemic, which limited physical store visits. Businesses must now rely heavily on data to understand customer behavior, optimize product performance, and improve operational efficiency.
Using the Global Superstore dataset, the following are structured Power BI projects that demonstrate end-to-end analytics capabilities.
Downloading Dataset:
Downloading the Dataset:
Follow the link below to download the .csv file with the dataset. Open this link, https://github.com/LuxDevHQ/Data, and download Global_Superstore2.csv.
Please note that this is a private repository under the LuxDevHQ organization, so you need to be added to LuxDevHQ.
If you are not added, WhatsApp your username to 0796448232 to be granted access.
Stage 1 Data Engineering & Ingestion
Objective
Build a scalable data pipeline by moving raw data into a database.
Scope
- Import CSV/Excel data into:
- SQL Server / MySQL / PostgreSQL
- Create structured tables:
- orders
- customers
- products
- Connect Power BI to the database
Stage 2: Data Cleaning & Transformation
Prepare clean and reliable data for analysis using Power Query.
Key Tasks
- Handle missing values (Postal Code, Discount anomalies)
- Remove duplicates (Order ID + Product ID)
- Fix data types (dates, numeric fields)
- Create derived columns:
- Delivery Days
- Profit Margin
- Standardize categories and country names
Stage 3: Data Modeling (Star Schema Design)
Design an optimized data model for performance and scalability.
Model Structure
- Fact Table: Orders
-
Dimension Tables:
- Customers
- Products
- Geography
- Date Table
Key Deliverables
- Proper relationships
- Calendar table creation
- Optimized filtering
Project 4: Customer Analytics
Analyze customer behavior and profitability.
Key Questions
- How frequently do customers purchase?
- Do high-frequency customers generate more revenue?
- Are they more profitable?
- What is the profit margin across customer segments?
- Which customer segment is most profitable each year?
- How are customers distributed geographically?
Stage 5: Product Analytics
Evaluate product performance and pricing impact.
Key Questions
- Which country generates the highest sales?
- What are the top 5 profit-making products yearly?
- How does price affect sales?
- Is there a relationship between price drops and increased sales?
Stage 6: Operations & Delivery Analytics
Assess logistics and delivery performance.
Key Questions
- What is the average delivery time across countries?
- Which regions experience delays?
- How does delivery performance impact customer satisfaction?
Stage 7: DAX & Advanced Analytics
Develop advanced analytical capabilities using DAX.
Key Deliverables
- Core KPIs:
- Total Sales
- Total Profit
- Total Orders
- Customer segmentation logic
- Profit margin calculations
- Time intelligence (YTD, YoY)
- Moving averages and ranking
Stage 8: Dashboarding & Reporting
Build interactive and insightful dashboards.
Dashboard Pages
Executive Overview
- KPIs (Sales, Profit, Orders)
- Sales trends
Customer Insights
- Segmentation
- Revenue contribution
- Geographic distribution
Product Performance
- Top products
- Price vs sales analysis
Operations Dashboard
- Delivery time
- Shipping performance
Stage 9: Business Insights & Storytelling
Extract and communicate actionable insights.
Example Insights
- High-frequency customers are not always the most profitable
- Some regions have high sales but low margins
- Discounts reduce profitability
- Delivery delays vary significantly by region
Final Outcome
By completing this project, you will:
- Perform data cleaning and transformation
- Design efficient data models
- Apply advanced DAX analytics
- Create professional dashboards
- Deliver business insights effectively
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