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. 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 framewor
The proliferation of subscription and pay-per-use services, coupled with advances in AI/ML, necessitates more sophisticated, continuous churn prediction methods.
Improved churn prediction directly impacts customer retention and revenue optimization for businesses in competitive, non-contractual service environments.
This framework offers a more dynamic and interpretable approach to identifying churn risk and its behavioral drivers, moving beyond static, single-point analyses.
- · Customer analytics providers
- · Subscription-based businesses
- · Customer relationship management (CRM) software companies
- · Businesses relying on outdated churn prediction models
- · Inefficient marketing departments
Companies can proactively address customer disengagement through targeted interventions.
Enhanced customer lifetime value and stabilized revenue streams will become more achievable.
The integration of such models could lead to more personalized and adaptive service offerings, further raising customer expectations.
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Read at arXiv cs.LG