{"id":31780629,"url":"https://github.com/rightfulcode/customer-segmentation-rfm","last_synced_at":"2026-05-08T14:01:58.814Z","repository":{"id":316382452,"uuid":"1063130093","full_name":"RightfulCode/Customer-Segmentation-RFM","owner":"RightfulCode","description":"This project performs customer segmentation using Recency, Frequency, and Monetary (RFM) metrics to identify key customer groups and provide actionable marketing insights.","archived":false,"fork":false,"pushed_at":"2025-09-24T08:00:35.000Z","size":22590,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-24T10:11:00.813Z","etag":null,"topics":["data-analysis-python","data-visualization","elevvo-internship","jupyter-notebook","matplotlib","pandas","python","rfm-analysis","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Task 3: Customer Segmentation Using RFM Analysis\n\nThis project is part of my **Elevvo Internship (Data Analytics Track)**.  \nThe objective was to perform **customer segmentation** on the Online Retail dataset using **RFM (Recency, Frequency, Monetary)** analysis to identify key customer groups and suggest actionable marketing strategies.  \n\n## 📂 Dataset\n- Source: [Online Retail Dataset (UCI)](https://archive.ics.uci.edu/dataset/352/online+retail)  \n- Data includes invoice details, customer IDs, quantities, unit prices, and purchase dates.\n\n## 🛠️ Tools \u0026 Libraries\n- Python\n- Pandas\n- Matplotlib\n- Seaborn\n- Jupyter Notebook\n\n## 🔎 Key Steps\n1. **Data Loading** – Import the Excel dataset into a Pandas DataFrame  \n2. **Data Cleaning** – Remove missing CustomerIDs, filter negative/zero quantities or prices, convert data types for memory efficiency  \n3. **Calculating RFM Metrics** – Compute Recency, Frequency, and Monetary value per customer  \n4. **Assigning RFM Scores** – Score each metric from 1–4 based on quartiles  \n5. **Segmentation** – Classify customers into segments like Champions, Loyal Customers, At Risk, and Others  \n6. **Data Visualization** – Create bar charts and heatmaps to analyze RFM patterns  \n7. **Insights \u0026 Marketing Suggestions** – Provide actionable recommendations for each segment  \n\n## 📊 Key Insights\n- **Champions:** High recency, frequency, and monetary — top customers to reward  \n- **Loyal Customers:** Frequent buyers — encourage repeat purchases and cross-selling  \n- **At Risk:** Inactive or infrequent buyers — target with re-engagement campaigns  \n- **Others:** Moderate or new customers — monitor and engage lightly  \n\n## 📈 Visualizations\n\n### Customer Segment Distribution\n![Customer Segment Distribution](images/customer_segments_distribution.png)\n\n### Monetary Value by Recency \u0026 Frequency\n![Monetary Heatmap](images/rfm_heatmap.png)\n\n## ▶️ How to Run\n- Install dependencies: `pip install -r requirements.txt`  \n- Open the notebook: `jupyter notebook Customer_Segmentation_RFM.ipynb`  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frightfulcode%2Fcustomer-segmentation-rfm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frightfulcode%2Fcustomer-segmentation-rfm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frightfulcode%2Fcustomer-segmentation-rfm/lists"}