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The objective is to understand customer purchasing behaviors and group them into segments that can be targeted with personalized marketing strategies.\n\n## 📊 Project Objectives\n\n- Calculate Recency, Frequency, and Monetary metrics for each customer.\n- Assign RFM scores based on business rules.\n- Segment customers based on RFM scores.\n- Provide actionable insights to improve customer engagement and retention.\n\n## 📁 Dataset\n\nThe dataset contains customer order information including:\n- Customer ID\n- Frequency of purchases\n- Recency (days since last purchase)\n- Total monetary value of purchases\n\n## ⚙️ Tools Used\n\n- Python (pandas, numpy)\n- Microsoft Excel\n- RFM scoring logic\n- Documentation in Word format\n\n## 🔍 Methodology\n\n1. Preprocessed the transaction data to extract R, F, and M metrics.\n2. Applied percentile-based segmentation to assign scores (1-3 scale).\n3. Calculated the RFM score by combining individual scores.\n4. Segmented customers into groups (e.g., high value, loyal, at-risk).\n5. Derived key business insights.\n\n## 🧠 Key Insights\n\n- 33% of customers purchased within the last 49 days.\n- Customers with RFM scores like `333`, `331`, etc. are highly engaged and valuable.\n- Customers with scores like `111`, `112` may need win-back strategies.\n\n## 📌 Project Files\n\n- `Orders.xlsx` – Source dataset with RFM calculation.\n- `RFM_Analysis_Project_Documentation.docx` – Full project report.\n- `README.md` – Project overview and instructions.\n\n## 📈 Potential Extensions\n\n- Add customer segmentation visualizations (bar charts, heatmaps).\n- Build dashboards using Tableau or Power BI.\n- Deploy customer segmentation model using Streamlit or Flask.\n\n---\n\n**Author:** Ayan Jawaid  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Frfm-based-customer-profiling-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayaanjawaid%2Frfm-based-customer-profiling-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Frfm-based-customer-profiling-/lists"}