At-risk accounts surfaced early
Weakening engagement, slipping usage and rising support load are picked up while there is still time to act, rather than at renewal.
— Sales & Growth
Client Health Pulse
Keeps a standing weekly read on the health of every client account, scoring each one on the signals that actually predict trouble, tracking how it moves over time and putting the accounts that need attention in front of the person who owns them.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~25 minutes
For a full population, not a sample
No coding required
- Input
- Your account list with the clients to cover
- Output
- A health score for every account with the reasoning behind it
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Client Health Pulse for
Movement, not just a snapshot
Because each week is recorded on the same basis, you can see which accounts are genuinely deteriorating and which had a quiet fortnight.
Owners see their own book
Each account owner gets the accounts that are theirs, with the reason behind the score, so the follow-up is specific rather than a general warning.
A consistent read for leadership
Leadership gets the same short weekly view of where the client base stands, without anyone assembling it by hand.
What it works from
- Your account list with the clients to cover
- The engagement signals you want weighed — usage, support load, meeting cadence, renewal dates
- Who owns each account for follow-up
What you get back
- A health score for every account with the reasoning behind it
- A week-on-week view of how the health mix is shifting
- An at-risk list per owner and a short leadership read
— HOW IT BEHAVES
How Client Health Pulse produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the pipeline in scope
Each row of your export is processed on the same basis, so no account is skipped however long the table is.
The pattern charted
Movement across the pipeline in scope is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the pipeline in scope are written up as a document that reads like professional output, with each claim tied back to a account.
Each account enriched in place
Derived columns are added row by row, keeping your source data and the judgement about each account side by side.
Roll-up alongside account-level detail
Detail rows are summarised into the grouped view of the pipeline in scope without losing the underlying accounts.
Why this is expensive by hand
Account health is usually known unevenly: one owner senses a relationship cooling, another notices a renewal creeping up, and leadership only hears about it when the account is already in difficulty. At-risk accounts surfaced early is the typical trigger — weakening engagement, slipping usage and rising support load are picked up while there is still time to act, rather than at renewal. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 weeks of experienced attention.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces a health score for every account with the reasoning behind it plus a week-on-week view of how the health mix is shifting. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. The practical effect: 2–3 weeks of manual work becomes a ~25 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Revenue and commercial leaders
- Sales operations and enablement teams
- Customer success and account management leads
- Consultants advising on go-to-market
Run it in Skillsize — or export it anywhere
Client Health Pulse exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
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