This project analyses donor management data for a social development organisation to identify trends, improve donor retention, and optimise fundraising strategies. The analysis covers donor segmentation, campaign performance, retention rates, and financial forecasting using Python, SQL, and Power BI.
Business Problem: Many NGOs struggle with donor retention — acquiring new donors is 5x more expensive than retaining existing ones. This project helps identify at-risk donors, high-value segments, and the most effective campaigns to improve retention and programme funding.
- What is the overall donor retention rate year-on-year?
- Which donor segments contribute the most to programme funding?
- Which campaigns have the highest ROI?
- Which donors are at risk of lapsing?
- How does donation frequency correlate with donor lifetime value?
- What is the programme vs non-programme expense ratio?
| Metric | Value |
|---|---|
| Overall Donor Retention Rate | 72% → 89% (improved by 17%) |
| Top Donor Segment | Corporate CSR Partners (45% of total funds) |
| Best Performing Campaign | Braille Books Initiative (ROI: 340%) |
| At-Risk Donors Identified | 23% of donor base |
| Programme Expense Ratio | 85% programme / 15% admin |
| Average Donation Growth YoY | 12% |
| Reporting Time Reduction | 35% through automation |
donor_management_project/
│
├── README.md # Project overview (this file)
├── data/
│ └── donor_data_sample.csv # Sample anonymised donor dataset
├── sql/
│ └── donor_queries.sql # SQL queries for data extraction
├── analysis/
│ └── donor_analysis.py # Python analysis script
├── dashboard/
│ └── dashboard_screenshots/ # Power BI dashboard screenshots
│ ├── donor_overview.png
│ ├── retention_analysis.png
│ └── campaign_performance.png
└── reports/
└── donor_insights_report.md # Summary insights report
| Tool | Purpose |
|---|---|
| Python (Pandas, Matplotlib, Seaborn) | Data cleaning, analysis, visualisation |
| SQL | Data extraction and transformation |
| Power BI | Interactive dashboard development |
| Excel (Power Query) | Data preparation and pivot analysis |
| GitHub | Version control and portfolio |
The dataset contains anonymised donor records with the following fields:
| Column | Description |
|---|---|
| donor_id | Unique donor identifier |
| donor_type | Individual / Corporate / Foundation |
| donation_amount | Amount donated (INR) |
| donation_date | Date of donation |
| campaign_name | Campaign associated with donation |
| region | Donor geographic region |
| donation_frequency | One-time / Monthly / Annual |
| retention_status | Active / Lapsed / At-Risk |
| programme_area | Education / Health / Livelihood |
Key queries used in this project — see sql/donor_queries.sql for full code:
- Donor retention rate calculation by year
- Top donors by lifetime value
- Campaign performance analysis
- At-risk donor identification
- Programme vs admin expense ratio
Key analyses performed — see analysis/donor_analysis.py for full code:
- Data cleaning and validation
- Donor segmentation (RFM Analysis — Recency, Frequency, Monetary)
- Retention trend analysis
- Campaign ROI calculation
- Visualisations: bar charts, heatmaps, trend lines
The Power BI dashboard includes 4 report pages:
- Donor Overview — Total donors, active vs lapsed, YoY growth
- Retention Analysis — Retention rates by segment, at-risk donors
- Campaign Performance — ROI, funds raised per campaign
- Financial Summary — Programme vs admin expense ratio, budget tracking
Based on the analysis, the following recommendations were made:
- Re-engagement Campaign — Target the 23% at-risk donors with personalised outreach within 30 days
- Corporate CSR Focus — Expand corporate partnerships as they contribute 45% of funding with higher retention
- Monthly Giving Programme — Convert one-time donors to monthly giving — 3x higher lifetime value
- Braille Books Expansion — Highest ROI campaign (340%) — scale budget allocation by 20%
- Automated Reporting — Implement automated monthly donor reports to reduce manual effort by 35%
Nasreen Mohamed Data Analyst | Business Intelligence | MIS & CRM Specialist 📧 nasreenfousal@gmail.com 🔗 LinkedIn 📍 Sharjah, UAE
This project is based on anonymised data from a social development organisation. All donor information has been modified to protect privacy.