https://github.com/rightfulcode/customer-segmentation-rfm

This project performs customer segmentation using Recency, Frequency, and Monetary (RFM) metrics to identify key customer groups and provide actionable marketing insights.
https://github.com/rightfulcode/customer-segmentation-rfm

data-analysis-python data-visualization elevvo-internship jupyter-notebook matplotlib pandas python rfm-analysis seaborn

Last synced: 5 months ago
JSON representation

This project performs customer segmentation using Recency, Frequency, and Monetary (RFM) metrics to identify key customer groups and provide actionable marketing insights.

• Host: GitHub
• URL: https://github.com/rightfulcode/customer-segmentation-rfm
• Owner: RightfulCode
• Created: 2025-09-24T07:54:04.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-09-24T08:00:35.000Z (6 months ago)
• Last Synced: 2025-09-24T10:11:00.813Z (6 months ago)
• Topics: data-analysis-python, data-visualization, elevvo-internship, jupyter-notebook, matplotlib, pandas, python, rfm-analysis, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 21.5 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Task 3: Customer Segmentation Using RFM Analysis

This project is part of my **Elevvo Internship (Data Analytics Track)**.  

The 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.  

## 📂 Dataset

- Source: [Online Retail Dataset (UCI)](https://archive.ics.uci.edu/dataset/352/online+retail)  

- Data includes invoice details, customer IDs, quantities, unit prices, and purchase dates.

## 🛠️ Tools & Libraries

- Python

- Pandas

- Matplotlib

- Seaborn

- Jupyter Notebook

## 🔎 Key Steps

1. **Data Loading** – Import the Excel dataset into a Pandas DataFrame  

2. **Data Cleaning** – Remove missing CustomerIDs, filter negative/zero quantities or prices, convert data types for memory efficiency  

3. **Calculating RFM Metrics** – Compute Recency, Frequency, and Monetary value per customer  

4. **Assigning RFM Scores** – Score each metric from 1–4 based on quartiles  

5. **Segmentation** – Classify customers into segments like Champions, Loyal Customers, At Risk, and Others  

6. **Data Visualization** – Create bar charts and heatmaps to analyze RFM patterns  

7. **Insights & Marketing Suggestions** – Provide actionable recommendations for each segment  

## 📊 Key Insights

- **Champions:** High recency, frequency, and monetary — top customers to reward  

- **Loyal Customers:** Frequent buyers — encourage repeat purchases and cross-selling  

- **At Risk:** Inactive or infrequent buyers — target with re-engagement campaigns  

- **Others:** Moderate or new customers — monitor and engage lightly  

## 📈 Visualizations

### Customer Segment Distribution

![Customer Segment Distribution](images/customer_segments_distribution.png)

### Monetary Value by Recency & Frequency

![Monetary Heatmap](images/rfm_heatmap.png)

## ▶️ How to Run

- Install dependencies: `pip install -r requirements.txt`  

- Open the notebook: `jupyter notebook Customer_Segmentation_RFM.ipynb`