Customer Churn & Revenue Intelligence Dashboard
Business Problem Subscription-based companies lose revenue due to customer churn. This project analyzes telecom customer data to identify churn drivers, quantify revenue at risk, and provide actionable retention insights through an executive dashboard.
Objectives
- Calculate churn rate and revenue exposure
- Identify key churn drivers (contract type, tenure, payment method)
- Segment customers by revenue risk
- Build an interactive Power BI dashboard for decision-makers
- Develop a baseline churn prediction model
Tools & Technologies -Python – Data cleaning, feature engineering, churn analysis, logistic regression -SQL – KPI calculations and business queries -Power BI – Executive dashboard & interactive visualizations
Key Insights -Month-to-month contracts show significantly higher churn -Electronic check payments are associated with higher churn probability -Short-tenure customers are at highest risk -High-value churners contribute disproportionately to revenue loss
Business Impact This analysis helps prioritize retention strategies by identifying high-risk, high-revenue customers and uncovering behavioral churn patterns.