This repository contains a Power BI project focused on analyzing budget and sales data. The project aims to provide insights into company performance by comparing actual sales against budgeted targets, extracting key metrics, and visualizing trends to support data-driven decision making.
- Project Overview
- Dataset
- Features
- Tools and Technologies
- Installation
- Usage
- Insights and Conclusions
- Contributing
- Contact
This project analyzes sales budget data to extract meaningful information about products and customers based on multiple features. It includes data cleaning (ETL), exploratory data analysis (EDA), and the creation of an interactive Power BI dashboard to review company performance.
Key objectives include:
- Extract, transform, and load (ETL) the sales and budget dataset
- Perform exploratory data analysis using Python
- Compare actual sales with budgeted sales and calculate variances
- Identify key metrics and relationships between sales, products, customers, and regions
- Build interactive dashboards to visualize performance trends and support decision making
The dataset includes detailed sales and budget information by product, customer, region, and time period. It contains features such as sales amounts, budgeted targets, quantities ordered, customer demographics, and order dates.
Files included:
Budget and Sales Data.xlsx— Raw budget and sales databudget_and_sales_data_hqtl.xlsx— Cleaned and processed dataset
- Interactive Power BI dashboard visualizing budget vs. actual sales
- Analysis of sales trends over time, by product category, and by region
- Customer segmentation and profiling based on purchasing behavior
- Identification of peak sales periods and high-profit categories
- Visualization of key metrics such as sales variance, order quantities, and profit margins
- Microsoft Power BI Desktop for data modeling and visualization
- Python (for data cleaning and exploratory analysis)
- Excel for dataset storage and preprocessing
- Ensure you have Power BI Desktop installed.
- Clone or download this repository.
- Open the Power BI file
sales.pbixwith Power BI Desktop.
- Explore the interactive dashboards to analyze sales performance against budget.
- Use slicers and filters to drill down by product, region, customer segment, and time period.
- Update the dataset with your own data by replacing the Excel files and refreshing the Power BI model.
- Significant sales growth observed in 2016, especially in bike category products.
- Customers aged 40-59 form a large portion of the clientele with high demand.
- Highest profits recorded in June, November, and December.
- Sales orders peak on Wednesdays and Saturdays.
- Negative correlation between price and quantity ordered observed.
- Customer retention improved slightly in 2016 compared to 2014.
- High school diploma holders with moderate income tend to purchase more than those with bachelor's degrees.
- Approximately 15% of customers are high-value clients, with the majority being low-value or lost clients.
Contributions are welcome! If you find any bugs, have suggestions, or want to add features, please fork the repository and submit a pull request.
For questions, feedback, or collaboration, please reach out via GitHub: ferdos-coder
Thank you for visiting this repository! This project serves as a comprehensive resource for budget and sales data analysis using Power BI.