Adidas Sales Analysis Dashboard in Power BI

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 Welcome to my portfolio blog! In this post, I’ll walk you through how I built an interactive Adidas Sales Analysis Dashboard in Power BI. This dashboard provides key insights into sales performance, regional trends, and product profitability, helping businesses make data-driven decisions.

Dashboard Overview

This dashboard analyzes Adidas’ U.S. sales data (2020-2021) with the following key metrics:

Key Performance Indicators (KPIs)

·         Total Sales: $900M

·         Operating Profit: $332M

·         Units Sold: 2M

·         Average Price per Unit: $45

·         Average Margin: 42%



Interactive Visualizations

1.    Monthly Sales Trend (Area Chart) – Identifies peak sales periods.

2.    State-wise Sales (Map Chart) – Highlights top-performing states.

3.    Regional Sales (Donut Chart) – Breaks down sales by U.S. regions.

4.    Top Products & Retailers (Bar Charts) – Shows best-selling products and retailers.

Dynamic Filters

·         Region (Northeast, West, etc.)

·         State (California, Texas, etc.)

·         Date Range (Custom date selection)

How I Built the Dashboard

1. Data Import & Preparation

·         Dataset: Publicly available Adidas U.S. sales data (~10,000 rows).

·         Data Fields: Retailer, Invoice Date, Region, State, Product, Price, Units Sold, Profit, Margin.

·         Cleaning: Minimal cleaning required—data was already structured.

2. Key Metrics (DAX Measures)

·         Total Sales: SUM(Data[Total Sales])

·         Average Price: AVERAGE(Data[Price per Unit])

·         Operating Margin: AVERAGE(Data[Operating Margin])

3. Visualizations & Insights

A. Monthly Sales Trend (Area Chart)

·         Insight: Peak sales in July, August, and December (holiday season).

·         Actionable Takeaway: Increase inventory and marketing efforts during these months.

B. State-wise Sales (Map Chart)

·         Top States: New York, California, Texas, Florida.

·         Insight: Highest revenue from urban and high-population states.

C. Regional Sales (Donut Chart)

·         Best-Performing Region: West (30% of total sales).

·         Lowest: Midwest.

D. Product & Retailer Performance (Bar Charts)

·         Top Product: Men’s Street Footwear (highest sales).

·         Top Retailer: West Gear (outperforms Walmart & Amazon).

4. Interactive Filters

·         Added slicers for Region, State, and Date Range to allow dynamic filtering.

·         Enabled cross-filtering—clicking on a product/retailer updates all visuals.

Key Business Insights

1.    Seasonal Trends: Q3 (July-August) and December are peak sales months.

2.    Geographic Focus: New York and California contribute the most revenue.

3.    Product Strategy: Men’s Street Footwear is the best seller—consider expanding this line.

4.    Retailer Performance: West Gear outperforms others—strengthen partnerships with top retailers.

Try It Yourself!

·         Download Dataset: Adidas U.S. Sales Data (Link in video description).

·         Power BI File: GitHub Repository (Coming soon).

Why This Project?

This dashboard demonstrates my ability to:
✅ Transform raw sales data into actionable insights.
✅ Design user-friendly, interactive reports in Power BI.
✅ Apply data storytelling for business decision-making.

Final Thoughts

Building this dashboard helped me strengthen my Power BI, DAX, and data visualization skills. By analyzing Adidas’ sales trends, I learned how to present complex data in a clear, impactful way.

Want to see more? Check out my Power BI Portfolio or connect with me on LinkedIn!

Thanks for reading! 🚀

 

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