Predicting Customer Churn in Subscription-Based Services

Introduction

Predicting customer churn is a critical task for subscription-based businesses aiming to retain customers and reduce revenue loss. This project proposal presents a machine learning framework for predicting churn, inspired by recent research on the topic.

Background

Customer churn prediction involves identifying customers who are likely to discontinue their subscriptions. It is a vital metric for subscription businesses as retaining existing customers is more cost-effective than acquiring new ones. Machine learning models have proven effective in analyzing customer behavior patterns and predicting churn, allowing businesses to implement targeted retention strategies.

Project Objective

The primary objective of this project is to develop a predictive model that accurately identifies customers at risk of churning. By analyzing historical customer data, the model aims to provide actionable insights that can help businesses enhance customer retention efforts.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Telco Customer Churn dataset from IBM, which contains information about customer demographics, account information, and churn status.
• Data Cleaning: Handle missing values and normalize data to ensure consistency across features.

2. Model Development

• Feature Engineering: Extract relevant features such as tenure, monthly charges, and payment methods.
• Machine Learning Algorithms: Implement various algorithms like logistic regression, decision trees, and random forests to identify the most effective model for churn prediction.

3. Training and Evaluation

• Training: Split the dataset into training and testing sets to build and validate the model.
• Evaluation Metrics: Use metrics such as accuracy, precision, recall, and F1-score to assess model performance.

Expected Outcomes

The proposed model is expected to provide accurate predictions of customer churn, enabling businesses to proactively address potential issues and improve customer satisfaction. By leveraging machine learning techniques, the project aims to enhance the efficiency of retention strategies.

Conclusion

This project seeks to advance the understanding of customer churn dynamics in subscription-based services through the application of machine learning models. By accurately predicting churn, businesses can implement effective strategies to retain customers and maintain a competitive edge.

For further details on related research, please refer to the paper "Predicting Customer Churn in Subscription-Based Services" available at ScienceDirect.

Dataset Link: Telco Customer Churn Dataset