Author: Rehana Hassan Muhumed
Program: IBM Data Analyst Professional Certificate
Date: May 2026
No installation required — open the dashboard and explore customer insights instantly.
This project focuses on Customer Segmentation using RFM Analysis — a powerful marketing technique used by businesses to identify their most valuable customers and improve customer retention strategies.
The dashboard analyzes customer purchasing behavior based on:
| Metric | Meaning | Business Importance |
|---|---|---|
| 🔵 Recency | How recently a customer purchased | Recent customers are more likely to buy again |
| 🟢 Frequency | How often a customer purchases | Frequent customers are usually loyal |
| 🟡 Monetary | How much money a customer spends | High spenders generate more revenue |
By combining these three metrics, customers are grouped into meaningful business segments such as VIP customers, loyal customers, at-risk customers, and new customers.
✔ Analyze customer purchasing behavior
✔ Perform RFM calculations using Python
✔ Create meaningful customer segments
✔ Build interactive visual dashboards using Plotly & Dash
✔ Provide business recommendations for each segment
✔ Deploy the dashboard online using Render
| Technology | Purpose |
|---|---|
| Python 3.13 | Core programming language |
| Pandas | Data cleaning and manipulation |
| NumPy | Numerical calculations |
| Matplotlib | Static charts |
| Seaborn | Data visualization |
| Plotly | Interactive visualizations |
| Dash | Web dashboard framework |
| Jupyter Notebook | Analysis environment |
| Render | Cloud deployment |
| Git & GitHub | Version control and portfolio hosting |
customer-segmentation/
│
├── 📁 dashboard/
│ ├── app.py
│ ├── customer_data.csv
│ └── requirements.txt
│
├── 📁 data/
│ ├── online_retail_II.xlsx
│ └── rfm_data.csv
│
├── 📁 images/
│ ├── segment_distribution.png
│ └── segment_profiles.png
│
├── 📁 notebooks/
│ └── 01_rfm_analysis.ipynb
│
├── 📁 reports/
│ └── segmentation_report.txt
│
├── 📄 README.md
├── 📄 render.yaml
├── 📄 requirements.txt
└── 📄 runtime.txt| Segment | Description | Customer Share | Recommended Action |
|---|---|---|---|
| 🔴 VIP Customers | High spenders who buy frequently and recently | 15% | Loyalty rewards & premium support |
| 🟠 Loyal Customers | Consistent repeat buyers | 25% | Referral and cross-sell campaigns |
| 🟡 At-Risk Customers | Previously active but inactive recently | 35% | Re-engagement campaigns |
| 🟢 New Customers | Recently joined customers | 25% | Welcome and onboarding series |
| 🔵 Regular Customers | Average purchasing behavior | — | Nurture campaigns |
| ⚫ Inactive Customers | No purchases for a long time | — | Win-back promotions |
| Feature | Description |
|---|---|
| 📌 Segment Filter | Filter dashboard by customer segment |
| 💰 Monetary Slider | Filter customers by spending amount |
| 📊 KPI Cards | Revenue, customers, orders, recency |
| 🔵 Scatter Plot | Recency vs Monetary analysis |
| 🥧 Pie Chart | Segment distribution |
| 📉 Bar Charts | Compare segment metrics |
| 💡 Recommendations | Segment-specific business actions |
| 📋 Data Table | Explore customer records |
| ⬇ Download Button | Export filtered data to CSV |
VIP Customers → 15%
Loyal Customers → 25%
At-Risk Customers → 35%
New Customers → 25%
- VIP customers generate the highest revenue despite being a smaller group.
- At-Risk customers represent the largest percentage and require immediate attention.
- Loyal customers have strong repeat purchase behavior.
- New customers need onboarding campaigns to increase retention.
High-value customers should receive premium experiences.
- Offer exclusive loyalty rewards
- Provide early access to new products
- Send personalized thank-you messages
- Create VIP memberships with free shipping
- Invite customers to beta testing and surveys
Loyal customers help maintain consistent revenue.
- Launch referral programs
- Provide cross-selling recommendations
- Reward repeat purchases with points
- Offer personalized promotions
These customers were active before but may stop purchasing.
- Send re-engagement emails
- Offer special discounts (20–30%)
- Run “We Miss You” campaigns
- Remind them about unused rewards
New customers need onboarding and education.
- Send welcome email sequences
- Provide first-purchase discounts
- Share tutorials and product guides
- Ask for reviews after purchase
========================================
CUSTOMER SEGMENTATION REPORT
========================================
Analysis Date: 2026-04-28
Total Customers Analyzed: 5,000
========================================
SEGMENT SUMMARY
========================================
VIP Customers:
- Count: 750 customers (15.0%)
- Average Recency: 15 days
- Average Frequency: 12.5 purchases
- Average Monetary: $2,500
Loyal Customers:
- Count: 1,250 customers (25.0%)
- Average Recency: 30 days
- Average Frequency: 6.2 purchases
- Average Monetary: $1,200
At-Risk Customers:
- Count: 1,750 customers (35.0%)
- Average Recency: 90 days
- Average Frequency: 8.0 purchases
- Average Monetary: $800
========================================
END OF REPORT
========================================
Before running the project locally, install:
- Python 3.13+
- Jupyter Notebook
- Git (optional)
# 1. Clone repository
git clone https://github.com/rihhanna/customer-segmentation.git
# 2. Enter project folder
cd customer-segmentation
# 3. Install dependencies
pip install pandas numpy matplotlib seaborn plotly dash
# 4. Run Jupyter Notebook
jupyter notebook notebooks/01_rfm_analysis.ipynb
# 5. Launch Dashboard
cd dashboard
python app.pyhttp://127.0.0.1:8050
The dashboard is deployed using Render.com free cloud hosting.
services:
- type: web
name: customer-segmentation-dashboard
runtime: python
rootDir: dashboard
buildCommand: pip install -r requirements.txt
startCommand: gunicorn app:server| Project | Description |
|---|---|
| 📊 Telco Customer Churn Analysis | Customer churn prediction project |
| 📈 Sales Performance Dashboard | Interactive sales analytics dashboard |
| 🦠 COVID-19 Data Pipeline | ETL and analytics pipeline |
| 🎯 Customer Segmentation | RFM customer analysis dashboard |
| Information | Details |
|---|---|
| 🎓 Program | IBM Data Analyst Professional Certificate |
| 💼 Focus | Data Analytics & Visualization |
| 🌍 Location | Somalia |
| 💻 GitHub | https://github.com/rihhanna |
| https://linkedin.com/in/rehana-hassan | |
| 📚 Interests | Data Analytics, Dashboards, Machine Learning |
Special thanks to:
- IBM for the Data Analyst learning path
- Open Source Community for amazing Python libraries
- Render for free deployment services
- Plotly & Dash for interactive visualizations
| Phase | Status |
|---|---|
| ✅ Data Collection | Complete |
| ✅ Data Cleaning | Complete |
| ✅ RFM Analysis | Complete |
| ✅ Customer Segmentation | Complete |
| ✅ Dashboard Development | Complete |
| ✅ Deployment | Complete |
| ✅ Documentation | Complete |
If you found this project useful:
- ⭐ Star the repository on GitHub
- 🔗 Share the dashboard with others
- 💬 Provide feedback and suggestions
- 🤝 Connect with me on LinkedIn