Editing Adopt (section)

==Observability: the health score==

You can't manage adoption you can't see. A customer health score is the monitoring layer of Customer Success — it tells you the state of an account before the customer has to tell you themselves. A health score that only measures sentiment is a lagging indicator dressed up as a dashboard. The useful ones combine:

* '''Product usage and adoption depth''' — not just "did they log in," but which features, how deep, and whether usage is trending up or down. This is the highest-weight signal, because usage drops before satisfaction scores do.
* '''Support signal''' — ticket volume and severity trend, not a raw count. A spike after a quiet stretch matters more than a steady baseline.
* '''Relationship and engagement''' — meeting attendance, response latency, whether the champion is still the champion or quietly changed jobs.
* '''Commercial signal''' — contract terms, upcoming renewal date, prior expansion or contraction history.
* '''CSM/TAM pulse''' — the qualitative gut-check from the human who actually talks to the account. Don't let the model override this; let it flag disagreement instead.

That's five, and five is close to the ceiling, not the floor. Gainsight's own published guidance lands on 4–6 signals as the practical range — usage, support trend, sentiment, executive engagement — and every dimension past that adds noise, not accuracy. Drop any signal the team has no ability to influence, however clean its data source is; a health score is a tool for action, not a trivia dashboard.

A real example of why blending beats any single signal: support-ticket volume alone routinely misdirects attention. Teams that scored health on raw ticket count found their loudest, highest-ticket accounts were often their most engaged customers — while quieter, genuinely at-risk accounts generated no signal at all and slipped through unnoticed. Blending usage alongside support volume catches what raw ticket counts hide.