This project explores a sample customer subscription dataset to identify churn patterns and uncover actionable insights. Using Excel, the analysis applies pivot tables, calculated fields, and visualizations to summarize churn behavior by city, subscription type, and monthly revenue trends.

Project Objectives

• Identify the number of churned vs. active customers.
• Analyze churn rate by city, subscription type, and month.
• Highlight monthly revenue trends.
• Create a visual dashboard for executive insights.

💡 Key Metrics (KPIs)

• Total Customers: 20
• Churned Customers: 12
• Active Customers: 8
• Overall Churn Rate: 60.0%
• Average Monthly Revenue: $31.50

Key Insights

• New York had the highest churn rate at 75%.
• Standard subscriptions experienced the highest churn (80%).
• Churn was most concentrated in the months of June and October (100% churn).
• The average monthly revenue for churned customers is slightly higher than that of retained customers.

Dashboard Highlights

• Pie Chart: Churn Rate by City
• Bar Chart: Churn by Subscription Type
• Line Chart: Monthly Revenue Trend
• KPI Section: Quick view of total customers, churned, active, and revenue

Files Included

• customer-churn-analysis.xlsx: Cleaned and analyzed Excel workbook
• dashboard-customer-churn.png: Final dashboard screenshot
• churn-data.csv: Source dataset

Tools Used

• Microsoft Excel (Pivot Tables, Calculated Fields, Charts)
• Data Cleaning
• Dashboard Design

📷 Screenshot

GitHub Repository: View on GitHub

🔗 Project Link

View on GitHub

🧠 What I Learned

Advanced PivotTable techniques

• Visual storytelling in Excel
• Churn and customer behavior analysis
• Data cleaning and field transformation

📂 Folder Structure

📁 customer-churn-analysis

├── customer_churn_dataset.xlsx

├── dashboard-churn-analysis.png

└── README.md

📬 Contact

For questions or collaboration: GitHub: DonaM32