Customer Churn Analysis
EDA & Analytics for Customer Retention
An exploratory data analysis project focused on understanding customer churn patterns. Uses data analysis and visualization to identify factors that influence customer retention and provide actionable business insights.
Overview
Customer churn is one of the most important metrics for subscription-based businesses. This project dives deep into customer data to understand why customers leave, what patterns exist among churned customers, and what factors are most strongly associated with retention.
The Problem
Businesses lose significant revenue to customer churn but often lack clear visibility into why customers leave or which customers are at risk. Without data-driven insights, retention strategies are based on guesswork.
The Solution
Performed comprehensive exploratory data analysis on customer data — cleaning, analyzing, and visualizing patterns to surface actionable insights about churn drivers and customer segments most at risk.
Features
Tech Stack
📈 Identified key churn drivers through data-driven analysis
Architecture
What I Learned
This project strengthened my ability to extract meaningful insights from raw data. I learned how to ask the right analytical questions, choose appropriate visualizations for different data types, and translate data findings into business-relevant conclusions.