This project analyzes customer churn behavior using SQL and Tableau.
The goal is to identify key factors that influence customer churn and provide actionable business insights.
- Source: Telco Customer Churn dataset (Kaggle)
- Records: ~7,000 customers
- Features include:
- Contract type
- Monthly charges
- Customer demographics
- Churn status
- SQL (PostgreSQL)
- Tableau (Dashboard & Visualization)
- DBeaver
View Interactive Dashboard on Tableau: https://public.tableau.com/views/Customerchurnanalysis_17770716825330/Dashboard1?:language=en-US&publish=yes&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
- 📌 Customers with month-to-month contracts have the highest churn (~43%)
- 💰 Customers who churn tend to have higher monthly charges
- 👵 Senior customers are significantly more likely to churn (~42%)
These insights can help businesses:
- Improve retention strategies
- Offer better pricing plans
- Target high-risk customer segments
The analysis shows that contract type is the strongest driver of churn, with month-to-month customers being significantly more likely to leave.
Customers who churn tend to have higher monthly charges, suggesting potential pricing dissatisfaction. Additionally, senior customers are at a higher risk of churn compared to non-senior customers.
Overall, the results highlight key customer segments that require targeted retention strategies to reduce churn and improve long-term customer value.