The Reasons for Returns Data Analysis project aims to scrutinize and derive insights from customer return data to understand the underlying reasons for product returns. By conducting a comprehensive analysis of return data, the project aims to uncover the key factors contributing to product returns.
- Remove Duplicates: Eliminated duplicate values from the dataset.
- Format Modification: Adjusted data formats for numbers and dates.
- Fill Missing Data: Populated empty cells in the "adjustment_reason" column based on the available data.
- Fill Missing Data: Populated empty cells in the "simplified_return_reason" column based on the available data.
- Pivot Tables:
- Created Pivot table to count the total number of returned quantity based on simplified return reason.
- Created Pivot table to count the total number of returned quantity based on city.
- Created Pivot table to count the total number of returned quantity based on town.
- Developed a dynamic dashboard for interactive data exploration and visualization.
- Clone the repository to your local machine.
- Open the Excel file for detailed data cleaning and analysis steps.
- Explore the dynamic dashboard for visualizations and insights.
The project delivers actionable insights into the reasons for product returns, enabling businesses to make informed decisions to reduce return rates and enhance customer satisfaction. The use of Excel ensures a thorough analysis, and the dynamic dashboard provides an interactive way to explore the data.
Reasons For Returns Data Analysis
Feel free to contribute, provide feedback, or use the project as a reference for your own reasons for returns data analysis endeavors.
Happy Analyzing! 📊