This project showcases a comprehensive analysis of customer churn for a fictional telecom company called Databel. Customer churn; when customers stop using a company's service is a critical metric for any business aiming to grow sustainably. Understanding why customers leave and identifying patterns in their behavior allows organizations to take proactive steps to reduce churn and improve customer retention.

Using Power BI, I have created an interactive dashboard that visualizes key metrics such as churn rate, reasons for churn, customer demographics, and service usage patterns. This analysis leverages various data visualizations including bar charts, pie charts, and geographical maps and leverage DAX formula to derive insight to uncover insights that help pinpoint the main drivers behind customer churn.

What you will find in this project:

• Calculated measures created with DAX for dynamic reporting
• Interactive filters and slicers for deeper data exploration
• Combined visualizations to present a clear overview of customer behavior
• Insights into customer service interactions, contract types, and extra charges impacting churn

This project highlights how data visualization and analytics can empower decision-makers to improve customer satisfaction and reduce churn in a competitive marketplace.

Key Insights

1. Overall Churn Rate & Customer Segments
   • The overall churn rate stands at 26.86%, which is relatively high and signals the need for immediate retention efforts.
   • Customers on month-to-month contracts and those paying via direct debit exhibit higher churn and more frequent customer service interactions.
2. Churn Reasons Breakdown
   • The main reasons for churn include competitor offers, competitors having better devices or service, and dissatisfaction with support staff.
   • Competitor-related factors form the largest category of churn causes, indicating competitive pressure is a key challenge.
3. Contract Types and Impact on Churn
   • Month-to-month contract holders have the highest churn rates.
   • Customers with two-year contracts show significantly lower churn rates, highlighting the positive impact of longer commitments on retention.
4. Customer Service Interaction and Churn
   • Frequent callers to customer service tend to churn more often than average.
   • This underscores the importance of improving service quality and addressing customer issues promptly to reduce churn.
5. Geographical Patterns
   • California emerges as a unique case: it has the highest churn rate but the lowest average customer service calls.
   • This suggests potential untapped opportunities for targeted retention and engagement efforts.
6. Extra Charges & Data Usage
   • Customers not enrolled in unlimited data plans but with high data consumption incur significant extra data charges.
   • These additional costs may be a factor contributing to their churn.
7. Demographics and Age Bins
   • Churn rates increase notably beyond a certain age threshold.
   • However, shifting customers to longer contract durations (one- or two-year) appears to mitigate churn even among older customer segments.

Analysis Process