arXiv:2607. 00473v1 Announce Type: new Abstract: How many days of early behavior suffice for subscription churn prediction?
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 06776v1 Announce Type: new Abstract: 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.
The paper investigates how seasonal labeling in customer churn and decline early‑warning systems can misclassify seasonal activity as decline, leading to false alarms. By aligning the baseline to the same calendar months one year earlier, the authors demonstrate a significant improvement in model performance (ROC‑AUC rises from 0.767 to 0.864) and a reduction in the number of flagged accounts. The study quantifies the extent of the problem across public and production datasets and shows that the proposed correction reduces intervention load while maintaining predictive accuracy.
arXiv:2608. 11555v1 Announce Type: new Abstract: Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-point-process (TPP) literature follows suit with covariate- and external-covariate-conditioned intensities.
arXiv:2608. 14367v1 Announce Type: cross Abstract: The early detection of delayed cases in business processes is a critical capability for organizations.
arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.
arXiv:2606. 30664v1 Announce Type: cross Abstract: The coupon incentive is one of the most common tools marketers use to court users to engage with a business at various stages of the customer life cycle.