mlsanityData to 14 Oct 2026

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

arXiv 2607.00473 ↗First posted 1 Jul 2026cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 29 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

6 of 29 references.

References · 6

  1. 2023An Integrated Transportation Distance Between Kernels and Approximate Dynamic Risk Evaluation in Markov Systemscited by 9
  2. 2026The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Managementcited by 6
  3. 2016Rapid Prediction of Player Retention in Free-to-Play Mobile Gamescited by 3
  4. 2015Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyaltycited by 1
  5. 2023Early Churn Prediction from Large Scale User-Product Interaction Time Seriescited by 1
  6. 2026Event history analysis with time-dependent covariates via landmarking supermodels and boosted treescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.