This is the part of the data competition where my team (Alpha) and I analyzed the U.S. sales performance.
Welcome to the official repository for our submission to the DataCrux Analytics Case Clash hosted by the Analytical Arena – IIT Patna's Data Science Club. This project explores Adidas's U.S. sales data to extract key insights and business recommendations using data analytics and visualization.
🥇 Positioned 1st among the Top 10 Final Participants
Event: DataCrux – The Analytics Case Clash 2025
Host: Analytical Arena, The Data Science Club – IIT Patna
🏅 Awarded Certificate of Excellence for Insightful Analytics and Business Strategy
Adidas aims to analyze its U.S. sales performance across:
- Retailers
- Product categories
- Cities and regions
- Sales methods
The goal is to uncover key insights to:
- Reallocate marketing budgets
- Optimize product mix
- Recommend city- or retailer-specific growth strategies
- West Gear is the top retailer with $242.96M in sales and $85.67M in operating profit.
- Sports Direct has the highest profit margin (44.5%) despite lower total sales.
- Street Footwear & Apparel are the most profitable categories (42.7–42.8% margin).
- The West region is the best-performing, while Midwest shows potential for growth.
- Post-COVID recovery in 2021 tripled total sales and profit.
- July 2021 was the highest-performing month.
- Holiday seasons (Nov–Dec) showed strong, recurring spikes in sales.
- Online sales: Most profitable with 46.4% margin.
- Outlet stores: Also high in profitability (39.5%).
- Charleston, NY & Miami: Top cities in terms of operating profit efficiency.
📥 Download dataset(if needed)
To maximize growth and profitability, Adidas should:
- Expand Street Footwear and Apparel offerings.
- Launch promotions around July, August, and December (sales peak).
- Focus on high-margin Online and Outlet channels.
- Improve cost efficiency for In-Store sales.
- Target high-performing cities like Charleston, New York, and Miami for deeper market penetration.
- 🐍 Python (Pandas, Matplotlib, Seaborn)
- 📊 Power BI for Visualization
- 📁 Jupyter Notebook for Exploratory Analysis
- 📄 PDF Report for Final Submission
- Nikita Kumari
- Soniya Verma
- Avika Adarsh
Make sure you have Python installed (recommended version: 3.8 or later)
This project is licensed under the MIT License.
You are free to use, modify, and distribute this project with proper credit.
See the LICENSE file for full details.