Telecom Customer Churn & Retention

Telecom Customer Churn & Retention Analysis

Description:

This project involved analyzing a dataset of 7,043 customers to identify key drivers of churn and develop actionable retention strategies. I built an end-to-end Power BI dashboard that transforms raw service data into high-level business intelligence.

Key Features & Insights:

Churn Risk Identification: Developed DAX measures to track 1,869 "Risk Customers," revealing that 26.54% of the user base was at risk of leaving.

Service Impact Analysis: Identified that customers without Online Security had a significantly higher churn rate (31.3%) compared to other segments.

Behavioral Segmentation: Analyzed churn by contract type and payment method, finding that senior citizens showed a 48.3% early-leaving rate.

Technical Stack: Utilized PostgreSQL for data querying and Power BI for advanced data modeling, DAX calculations, and interactive visualization.

Business Impact: By identifying high-churn segments (like those without tech support or security services), this dashboard allows telecom managers to target specific groups with retention offers, potentially saving a portion of the $16.1M in total charges at risk.