https://github.com/data-edd/sales_performance_analysis
Sales performance analysis from 2018-2021
https://github.com/data-edd/sales_performance_analysis
Last synced: 8 months ago
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Sales performance analysis from 2018-2021
- Host: GitHub
- URL: https://github.com/data-edd/sales_performance_analysis
- Owner: data-edd
- Created: 2025-02-09T18:09:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-04T15:54:18.000Z (about 1 year ago)
- Last Synced: 2025-03-04T16:31:18.979Z (about 1 year ago)
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sales Performance Analysis - Power BI
## Description
This project analyzes sales performance over the years 2018-2021 using Power BI. The dashboard provides insights into customer purchasing behavior, regional sales distribution, and trends over time to help businesses make data-driven decisions.
## Dataset Information
**Source:** Sales transaction and customer database
**Format:** Excel / CSV
### Tables Used:
#### Customer Table
- City
- Customer Category
- Customer ID
- Province/State
#### Invoice Table
- Customer Code
- Date
- Description
- Invoice ID
- Quantity
- Sales
- Transaction ID
## Installation & Requirements
To set up and explore the Power BI analysis, ensure you have the following tools installed:
### Required Tools:
- Power BI Desktop
- Data source connections (Excel, CSV)
## Usage
To analyze the data and explore insights:
1. Clone the repository:
```sh
git clone https://github.com/data-edd/sales-performance-dashboard.git
```
2. Open the `SALES-PERFORMANCE-DASHBOARD.pbix` file in Power BI Desktop.
3. Refresh the dataset to load the latest data.
4. Explore the interactive dashboards and reports.
## Key Metrics & Insights
This Power BI dashboard focuses on the following key performance indicators:
- **Sales by Customer Category:** Understanding revenue contributions from different customer segments.
- **Quantity by Customer Category:** Analyzing product demand among customer categories.
- **Sales by Province/State:** Geographical distribution of sales.
- **Quantity and Sales by Year:** Trends in sales and product movement over time.
- **Year-to-Date (YTD) Sales:** Tracking cumulative sales for the current year.
- **Year-on-Year (YoY) Sales:** Comparing annual sales performance to identify growth trends.
## Methodology
The analysis follows a structured approach:
1. **Data Import & Transformation:** Data cleaning and preparation using Power Query.
2. **Exploratory Data Analysis (EDA):** Identifying trends, patterns, and relationships.
3. **Data Modeling:** Establishing relationships between tables and creating DAX measures.
4. **Visualization:** Presenting insights through Power BI dashboards.
## Results & Insights
### Key findings from the analysis include:
- **High Sales in Specific Customer Categories:** Certain customer segments contribute significantly to revenue, suggesting potential for targeted marketing efforts.
- **Regional Sales Disparities:** Some provinces/states consistently outperform others in sales, indicating opportunities for expansion or targeted sales strategies.
- **Seasonal Trends in Sales:** Notable fluctuations in sales figures throughout the year suggest seasonal demand patterns.
- **Customer Buying Behavior:** Analysis shows that repeat customers generate a significant portion of sales, emphasizing the importance of customer retention strategies.
- **YoY Growth Trends:** While sales have grown year-over-year, certain periods show stagnation or decline, highlighting areas for improvement.
## Contributions
Contributions are welcome! If you’d like to contribute:
1. Fork the repository.
2. Create a new branch (`feature-branch-name`).
3. Commit your changes and push to the branch.
4. Submit a pull request.
## Contact
For any inquiries or support, feel free to open an issue or contact [mr.armstrongedward@gmail.com](mailto:mr.armstrongedward@gmail.com).