How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction
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Computer Science > Machine Learning
Title:How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction
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 a diminishing-returns knee in a 45-90 day band. However, 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. All evidence is from one music-streaming dataset; the mechanism should generalize but the magnitudes may not.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.00473 [cs.LG] |
| (or arXiv:2607.00473v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00473
arXiv-issued DOI via DataCite (pending registration)
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