This project presents a detailed Adidas Sales Dashboard analyzing sales data across different products, retailers, and sales methods within the US market. The dashboard offers an interactive and visual breakdown to support decision-making for sales optimization, retailer management, and product performance evaluation.
You can view the dataset used in this project here:
🔗 Adidas US Sales Dataset (Excel)
- 💰 Total Sales – Cumulative sales revenue across all transactions
- 📦 Total Units Sold – Sum of products sold
- 📊 Operating Profit – Sales revenue after operating costs
- 💵 Average Selling Price – Average revenue per unit sold
- 🛍️ Sales by Product Category – Performance across product lines
- 🏬 Sales by Retailer – Retailer-wise performance breakdown
- 🌎 Sales Method Analysis – In-store vs Outlet performance
- 📈 Monthly Sales Trends – Seasonal fluctuations and trends
- 📉 Operating Profit Analysis – Profitability across categories
- 📊 Men’s Street Footwear and Men's Apparel drive the highest sales volumes.
- 🏬 Foot Locker remains the top-performing retailer across the dataset.
- 🏷️ Sales are distributed almost equally between In-store and Outlet methods.
- 📆 Clear seasonal trends can be observed, with strong sales in Q1 and Q2.
- 💹 Profit margins remain stable across most product categories.
- 🔼 A steady growth is observed month-over-month in certain product categories.
- 🎯 Targeting higher-margin products like Men’s Street Footwear can further improve profitability.
- ⚙️ Resource allocation can be optimized for peak sales months to manage inventory efficiently.
- 📉 Lower sales observed in certain Women's Apparel categories suggest potential for targeted marketing or product adjustments.
- 📊 Operating profit analysis highlights consistent profit margins across both sales methods.
- 💡 Recommendations:
- Focus on sustaining growth for best-selling categories.
- Evaluate under-performing SKUs for possible replacement or promotion.
- Maintain optimized inventory planning for seasonal demand cycles.
