__STYLES__
Tools used in this project
Adidas US Sales EDA

Interactive Power BI Dashboard

About this project

GitHub Repo

Kaggle Notebook

Project Objective:

In this project I conducted Exploratory Data Analysis (EDA) on Adidas US 9.6 thousand sales records for FY 2020-21, focusing on retailer performance, regional sales distribution, and channel-specific trends. Using MySQL for data cleaning & ETL, Python for EDA, and Power BI for dashboarding, the project uncovers key insights to identify growth opportunities and optimize strategies to drive operational profitability.

Vital KPIs Tracked:

Revenue, Operating Profit, Units Sold, Operating Profit Margin %, Average Selling Price, Revenue per Unit & Operating Profit per Unit.

EDA Python Notebook Overview:

The notebook is divided into different sections for specific types of analysis:

  • KPIs Performance Breakdown: Analyzed Top Retailers, Regions, Products, Seasons, and Sales methods contributing to Adidas’ overall Sales, Profitability, and Unit performance, identifying areas of strength and growth potential.
  • Temporal Analysis: Examined Retailer, Product, Region and Seasonal trends across FY 2020-21 to highlight fluctuations in sales metrics, focusing on Covid-19 recovery patterns and identifying key surge and fall periods.
  • Comparative Analysis: Compared Retailer, Region, and Product performance to identify unique trends and deviations, providing insights for targeted growth strategies.
  • Geospatial Analysis: Identified Top N performing States and Cities by Sales, Operating profit and Units sold, highlighting geographical opportunities and underperforming areas.
  • Distribution & Correlation Analysis: Explored Unit Price and Operating Profit Margin % distribution to uncover outliers and correlations, providing insights into product profitability and pricing strategies.

Analysis Insights & Recommendations

Project Presentation

Tools used:

  • Jupyter Notebook: for EDA and Visualizations
  • MySQL Workbench: for Data Cleaning and ETL
  • Microsoft Power BI Desktop: Data Modelling & Dashboard Design
  • Microsoft Power BI Service: for Publishing Report
  • GitHub: for Project Documentation

Skills: Python, EDA, Data Cleaning, Microsoft Power BI, Dashboards

Additional project images

KPI Distribution by Retailers
Product KPIs Monthly Trend
Product KPIs by Retailer
KPI Choropleth by States
KPIs by Top 20 States and Cities
Unit Price Distribution by Products
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