Churn Prediction under Feature Drift in Subscription Analytics Platforms: Benchmark Study

Abstract

The rapid proliferation of the subscription economy has fundamentally transformed consumer behavior and corporate revenue models across global markets. In this context, predicting customer churn has emerged as a critical operational imperative for subscription analytics platforms. However, the predictive performance of machine learning models deployed in these environments is often severely compromised by feature drift, a phenomenon where the statistical properties of input variables change over time due to shifting consumer preferences, economic conditions, or platform updates. This paper presents a comprehensive benchmark study that systematically evaluates the impact of feature drift on churn prediction algorithms within subscription analytics. By designing a robust evaluation framework, we analyze the degradation of predictive accuracy across various traditional and contemporary machine learning architectures when exposed to synthetic and naturally occurring feature drift. Our experimental methodology incorporates diverse drift profiles, including gradual, sudden, and recurring shifts, to simulate real-world volatility. The findings indicate that while complex ensemble models and deep learning architectures exhibit superior baseline performance, they are frequently more susceptible to feature drift compared to regularized linear models unless specifically coupled with adaptive learning mechanisms. This benchmark provides a foundational reference for practitioners and researchers to design more resilient churn prediction systems, ultimately enhancing customer retention strategies in dynamic subscription environments.