Project Lead: Neha Gupta
Team Member: Devisha Kumari
An end-to-end Power BI analytics project built using the Kaggle Superstore Dataset, designed to uncover sales trends, profit drivers, and customer behavior across regions.
This project demonstrates data preparation, star schema modeling, advanced DAX, and interactive dashboarding for executive-level decision-making.
Source: Kaggle – Superstore Dataset (Retail)
Key Fields:
- Orders: Order ID, Order Date, Ship Date
- Customers: Customer ID, Name, Segment
- Products: Category, Sub-Category
- Sales Metrics: Sales, Quantity, Discount, Profit
- Location: Region, State, City, Postal Code
- Logistics: Shipping Mode
A clean, relational dataset — ideal for data modeling and business intelligence dashboards.
To provide actionable business insights by:
- Identifying top-performing products, customers, and regions
- Analyzing time-based trends in sales & profitability
- Measuring the impact of discounts on profit
- Enabling role-based, region-specific insights using RLS
- Removed unnecessary rows and cleaned column values
- Corrected data types & renamed columns
- Split/Merged fields (e.g., Customer Name)
- Appended/Merged queries to integrate returns data
- Implemented Star Schema:
- Fact Table: Sales Orders
- Dimension Tables: Customers, Products, Regions, Dates
- Defined One-to-Many & Many-to-One relationships
- Managed Model View for clean design
- Basic Measures:
- Total Sales
- Total Profit
- Distinct Customers
- Average Discount
- Time Intelligence:
- Year-to-Date (YTD), Month-to-Date (MTD), Quarter-to-Date (QTD)
- Year-over-Year comparisons
- Advanced Metrics:
- Customer contribution to total sales
- % Sales by Region using iterators (
SUMX,AVERAGEX)
- Role-based filtering to restrict access by region
- KPI Cards: Sales, Profit, Customer Count
- Top Cities, Top Customers
- Sales & Profit trend lines
- Regional performance heatmaps
- Slicers for Segment, Region, Product Category
- Drilldowns, bookmarks, and a clean corporate theme
- Region Performance: Clear leaders and underperformers identified
- Profitability Impact: Discounts >20% significantly erode profit margins
- Customer Analysis: Repeat customers contribute a high share of total revenue
- Seasonality: Quarterly spikes observed in specific regions
- Power BI – Data Modeling, Visualization, DAX
- Power Query – Data Cleaning & Transformation
- Excel/CSV – Data Source
The RetailPulse Dashboard enables:
- Quick identification of profitable segments
- Targeted sales strategies per region
- Monitoring of discount policies’ financial impact
- Better resource allocation based on customer and product trends
ial impact - Better resource allocation based on customer and product trends