Abstract

Customer churn is a critical challenge for businesses aiming to maintain long-term growth and profitability. Traditional methods of churn prediction are often limited by their inability to incorporate complex patterns and relationships within large, high-dimensional datasets. This presentation explores the application of machine learning techniques, specifically classification algorithms, to predict customer churn more accurately and effectively. We will discuss the key stages involved in developing a churn prediction model, including data collection, preprocessing, feature engineering, and model selection. A comparison of popular machine learning models, such as logistic regression, decision trees, and ensemble methods like random forests and gradient boosting, will be presented. Emphasis will be placed on evaluating model performance using metrics such as precision, recall, F1-score, and ROC-AUC, highlighting the importance of balancing false positives and false negatives in the context of customer retention. Additionally, we will address the challenges of handling imbalanced datasets and strategies for overcoming these issues, such as the use of synthetic data and advanced resampling techniques. Finally, the presentation will conclude with insights into model deployment and integration into customer relationship management systems to provide actionable insights that can drive targeted retention strategies. By leveraging machine learning, businesses can proactively identify at-risk customers and reduce churn, leading to improved customer retention and business sustainability.