Quantifying Hidden Revenue Drain: A Churn-Path Decomposition Framework for Predictive Leakage Control in Subscription Ecosystems

Abstract

Subscription-based businesses increasingly suffer from hidden revenue leakage that is not fully captured by aggregate churn metrics. This study
proposes a novel churn-path decomposition framework grounded in principles of Data Science and Business Analytics to identify, quantify, and
predict revenue leakage across customer lifecycle trajectories. Using a dataset of 120,000 users from a simulated SaaS platform, customer
journeys were modeled as sequential behavioral paths and analyzed using Markov chain transitions and sequence clustering techniques. The
results reveal that 68% of total revenue leakage originates from just three dominant churn paths, primarily involving early-stage disengagement
and payment friction. The proposed model improves leakage attribution accuracy by 34% compared to traditional churn-rate analysis and enables
early detection of high-risk users with an AUC score of 0.87. Targeted interventions designed using churn-path insights reduced projected
monthly revenue loss by 21.5% and improved customer retention by 17%. These findings demonstrate that path-based churn analytics provides
significantly deeper diagnostic power than aggregate metrics, allowing firms to proactively mitigate revenue loss. The study contributes a
scalable, data-driven framework for optimizing retention strategies and enhancing profitability in subscription-driven markets.