https://github.com/scarblase/sales_insights
A data-driven analysis of 15,000 sales records using Python, Pandas, and visualizations to uncover trends, optimize strategies, and enhance business performance. 🚀📊
https://github.com/scarblase/sales_insights
data-analysis data-visualization dataset matplotlib-pyplot pandas python3 sales-analysis seaborn
Last synced: 8 months ago
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A data-driven analysis of 15,000 sales records using Python, Pandas, and visualizations to uncover trends, optimize strategies, and enhance business performance. 🚀📊
- Host: GitHub
- URL: https://github.com/scarblase/sales_insights
- Owner: scarblase
- Created: 2025-02-14T15:53:14.000Z (9 months ago)
- Default Branch: origin
- Last Pushed: 2025-02-22T16:16:12.000Z (8 months ago)
- Last Synced: 2025-03-12T11:18:09.187Z (8 months ago)
- Topics: data-analysis, data-visualization, dataset, matplotlib-pyplot, pandas, python3, sales-analysis, seaborn
- Language: Jupyter Notebook
- Homepage: https://www.datacamp.com/portfolio/tmhomenko
- Size: 896 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sales Performance Analysis & Insights
## 📌 Project Overview
This project analyzes **15,000 sales records** to uncover insights into sales performance, customer behavior, and revenue trends. By leveraging **Python, Pandas, Matplotlib, and Seaborn**, we clean, explore, and visualize data to support **data-driven decision-making** for optimizing sales strategies.
## 📂 Repository Structure
```
📦 Portfolio_project
├── 📄 README.md # Project documentation
├── 📊 product_sales.csv # Sales dataset
├── 📓 updated_notebook.ipynb # Jupyter Notebook with full analysis
```
## 📜 Dataset Description
The dataset consists of **15,000 records** with the following columns:
| Column Name | Description |
|------------------|-------------|
| `week` | Week of the year |
| `sales_method` | Sales method used (Email, Call, Email + Call) |
| `customer_id` | Unique customer identifier |
| `nb_sold` | Number of items sold |
| `revenue` | Revenue generated (in USD) |
| `years_as_customer` | Years the customer has been with the company |
| `nb_site_visits` | Number of times the customer visited the website |
| `state` | Customer's state |
## 📊 Key Insights & Findings
✅ **Email is the most used sales method**, followed by calls and email + call.
✅ **Revenue distribution is right-skewed**, with a few high-value transactions driving most revenue.
✅ **Sales volume is positively correlated with revenue** – selling more leads to higher revenue.
✅ **Key Metric: Average Revenue per Sale (ARPS)** – tracks sales efficiency (**$9.28 per sale**).
✅ **Supplemental Metric: Revenue Per Customer (RPC)** – measures customer value (**$93.62 per customer**).
✅ **Multi-channel approach (email + call) shows strong potential** for improving sales performance.
## 🚀 Business Recommendations
🔹 **Optimize email campaigns** with better targeting & personalization.
🔹 **Monitor ARPS & RPC** to track trends and sales efficiency.
🔹 **Encourage multi-channel (email + call) strategies** for higher conversions.
🔹 **Focus on high-value customers** through segmentation & personalized offers.
🔹 **Experiment with pricing & promotions** to increase revenue per sale.
## ⚙️ Installation & Setup
To run the analysis, follow these steps:
### 1️⃣ Clone the repository
```bash
git clone https://github.com/scarblase/Portfolio_project.git
cd Sales-Performance-Analysis
```
### 2️⃣ Install dependencies
Ensure you have Python installed, then run:
```bash
pip install pandas numpy matplotlib seaborn jupyter
```
### 3️⃣ Open the Jupyter Notebook
```bash
jupyter notebook
```
Then, open `notebook.ipynb` and run the cells.
## 🛠️ Tools & Technologies
- **Python** 🐍
- **Pandas** for data manipulation
- **Matplotlib & Seaborn** for visualization
- **Jupyter Notebook** for interactive analysis
## 🎯 Future Improvements
- Add **predictive modeling** to forecast revenue trends.
- Include **A/B testing analysis** to evaluate different sales strategies.
- Automate **monthly sales reports** for better tracking.
## 🤝 Contributing
Have suggestions? Feel free to open an issue or submit a pull request! 😊
## 📧 Contact
For any questions, reach out via [LinkedIn](https://www.linkedin.com/in/bkhomenko/) or email.
**🚀 Let’s optimize sales with data!**