account-health

name

account-health

description

Assess the health of an account from its recent conversations — sentiment trajectory and risk signals — backed by an engagement timeline and an account_research snapshot. Identify the account by ZoomInfo company ID (preferred) or name/domain (triggers a lookup). Use when someone asks "how healthy is Acme", "are we at risk of churn here", "what's the sentiment trend", or wants a risk read before a QBR or renewal. Strictly evidence-based: it does not invent risk or generic advice, and it asks the user for input where the conversations do not settle the question.

Account Health

A grounded read on where an account's health is heading: the sentiment trend, the real risk signals, and what to do about them — only what the evidence supports.

Prerequisites

browse_engagements (timeline) requires an active calendar/email/meeting integration; conversation_intelligence (sentiment/risk) requires at least one connected source; conversation_intelligence and account_research consume AI credits. If no conversation data exists, the health read is limited to CRM/firmographic signal — say so explicitly rather than implying a confident verdict.

Input

Provided via $ARGUMENTS:

• Account (required) — ZoomInfo company ID (preferred), or a name/domain to resolve via search_companies.
• Context (optional but valuable) — anything the user knows that conversations won't show: renewal timing, recent escalations, exec sponsor changes, usage/adoption data. Ask for this if a health call hinges on it.

Workflow

1. Resolve the account. Use the ZoomInfo ID directly, or resolve a name/domain via search_companies.
2. Gather evidence. Build a recent timeline with browse_engagements (cadence and any drop-off in contact), run conversation_intelligence for sentiment trajectory and risk signals across recent conversations, and pull an account_research snapshot for deal/relationship context. Keep CI scoped to the account; it sees only the last few engagements and cannot count or topic-search, so treat its read as recent signal, not a full trend line.
3. Check before concluding. If the evidence is thin or mixed, or a verdict depends on something the data does not show (renewal date, usage, an off-platform escalation), ask the user for that input before writing the assessment. Do not fill gaps with generic churn-risk boilerplate.
4. Assess. Give a health read with a clear direction and a confidence level, every claim tied to specific evidence. Separate what the data shows from what is inferred or assumed.

Output Format

Account health — [Company]

Read — one line: healthy / watch / at-risk, with a confidence level (and why confidence is what it is).

Sentiment trajectory — how tone and engagement have moved across recent conversations, with source moments.

Risk signals — specific, evidence-backed signals (a gone-quiet champion, an unresolved escalation, slipping cadence). Omit anything you cannot support; do not pad.

Strengths — what is genuinely going well, if anything, with evidence.

Recommended actions — one to three concrete, evidence-tied moves. If the evidence does not support a confident recommendation, say what to confirm first instead.

Evidence discipline

Lead with what the conversations and data actually show. Mark inferences as inferences. Where you asked the user for input, fold their answer in and attribute it. A short, honest read beats a padded one.

When there is no data

If conversation data is unavailable, give the CRM/firmographic view only, state that sentiment and conversational risk could not be assessed, and point the user to their ZoomInfo admin.