https://github.com/ayaanjawaid/rfm-based-customer-profiling-
RFM-Based Customer Profiling for Business Insights
https://github.com/ayaanjawaid/rfm-based-customer-profiling-
customer-segmentation excel recency-frequency-monetary rfm-analysis
Last synced: 6 months ago
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RFM-Based Customer Profiling for Business Insights
- Host: GitHub
- URL: https://github.com/ayaanjawaid/rfm-based-customer-profiling-
- Owner: Ayaanjawaid
- License: mit
- Created: 2025-05-07T19:08:03.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-07T19:09:39.000Z (about 1 year ago)
- Last Synced: 2025-05-17T04:11:44.901Z (about 1 year ago)
- Topics: customer-segmentation, excel, recency-frequency-monetary, rfm-analysis
- Homepage:
- Size: 6.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer Segmentation Using RFM Analysis
This project demonstrates how to apply **RFM (Recency, Frequency, Monetary)** analysis for customer segmentation using order transaction data. The objective is to understand customer purchasing behaviors and group them into segments that can be targeted with personalized marketing strategies.
## 📊 Project Objectives
- Calculate Recency, Frequency, and Monetary metrics for each customer.
- Assign RFM scores based on business rules.
- Segment customers based on RFM scores.
- Provide actionable insights to improve customer engagement and retention.
## 📁 Dataset
The dataset contains customer order information including:
- Customer ID
- Frequency of purchases
- Recency (days since last purchase)
- Total monetary value of purchases
## ⚙️ Tools Used
- Python (pandas, numpy)
- Microsoft Excel
- RFM scoring logic
- Documentation in Word format
## 🔍 Methodology
1. Preprocessed the transaction data to extract R, F, and M metrics.
2. Applied percentile-based segmentation to assign scores (1-3 scale).
3. Calculated the RFM score by combining individual scores.
4. Segmented customers into groups (e.g., high value, loyal, at-risk).
5. Derived key business insights.
## 🧠 Key Insights
- 33% of customers purchased within the last 49 days.
- Customers with RFM scores like `333`, `331`, etc. are highly engaged and valuable.
- Customers with scores like `111`, `112` may need win-back strategies.
## 📌 Project Files
- `Orders.xlsx` – Source dataset with RFM calculation.
- `RFM_Analysis_Project_Documentation.docx` – Full project report.
- `README.md` – Project overview and instructions.
## 📈 Potential Extensions
- Add customer segmentation visualizations (bar charts, heatmaps).
- Build dashboards using Tableau or Power BI.
- Deploy customer segmentation model using Streamlit or Flask.
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**Author:** Ayan Jawaid