Skene
PLG term

Customer health score

A customer health score is an opinionated, weighted index of signals that indicate how likely an account is to renew, expand, or churn. In PLG, health scores rely heavily on product usage data rather than just subjective sentiment. A good health score clarifies where to focus your success and sales efforts instead of treating all customers the same.

Retention
Also called: Account health score, Customer health index
About this term

This page is part of the Skene PLG glossary. Use it as a reference when writing specs, dashboards, or playbooks that rely on this concept.

Canonical glossary index: /resources/glossary

What is a customer health score?

A customer health score is a single metric, often on a 0–100 or red/amber/green scale, that represents how healthy a customer relationship is.

It combines multiple signals such as product usage, adoption breadth, support tickets, NPS, and commercial data (e.g. contract value or tenure) into one view.

Common components of a health score

Product usage: frequency of logins, depth of feature adoption, completion of key onboarding journeys.

Business value: outcomes achieved, active projects, usage of value-driving features, and realized ROI.

Engagement: support interactions, stakeholder participation, and responsiveness to success outreach.

Customer health scores in a PLG motion

In PLG, health scores should be strongly anchored in product behavior rather than subjective opinions alone.

A well-calibrated health score lets you prioritize success outreach, renewal conversations, and expansion plays based on real usage and outcomes.

How Skene feeds better health scores

Skene automatically measures completion of journeys, milestones, and key PLG metrics like activation, time-to-value, and feature adoption.

Those signals can be used directly as inputs into your health score, making it easier to keep the score aligned with what actually drives retention and expansion.

Implementation notes

  • Start with a simple model (e.g., a handful of weighted signals) and validate it against historical churn and expansion before adding complexity.
  • Make sure every component of the score is measurable, up to date, and clearly understood by sales and success teams.