🚴 Adventure Works Sales & Customer Intelligence Dashboard

End-to-End Business Intelligence Project | Power BI

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📌 Overview

This project is an end-to-end Business Intelligence solution built in Power BI Desktop using the Adventure Works dataset.

The goal was to transform raw .csv sales and customer data into an interactive, executive-level dashboard that supports:

• KPI monitoring
• Regional performance analysis
• Product-level trend analysis
• Customer segmentation & revenue insights
• Target vs actual performance tracking
• What-if scenario simulation

This dashboard enables stakeholders to identify performance gaps, optimize pricing strategy, and make data-driven decisions using interactive analytics.

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🖼️ Dashboard Preview

Executive Summary	Regional Analysis	Product Trends	High-Value Customers

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📊 Key Metrics at a Glance

• 💰 $24.9M Total Revenue
• 📦 25.2K Total Orders
• 📈 $10.5M Total Profit
• 🔁 2.2% Overall Return Rate
• 👥 17.4K Unique Customers

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📂 Download the Power BI File

You can download the full interactive Power BI report here:

👉 Download AdventureWorks_Report.pbix

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🧠 Business Problem

Adventure Works management needed a centralized reporting solution to:

• Track Revenue, Orders, Profit, and Return Rate
• Identify underperforming regions and product categories
• Analyze product returns and profitability
• Discover high-value customers
• Evaluate performance against monthly targets
• Simulate pricing impact on profit

The available data consisted only of raw operational CSV files.

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🏗️ Solution Architecture

This project follows a structured Business Intelligence development lifecycle:

1️⃣ Data Transformation (Power Query)

• Data cleaning & shaping
• Table merging and transformations
• Rolling calendar table creation
• Conditional columns & aggregations
• Data type optimization

2️⃣ Data Modeling

• Implemented a Star Schema
• Fact table: Sales
• Dimension tables: Product, Customer, Geography, Date
• Managed active/inactive relationships
• Optimized filter flow

3️⃣ DAX & Advanced Calculations

• Revenue, Profit, Return %, Orders measures
• Time intelligence (MoM, YTD)
• Target vs Actual calculations
• What-if parameter for price adjustments
• Adjusted Profit simulation
• Customer segmentation metrics

4️⃣ Dashboard & UX Design

• Executive KPI cards
• Trend analysis visuals
• Drilldowns & hierarchical navigation
• Decomposition tree
• Key Influencers visual
• Region-level filtering experience
• Interactive What-if slider

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🧮 Sample DAX Measures

Below are a few core measures used in this dashboard:

Total Revenue = SUM(Sales[Revenue])

Total Orders = DISTINCTCOUNT(Sales[OrderNumber])

Return % = 
DIVIDE(
    [Total Returns],
    [Total Orders]
)

Adjusted Profit =
[Total Revenue] * (1 + SELECTEDVALUE('Price Adjustment'[Adjustment %]))
    - [Total Cost]

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📊 Dashboard Pages

📌 1. Executive Summary

• $24.9M Revenue
• $10.5M Profit
• 25.2K Orders
• 2.2% Return Rate
• Revenue trends, category breakdowns, and top product analysis
• Most ordered & most returned product types

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🌍 2. Regional Performance

• Country-level sales analysis
• Region segmentation (Europe, North America, Pacific)
• Performance comparison
• Geo-based visual insights

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📦 3. Product & Target Analysis

• Monthly Orders vs Target
• Revenue vs Target
• Profit vs Target
• Price Adjustment What-if simulation
• Profit trend comparison (Actual vs Adjusted)
• Return % tracking over time

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👤 4. Customer Intelligence

• Revenue per Customer
• Unique Customers (17.4K)
• Top 100 customers
• Income-level segmentation
• Occupation analysis
• Key Influencers analysis

Top Customer:

• Mr. Maurice Shan – $12.4K Revenue

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🧩 5. Product Hierarchy Drilldown

• Total Orders → Category → Subcategory → Product
• Full decomposition for granular analysis
• Demonstrates strong relational modeling

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📈 Key Insights

• 🌎 United States is the top revenue-generating region
• 🚲 Road Tire Tube is the most ordered product
• 🔁 Shorts have the highest return rate among product types
• 💰 Revenue per customer is trending downward over time
• 🧠 Professionals show strong revenue contribution in 2022

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💡 Business Recommendations

• Reduce return rates in the Clothing category (especially Shorts)
• Increase cross-selling in high-volume categories like Tires & Tubes
• Expand targeted campaigns in high-performing regions (US)
• Develop loyalty strategies for high-income professionals
• Monitor pricing strategy impact using What-if simulation

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🛠️ Tech Stack

Tool	Purpose
Power BI Desktop	Dashboard development
Power Query	Data transformation
DAX	Business calculations
Star Schema Modeling	Data modeling
What-if Parameters	Scenario simulation

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📂 Repository Structure

ADVENTURE-WORKS-POWER-BI-DASHBOARD/
│
├── 📁 AdventureWorks Raw Data/        # Source CSV files used for modeling
├── 📁 images/                         # Dashboard preview screenshots
├── 📊 AdventureWorks_Report.pbix      # Power BI report file
├── 📄 Report.pdf                      # Exported dashboard report (PDF)
└── 📘 README.md                       # Project documentation

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🔮 Future Enhancements

• Implement Row-Level Security (RLS)
• Schedule automatic refresh in Power BI Service
• Add forecasting models
• Enhance mobile-optimized layout
• Deploy to Power BI Service workspace

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🤝 Connect With Me

If you're hiring for Data Analyst, Business Analyst, or Power BI Developer roles - I’d love to connect.

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