How retention-scoring with contract-age, signal-quality, and support-frequency signals surfaces churn risk in time to do something about it.
The old way. A customer raises three support tickets in a month, the last one with a frustrated tone. Six weeks later they cancel. In the post-cancellation review, finance notices the contract was paid on time every month and the support tickets were all closed within SLA. Nobody had a flag system that said this customer was at risk.
What ISPCQ does. Aelita's retention-scoring runs daily across the customer base. Signals include: contract age, recent ticket frequency and tone, signal-quality trend, billing-payment punctuality, plan-change history. Customers crossing a configurable risk threshold appear in the supervisor's daily-flagged list with a one-paragraph explanation of why the score moved.
The operational outcome. Outreach happens before the cancellation form gets submitted. The customer-success team has a focused list of fifty customers per month, not a randomised slice of three thousand.
One of twenty-two detailed articles on real ISP workflows. Each walks through the problem, what teams used to do, what ISPCQ does, and the operational outcome. The architecture is the same; the workflows differ.