{"id":26215866,"url":"https://github.com/scarblase/sales_insights","last_synced_at":"2026-05-05T00:39:42.468Z","repository":{"id":277566053,"uuid":"932833689","full_name":"scarblase/sales_insights","owner":"scarblase","description":"A data-driven analysis of 15,000 sales records using Python, Pandas, and visualizations to uncover trends, optimize strategies, and enhance business performance. 🚀📊","archived":false,"fork":false,"pushed_at":"2025-02-22T16:16:12.000Z","size":918,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"origin","last_synced_at":"2026-05-05T00:38:58.472Z","etag":null,"topics":["data-analysis","data-visualization","dataset","matplotlib-pyplot","pandas","python3","sales-analysis","seaborn"],"latest_commit_sha":null,"homepage":"https://www.datacamp.com/portfolio/tmhomenko","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/scarblase.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-14T15:53:14.000Z","updated_at":"2025-03-02T12:09:30.000Z","dependencies_parsed_at":"2025-03-12T11:18:10.607Z","dependency_job_id":"94126180-5a8b-4d61-b0c6-b3a742d9753b","html_url":"https://github.com/scarblase/sales_insights","commit_stats":null,"previous_names":["scarblase/portfolio_project","scarblase/sales_insights"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/scarblase/sales_insights","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fsales_insights","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fsales_insights/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fsales_insights/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fsales_insights/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scarblase","download_url":"https://codeload.github.com/scarblase/sales_insights/tar.gz/refs/heads/origin","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scarblase%2Fsales_insights/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32631058,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-04T10:08:07.713Z","status":"ssl_error","status_checked_at":"2026-05-04T10:08:02.005Z","response_time":58,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-visualization","dataset","matplotlib-pyplot","pandas","python3","sales-analysis","seaborn"],"created_at":"2025-03-12T11:18:11.098Z","updated_at":"2026-05-05T00:39:42.439Z","avatar_url":"https://github.com/scarblase.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sales Performance Analysis \u0026 Insights\n\n## 📌 Project Overview\nThis 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.\n\n## 📂 Repository Structure\n```\n📦 Portfolio_project\n├── 📄 README.md  # Project documentation\n├── 📊 product_sales.csv  # Sales dataset\n├── 📓 updated_notebook.ipynb  # Jupyter Notebook with full analysis\n```\n\n## 📜 Dataset Description\nThe dataset consists of **15,000 records** with the following columns:\n\n| Column Name       | Description |\n|------------------|-------------|\n| `week`            | Week of the year |\n| `sales_method`    | Sales method used (Email, Call, Email + Call) |\n| `customer_id`     | Unique customer identifier |\n| `nb_sold`         | Number of items sold |\n| `revenue`         | Revenue generated (in USD) |\n| `years_as_customer` | Years the customer has been with the company |\n| `nb_site_visits`  | Number of times the customer visited the website |\n| `state`           | Customer's state |\n\n## 📊 Key Insights \u0026 Findings\n✅ **Email is the most used sales method**, followed by calls and email + call.  \n✅ **Revenue distribution is right-skewed**, with a few high-value transactions driving most revenue.  \n✅ **Sales volume is positively correlated with revenue** – selling more leads to higher revenue.  \n✅ **Key Metric: Average Revenue per Sale (ARPS)** – tracks sales efficiency (**$9.28 per sale**).  \n✅ **Supplemental Metric: Revenue Per Customer (RPC)** – measures customer value (**$93.62 per customer**).  \n✅ **Multi-channel approach (email + call) shows strong potential** for improving sales performance.  \n\n## 🚀 Business Recommendations\n🔹 **Optimize email campaigns** with better targeting \u0026 personalization.  \n🔹 **Monitor ARPS \u0026 RPC** to track trends and sales efficiency.  \n🔹 **Encourage multi-channel (email + call) strategies** for higher conversions.  \n🔹 **Focus on high-value customers** through segmentation \u0026 personalized offers.  \n🔹 **Experiment with pricing \u0026 promotions** to increase revenue per sale.  \n\n## ⚙️ Installation \u0026 Setup\nTo run the analysis, follow these steps:\n\n### 1️⃣ Clone the repository\n```bash\ngit clone https://github.com/scarblase/Portfolio_project.git\ncd Sales-Performance-Analysis\n```\n\n### 2️⃣ Install dependencies\nEnsure you have Python installed, then run:\n```bash\npip install pandas numpy matplotlib seaborn jupyter\n```\n\n### 3️⃣ Open the Jupyter Notebook\n```bash\njupyter notebook\n```\nThen, open `notebook.ipynb` and run the cells.\n\n## 🛠️ Tools \u0026 Technologies\n- **Python** 🐍\n- **Pandas** for data manipulation\n- **Matplotlib \u0026 Seaborn** for visualization\n- **Jupyter Notebook** for interactive analysis\n\n## 🎯 Future Improvements\n- Add **predictive modeling** to forecast revenue trends.\n- Include **A/B testing analysis** to evaluate different sales strategies.\n- Automate **monthly sales reports** for better tracking.\n\n## 🤝 Contributing\nHave suggestions? Feel free to open an issue or submit a pull request! 😊\n\n## 📧 Contact\nFor any questions, reach out via [LinkedIn](https://www.linkedin.com/in/bkhomenko/) or email.\n\n**🚀 Let’s optimize sales with data!**\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscarblase%2Fsales_insights","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscarblase%2Fsales_insights","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscarblase%2Fsales_insights/lists"}