How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction

Xiao Han; Yao Xiao; Chenyu Wu; Tongchen Zhang

cs.LGJul 1, 2026

How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction

Authors: Xiao Han, Yao Xiao, Chenyu Wu, Tongchen Zhang

Organizations: Goizueta Business School Emory University Atlanta, GA, USA · College of Computing Georgia Institute of Technology Atlanta, GA, USA · Pratt School of Engineering Duke University Durham, NC, USA · Independent Researcher Seattle, WA, USA

Abstract

How many days of early behavior suffice for subscription churn prediction? In the public KKBox dataset, the early indicator of churn is typically an indicator of someone's contract status; however, when looking in the heavily churned manual-renewal segment, having access to early behavior creates a substantial increase in prediction for that specific segment (PR +0.10 at 120 days). A nine-window sufficiency curve shows diminishing returns; descriptive inflection detectors place a knee between 30 and 120 days depending on the metric and model class. Stress-testing over three cohort/task designs shows that this curve is singular to the design being tested; for example, in our test with a moving target, the curve inverts and can shift depending on the feature set used. Therefore, any window-sufficiency claim should state its cohort construction, target definition, and feature families. A survival-filtered replication on educational-attrition data (OULAD) confirms the rising-curve shape in one behavior-dominated setting (floor ROC 0.56 vs. 0.88), though signal composition inverts.

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Jun 4, 2026 · cs.LGJ/K move · Enter open · S save

A Rolling-Window Framework for Churn Prediction and Behavioral Driver Identification

Muhammad Jawad Mufti, Omar Hammad, Haitham Saleh, Muqaddas Gull

Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement is not explicitly observed and must be inferred from behavioral inactivity. Existing churn prediction approaches often rely on simplified temporal assumptions or single-point representations of customer behavior, which limit their ability to support continuous risk assessment, interpretability, and realistic deployment over time. This study proposes a temporally explicit churn prediction framework that models customer behavior using rolling behavioral windows, enabling repeated and instance-level churn risk estimation as customer activity evolves. Customer behavior is summarized within a fixed 30-day observation window, followed by a 30-day future churn evaluation window, ensuring a clear temporal separation between behavioral evidence and churn outcomes. The framework integrates feature-based and sequence-based learning approaches within a unified temporal design. The proposed approach is evaluated on a large-scale, real-world dataset from a non-contractual service platform. Empirical results demonstrate strong and stable predictive performance, with accuracy reaching 87.6% and ROC-AUC of 0.94 for the feature-based model, while the sequence-based model achieves recall as high as 96.1% by capturing temporal disengagement patterns. Evaluation on future unseen data confirms meaningful robustness under temporal shift, with accuracy remaining above 83% and ROC-AUC exceeding 0.91 without model retraining. Overall, the findings highlight that carefully designed temporal framing, rather than model complexity alone, is critical for achieving robust, interpretable, and deployment-ready churn prediction. The study provides a practical foundation for churn-oriented decision support in dynamic service environments.

Churn Prediction Time Window Temporal Logic Specifications Temporal Dynamics Sequence Modeling Auc-Roc

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Abstract

Explore similar work

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