📌 Project Overview This project analyzes 9,994 retail sales transactions from a US-based superstore (2014–2017) using Power BI. The goal was to uncover profitability patterns, regional performance differences, and product-level insights through an interactive 3-page dashboard.
🔍 Key Findings 💰 Total Revenue: $2.30M | Total Profit: $286.40K 📦 Tables sub-category generates negative profit despite high sales volume — a critical loss driver 🌍 West region leads in sales, but East region shows strong profitability 🖥️ Technology category is the most profitable category 🚚 59.72% of orders use Standard Class shipping 🏙️ New York City is the highest profit-generating city ($62,036) 📉 Higher discounts correlate with lower profit — especially in Furniture
📊 Dashboard Pages Page 1 — Sales Overview KPI Cards: Total Sales, Total Profit, Order Count, Customer Count Sales by Region & Category (Clustered Bar Chart) Monthly Sales & Profit Trend (Line Chart) Top 10 Products by Sales (Bar Chart) Ship Mode Distribution (Donut Chart) Filters: Year Range (2015–2018), Customer Segment Page 2 — Profitability Analysis Profit by Sub-Category — reveals loss-making products Profit by Supplier (Bar Chart) Sales vs Profit by Supplier (Scatter Chart) Discount vs Profit by Category (Line Chart) Page 3 — Geographic Analysis US Map — Profit by State & Region City-level Profit Ranking Table
🗂️ Data Sources Table Source Rows Superstore_Sales_Clean Kaggle — Sample Superstore Dataset 9,994 Product_Details Supplementary product cost & supplier data 1,862 Takvim (Calendar) DAX generated calendar table —
Note: The Superstore dataset is a widely-used demo dataset originally created by Tableau for educational purposes.
🔗 Data Model
Superstore_Sales_Clean
└── Product ID ──── Product_Details (Many-to-One)
└── Order Date ──── Takvim/Calendar (Many-to-One)
📐 DAX Measures
Toplam Satış = SUM('Superstore_Sales_Clean'[Sales])
Toplam Kâr = SUM('Superstore_Sales_Clean'[Profit])
Average Order Value = DIVIDE([Toplam Satış], DISTINCTCOUNT('Superstore_Sales_Clean'[Order ID]), 0)
🛠️ Tools & Techniques Power BI Desktop — Dashboard design & visualization Power Query — Data transformation & cleaning DAX — Custom measures (Total Sales, Total Profit, Profit Margin) Data Modeling — Table relationships (Many-to-One) Map Visualization — US State & Region level geographic analysis
📁 Repository Structure
us-retail-sales-dashboard/
├── US_Retail_Sales_Dashboard.pbix
├── data/
│ ├── Superstore_Sales_Clean.xlsx
│ └── Product_Details.xlsx
├── screenshots/
│ ├── page1_overview.png
│ ├── page2_profitability.png
│ └── page3_map.png
└── README.md
👩💻 About Buse | Industrial Engineer transitioning into Data Analytics Connecting engineering mindset with data storytelling — this project applies efficiency and process thinking to retail sales analysis.
Dashboard created with Power BI Desktop. Dataset source: Kaggle.