Customer Churn Prediction & Retention Analytics

Overview

Developed a machine learning-based customer churn prediction system to identify customers at high risk of leaving a service. The project combines predictive analytics and business intelligence to support customer retention strategies through data-driven decision making.

Objectives

Analyze customer behavior and churn patterns.
Predict customer churn using machine learning techniques.
Identify key factors influencing churn.
Generate actionable business insights for retention planning.

Tech Stack

Python
Pandas
NumPy
Scikit-Learn
XGBoost
Power BI
SQL
Matplotlib
Seaborn

Methodology

Performed data cleaning and preprocessing on customer data.
Conducted exploratory data analysis to identify churn trends.
Engineered relevant features for improved model performance.
Trained and evaluated multiple classification models.
Selected XGBoost as the final model based on predictive performance.
Built interactive Power BI dashboards for business reporting.

Key Insights

Customers with shorter tenure showed higher churn probability.
Month-to-month contract users exhibited significantly higher churn rates.
Customers without support-related services were more likely to leave.
Payment and service usage patterns played an important role in churn behavior.

Business Impact

The solution enables organizations to proactively identify at-risk customers and implement targeted retention strategies, helping improve customer loyalty and reduce revenue loss.