Adidas US Sales Analysis Power BI Project

Introduction

This project was developed during my Analytics Extra mentorship program :㊗️, aimed at enhancing my data analytics and business intelligence skills. The focus was on creating a visually engaging and interactive dashboard to analyze Adidas US sales data. Leveraging Power BI, I incorporated advanced data modeling, data transformation, and report design techniques.

This repository showcases the steps I took to reproduce the project, from data preparation to deploying the dashboard on Microsoft Fabric.

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Problem Statement

Adidas needs a comprehensive dashboard to monitor sales performance across various products, regions, and retailers in the United States. The goal was to:

1. Identify top-performing regions, products, and sales channels.
2. Provide actionable insights into sales trends, profitability, and operational efficiency.
3. Enable stakeholders to interact with the data dynamically and intuitively.

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Skills and Concepts Applied

• Power Query: Data cleaning and transformation.
• Data Modeling: Created a star schema with 3 dimension tables and 1 fact table.
• Data Analysis Expressions (DAX): Developed measures and calculated columns for dynamic insights.
• Interactive Dashboard Design: Designed multiple pages (Home Page, Product Page, Deep Insights Page, and Tooltips) for intuitive navigation.
• Microsoft Fabric: Published the dashboard and implemented deployment pipelines (development, testing, and production stages).
• Visualization Best Practices: Used color schemes, slicers, and interactive visuals to enhance the user experience.

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Data Overview

The dataset included three tables in an Excel workbook:

1. Data Sales Adidas (Fact Table):

   • Fields: Retailer, Retailer ID, Invoice Date, Location Key, Product, Price Per Unit, Units Sold, Total Sales, Operating Profit, Operating Margin, Sales Method.

2. Product (Dimension Table):

   • Fields: Product, Image URL.

3. Location (Dimension Table):

   • Fields: Region, State, City, Location Key.

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Data Model

The final data model was a star schema with the following relationships:

• Fact Table: Data Sales Adidas.
• Dimension Tables: Product, Location, and a Date Table (created using DAX).
• Relationships: One-to-Many relationships connecting the fact table to the dimension tables.

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Dashboard Overview

1. Home Page

• Overview of the dashboard with a slicer for region-based filtering.
• Interactive map showing sales performance by state.

2. Product Page

• Dynamic visualizations of product-level performance.
• A slicer for filtering products with an interactive display of product images.

3. Deep Insights Page

• Detailed analysis of profitability and sales trends.
• Visualizations included bar charts, line graphs, and KPIs.

4. Tooltip Pages

• Contextual insights embedded within visuals.
• Hover-over details for granular exploration.

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Results and Analysis

Key insights derived from the dashboard:

1. Top States: Identified regions with the highest total sales and profitability.
2. Product Trends: Analyzed best-selling products and their contribution to revenue.
3. Retailer Insights: Compared sales performance across different retailers.

These insights enabled Adidas to focus on high-performing regions and optimize inventory management.

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Deployment

The dashboard was deployed to Microsoft Fabric using the following stages:

1. Development Stage: Initial version of the dashboard for internal testing.
2. Testing Stage: Conducted user acceptance testing to validate performance.
3. Production Stage: Published the final version for stakeholders and generated a public sharing link here.

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Conclusion

This project demonstrated the power of interactive dashboards in providing actionable insights for businesses. By utilizing Power BI and Microsoft Fabric, I created a professional-grade dashboard that is dynamic, visually appealing, and insightful.

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Recommendations

1. Expand Analysis: Incorporate additional datasets, such as customer demographics, for a more holistic analysis.
2. Predictive Analytics: Implement machine learning models to forecast future sales trends.
3. Automation: Automate data updates using Microsoft Power Automate for real-time insights.

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Repository Contents

1. Power BI File: Download the .pbix file.
2. Dataset: Download the Excel dataset.
3. Screenshots: All visuals included in this report.
4. Public Dashboard Link: Access the published dashboard here.

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Contact

Feel free :😃 to connect with me on LinkedIn or explore my GitHub profile for more projects:

• LinkedIn Profile or email thomas.nnyanumba.com