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| Part of [[The Playbook]]. Before you can run a play, you need to know something's wrong. Observability is the monitoring layer of Customer Success: a customer health score that tells you the state of an account before the customer has to tell you themselves.
| | #REDIRECT [[Adopt]] |
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| ==What actually goes into a health score==
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| A health score that only measures sentiment is a lagging indicator dressed up as a dashboard. The useful ones combine:
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| * '''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.
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| * '''Support signal''' — ticket volume and severity trend, not a raw count. A spike after a quiet stretch matters more than a steady baseline.
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| * '''Relationship and engagement''' — meeting attendance, response latency, whether the champion is still the champion or quietly changed jobs.
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| * '''Commercial signal''' — contract terms, upcoming renewal date, prior expansion or contraction history.
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| * '''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.
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| 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.
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| 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.
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| ==Leading vs. lagging==
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| Leading indicators (usage decline, feature abandonment, unanswered emails) predict a problem before it's visible. Lagging indicators (a formal complaint, a support escalation, a churn notice) confirm a problem that's already arrived. A health score built entirely on lagging indicators is just a churn report with extra steps — weight leading indicators higher, and treat lagging indicators as confirmation, not discovery.
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| ==Score bands and what they trigger==
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| A workable default, tuned per business:
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| {| class="wikitable"
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| ! Band !! Meaning !! Action
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| | 0–40 || Critical || [[Incident Response for At-Risk Accounts|Declare an incident]]
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| | 41–60 || At-risk || Run the matching [[The Play Library|play]], proactively
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| | 61–80 || Healthy || Standard cadence, no special action
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| | 81–100 || Thriving || Flag for expansion, hand to the growth motion
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| |}
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| ==What breaks health scores==
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| * '''Overcomplication.''' A score with 40 weighted inputs is a score nobody trusts and nobody can explain to a customer's exec sponsor. Fewer inputs, clearly defensible, beats a black box every time.
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| * '''Static snapshots.''' A single number hides the story. Track the trend — an account moving from 90 to 65 over three weeks is a different problem than one sitting at 65 for a year.
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| * '''Mistaking activity for engagement.''' Logins aren't adoption. A user opening the product every day to check one report they could get from an email digest is not a healthy account, even though the login graph looks great.
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| ==Where this feeds==
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| The score sets the trigger for [[The Play Library]] and defines the target in [[SLOs for Customer Outcomes]]. When a score crosses into critical, it's the input to [[Incident Response for At-Risk Accounts]].
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| ==Sources==
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| * [https://www.gainsight.com/blog/customer-health-scores/ Gainsight — "Customer Health Score Explained: Metrics, Models & Tools"] — 4–6 signal guidance
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| * [https://churnzero.com/blog/customer-health-scores-in-the-age-of-ai/ ChurnZero — "Customer Health Scores in the Age of AI"]
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| [[Category:Customer Success Manager]]
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| [[Category:Customer Engineering]]
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