Scores
Subscriberbot distills the graph into a handful of scores — derived signals that make health, risk, and reliability legible at a glance. Scores are computed from observed data; they are not self-reported.
The four scores
Subscription Health Score (user-facing)
How healthy a given Relationship is for you. It blends usage, cost, satisfaction, and engagement. A premium tool you never open scores low; a daily-use tool at a fair price scores high. The Health Score is what drives "consider cancelling" and "consider downgrading" recommendations.
Subscription Credit Score (provider-facing)
A measure of a subscriber's reliability from the provider's perspective — renewal reliability, payment consistency, and utilization. It is designed to help providers understand the quality of a relationship.
Vendor Health Score
How healthy a provider is as a vendor — reliability, support quality, security posture, and pricing stability. Organizations use it to compare vendors and flag risky dependencies.
AI Churn Prediction (provider-facing)
A forward-looking prediction of how likely a subscriber is to cancel, surfaced to providers through the Provider Success Platform. It is the input to retention automation.
At a glance
| Score | Audience | Inputs |
|---|---|---|
| Subscription Health | User | usage, cost, satisfaction, engagement |
| Subscription Credit | Provider | renewal reliability, payment consistency, utilization |
| Vendor Health | Organization | reliability, support, security, pricing stability |
| Churn Prediction | Provider | behavioral and engagement signals |
Using scores
Scores are first-class inputs to the AI Brain and to automations. A policy can reference a score directly — for example, "flag any vendor whose Vendor Health Score drops below a threshold for review."