Adidas US Sales Analysis (Power BI)

In this data-driven retail analytics project, I analyzed Adidas US sales performance using Power BI, uncovering key insights across products, channels, and regions to support strategic decision-making.

🔧 Tools & Techniques Used

• Power BI (Data modeling, DAX, visualization)
  
  

• Data Cleaning: Removed null rows and inconsistencies
  
  

• Time Intelligence: Created a calendar table, YTD, and variance calculations
  
  

• DAX Measures: Total Sales, Profit, Units Sold, Transactions, Average Price, Operating Margin
  
  

📊 Key Findings

• Total Sales: $900M — Up 394% Year-over-Year
  
  

• Units Sold: 2M — Up 4.36% YoY
  
  

• Profit: $332M — Up 4.24% YoY
  
  

• Operating Margin: Stable at 36–37%
  
  

🏆 Top Performers

• Best-Selling Products:
  
  

  • Men’s Street Footwear: $209M
    
    

  • Women’s Apparel: $179M (↑9% MoM | Avg. Price: $52)
    
    

• Top Retailers:

  • West Gear: $240M
    
    

  • Foot Locker: $220M
    
    

• Leading Regions:
  
  

  • West: $270M
    
    

  • Northeast: $190M
    
    

• Women’s Apparel by Channel:
  
  

  • In-store: $70M
    
    

  • Outlet: $59M
    
    

  • Online: $50M
    
    

• Standout States:
  
  

  • New York: $23.8M (↑571% YoY)
    
    

  • Florida: $21M (↓32% YoY)
    
    

• Peak Sales Months: June and December
  
  

📈 Recommendations

• Invest in Men’s Streetwear and Women’s Apparel campaigns via West Gear and Foot Locker in the West and Northeast regions.
  
  

• Investigate Florida’s 32% drop in sales for potential issues (e.g., marketing, inventory).
  
  

• Boost online channel performance with targeted digital ads.
  
  

• Launch seasonal promotions in June and December to capitalize on peak performance periods.
  
  

✅ Outcome

This project enhanced my skills in data transformation, advanced DAX, and dashboard design, and is a strong example of how I translate raw data into actionable business insights.