🚴 Adventure Works Cycles Analysis|Power-BI

Adventure Works is a Microsoft sample database that simulates a multinational manufacturing company producing and selling cycles and related products.
In this project, I connected and shaped the raw data using Power Query, built a relational data model, and created calculated fields with DAX measures.
Finally, I developed an interactive Power BI Dashboard to analyze the company’s sales performance, customer demographics, and product profitability.

---

💡 Project Overview

This project aims to provide actionable insights for Adventure Works management by answering key business questions such as:

• Which customers and regions contribute the most to profit?
• What product categories drive sales growth?
• How do demographics (income, occupation) affect customer behavior?
• How does pricing impact profitability (via What-If analysis)?

The final dashboard is designed to help stakeholders make data-driven decisions and track performance effectively.

---

🛠 Tools Used

• Excel (data prep/sample dataset) 👉 Datasets File
• Power BI – Data visualization and dashboarding
• Power Query – Data cleaning and shaping
• DAX (Data Analysis Expressions) – Calculations and measures
• What‑If parameter for single-value slicer

---

🗂️ Schema Diagram

Here is the schema used for data modeling in Power BI:

^{ERD showing relationships among dimension and fact tables}

---

---

❓ Key Business Questions Answered

1. Which customers and regions are most profitable?
2. What product categories contribute the most revenue & profit?
3. How do income levels and occupations affect purchasing?
4. What is the effect of price adjustment (What-If parameter) on total profit?
5. How does sales performance vary across time (year/quarter/month)?

---

---

🧮 Key Measures (DAX examples)

-- Core

Total_Order = DISTINCTCOUNT(AW_Sales_Data_2015_17[OrderNumber])
Total_Revenue = SUMX(AW_Sales_Data_2015_17,AW_Sales_Data_2015_17[OrderQuantity]*RELATED(AW_Products[ProductPrice]))
Total_Cost = SUMX(AW_Sales_Data_2015_17,AW_Sales_Data_2015_17[OrderQuantity]*RELATED(AW_Products[ProductCost]))
Total Profit  = [Total Revenue] - [Total Cost]

-- What‑If parameter (auto-created by Power BI when you add a Numeric Range parameter)
-- 'Price Adjustment (%)'[Price Adjustment (%)]  -- between -1 and +1 for -100%..+100%

Adjusted_Price = [Avg_retail_Price]*(1+'Price_Adjustment(%)'[Price_Adjustment(%) Value])

-- Example: Income banding
Income Level = 
IF(
    AW_Customers[AnnualIncome] <= 40000, "Low",
    IF(
        AW_Customers[AnnualIncome] <= 90000, "Average",
        IF(
            AW_Customers[AnnualIncome] <= 130000, "High",
            "Very High"
        )
    )
)

---

---

📊 Power BI Dashboard

📃 Executive Summary

👨‍👩‍👧‍👦 Customer Details

🏍️ Product Details

---

---

🔑 Key Insights

• Top Customers: 20% of customers drive ~65% of revenue.
• Regional Trends: North America leads in sales, but Europe has higher profit margins.
• Product Mix: Accessories generate volume, but Bikes bring the highest revenue share.
• Customer Behavior: Higher income groups spend significantly more per order.
• What-If Analysis: A 5% price increase scenario showed profit improvement without major customer churn.

---

✅ Conclusion

The analysis of AdventureWorks data provided valuable insights into sales performance, customer behavior, and product profitability.
With these insights, AdventureWorks management can optimize pricing strategies, target high-value customers, and focus on profitable regions.

---

📬 Contact

👤 Author: Mohan Kumar
📧 Email: mohan122000kumar@gmail.com

⭐ Feel free to fork or star this repo if you found it useful!