Customer churn is a major business concern for telecom companies because acquiring a new customer is often more expensive than retaining an existing one. This project analyzes customer-level telecom data to identify churn patterns, understand the factors associated with customer exits, and provide recommendations that can help improve customer retention.
Telecom companies lose revenue when customers discontinue their services. The objective of this project was to analyze customer churn data, identify the customer groups most likely to leave, and suggest actions that could help improve customer retention.
- Cleaned and validated the telecom customer dataset in Excel.
- Imported the cleaned data into MySQL and handled blank values in the TotalCharges field.
- Used SQL to calculate churn rate, compare churn across customer segments, and identify potential revenue at risk.
- Built an interactive Power BI dashboard to track churn KPIs and explore churn by tenure, contract type, payment method, monthly charges, and customer demographics.
- Customers with lower tenure were more likely to churn, showing that the early customer journey is important for retention.
- Customers on month-to-month contracts had higher churn compared to customers on long-term contracts.
- Churned customers generally had higher monthly charges, suggesting that pricing or perceived value may influence their decision to leave.
- Customers using electronic check payments showed higher churn and can be considered a high-risk segment.
- Churn creates a measurable monthly revenue risk, making retention efforts financially valuable.
- Improve onboarding and support for customers in their first 6–12 months.
- Offer discounts or loyalty benefits to encourage customers to move from month-to-month to longer contracts.
- Target high-charge, low-tenure customers with personalized plans or retention offers.
- Improve payment convenience and communication for customers using electronic checks.
- Create a high-risk customer list so retention teams can take action before customers churn.
- Microsoft Excel — Data cleaning and validation
- MySQL — Data import and churn analysis using SQL
- Microsoft Power BI — Interactive dashboard and KPI reporting
This project demonstrates how customer data can be used to identify churn patterns and support retention-focused business decisions. The analysis suggests that customer tenure, contract type, monthly charges, and payment behavior are important areas to monitor. By focusing on early-stage customers, high-charge plans, and month-to-month contract users, the telecom company can create targeted retention strategies and reduce potential revenue loss.