Adidas, a globally renowned sportswear brand, sought to monitor and optimize key business metrics to drive strategic decision-making. These metrics included Average Order Value (AOV), Customer Acquisition Cost (CAC), Total Sales, and Total Profit. Accurate tracking and analysis of these metrics were crucial for understanding business performance and implementing effective strategies.
The challenge lay in the necessity to thoroughly study and understand the data before creating a functional and insightful dashboard. This involved extensive data cleaning, structuring, and analysis to ensure the final metrics were accurate and reliable. Additionally, there was a need for a comprehensive process to handle and visualize the data effectively.
To address these complications, a multi-step approach was implemented:
- Data Cleaning with Python:
- The initial dataset was loaded and examined for inconsistencies, missing values, and errors.
- Using Python, the data was cleaned to handle missing values, correct inconsistencies, and prepare it for analysis. This step ensured the integrity and reliability of the data.
- Database Management with SQLite3:
- The cleaned data was then pushed into an SQLite3 database. This step facilitated efficient data management and querying.
- An SQLite3 database provided a structured environment to store and manage the data, enabling streamlined access and manipulation using SQL queries.
- Data Querying with SQL:
- SQL queries were employed to extract, transform, and finalize the data required for calculating key business metrics.
- Specific queries were designed to calculate the Average Order Value (AOV), Customer Acquisition Cost (CAC), Total Sales, and Total Profit from the dataset.
- Visualization with PowerBI:
- The finalized data was imported into PowerBI to create an interactive and comprehensive dashboard.
- PowerBI’s visualization tools were used to develop charts, graphs, and KPIs, providing a clear and concise view of the business metrics.
- The dashboard layout was designed to present the metrics in an intuitive and accessible manner, allowing stakeholders to easily interpret and utilize the data.
Process:
- Data Cleaning: Ensured data accuracy by handling missing values and correcting inconsistencies.
- Database Setup: Structured data storage and management using SQLite3.
- Data Analysis: Extracted and calculated key metrics using SQL queries.
- Visualization: Created an interactive dashboard in PowerBI to present the metrics clearly.
The implementation of this approach resulted in a robust and interactive dashboard that provided Adidas with valuable insights into their business performance. The dashboard effectively displayed the key metrics:
- Average Order Value (AOV): Provided insights into the average revenue per order.
- Customer Acquisition Cost (CAC): Highlighted the cost efficiency of acquiring new customers.
- Total Sales: Showcased the overall sales performance over time.
- Total Profit: Illustrated the profitability after accounting for costs.
By leveraging this dashboard, Adidas was able to:
- Optimize Marketing Efforts: By understanding the CAC, Adidas could refine their marketing strategies to reduce costs and increase customer acquisition efficiency.
- Enhance Sales Strategies: Insights from AOV and Total Sales metrics helped in identifying sales trends and optimizing product offerings.
- Improve Profitability: Analysis of Total Profit enabled Adidas to implement cost-saving measures and enhance overall profitability.
This case study demonstrates the importance of a structured approach to data handling and visualization in deriving actionable business insights and driving strategic decision-making.
