Customer Churn Prediction Analysis
Customer retention is a critical challenge for subscription-based businesses. In this project, I conducted an in-depth analysis to predict customer churn and provide data-driven strategies to enhance retention rates.
Project Overview:
To tackle this problem, I collected and processed large datasets from CRM systems, transaction records, and customer support interactions. Using Python (Pandas, NumPy), I cleaned the data, handled missing values, and prepared it for analysis.
Key Steps Taken: Exploratory Data Analysis (EDA): Leveraged Matplotlib and Seaborn to visualize trends, identify patterns, and understand key factors influencing churn.
Feature Engineering: Created new variables such as customer engagement frequency, payment behavior, and support ticket history to improve predictive accuracy.
Machine Learning Implementation: Developed Logistic Regression and Random Forest models using Scikit-Learn, optimizing them with GridSearchCV and handling data imbalance with SMOTE.
Performance Evaluation: Assessed models using ROC-AUC scores, precision-recall curves, and confusion matrices to ensure reliable predictions.
Data Visualization & Reporting: Designed an interactive Tableau dashboard to present insights to stakeholders, enabling them to track churn trends and customer behaviors.
Business Recommendations: Proposed retention strategies such as personalized marketing campaigns and proactive customer engagement, leading to a 15% reduction in churn within three months.