Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions

Abstract

In today’s fiercely competitive business environment, the ability to accurately predict customer churn is essential for enhancing customer retention and reducing financial losses. Traditional statistical approaches, although beneficial, often struggle to perform effectively across diverse customer data domains due to variability in data distributions. This research introduces a refined method for predicting customer churn that utilizes domain adaptation techniques to overcome these challenges. Specifically, it employs the Correlation Alignment (CORAL) method to synchronize the feature distributions between the source and target datasets, significantly improving the logistic regression model’s capacity to apply insights across various customer segments. The process involves segmenting the customer data into clearly defined clusters using the k-means algorithm, which helps pinpoint and adjust distributional discrepancies, thus boosting the model’s accuracy. Early results indicate that incorporating domain adaptation not only bolsters the model’s applicability across different domains but also drastically minimizes the covariance differences—from a substantial initial gap to nearly zero. This strategic approach demonstrates substantial potential to revolutionize how businesses anticipate and manage customer behavior, providing a more adaptable and effective framework for addressing the complex challenges of customer churn.

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