Renewal risk review
Highlights accounts with approaching renewals and deteriorating health signals. Gives account teams a tiered risk list with revenue exposure and a tailored retention proposal.
— Sales & Growth
Churn early-warning & save plan
Combines NPS movement, ticket velocity, usage drop-off and billing changes into a churn score per account, then drafts the save play with the ARR at risk quantified.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Account health data with account names and annual recurring revenue
- Output
- Tiered account churn-risk assessment
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Churn early-warning & save plan for
Retention resource allocation
Prioritises accounts by the relationship between save probability and revenue at stake, helping teams focus limited retention capacity on commercially meaningful opportunities.
Executive intervention planning
Provides account-specific retention plans with a proposed executive sponsor and offer, giving leadership a clear basis for targeted intervention.
Emerging account deterioration
Assesses changes in support demand alongside declining NPS, reduced usage and billing changes to surface accounts that warrant attention before renewal pressure builds.
What it works from
- Account health data with account names and annual recurring revenue
- Net Promoter Score changes
- Support ticket counts across comparison periods
- Product usage percentage changes and billing changes accompanying renewal dates
What you get back
- Tiered account churn-risk assessment
- Calculated changes in support ticket activity
- Annual recurring revenue at risk and calculated expected retention value
- Account priorities based on save probability and revenue exposure, with account-specific retention plans including proposed executive sponsors and offers
— HOW IT BEHAVES
How Churn early-warning & save plan 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
Churn signals often sit across customer feedback, support activity, product usage and billing records, making it difficult to distinguish urgent retention opportunities from routine account noise. Renewal risk review is the typical trigger — highlights accounts with approaching renewals and deteriorating health signals. Gives account teams a tiered risk list with revenue exposure and a tailored retention proposal. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 2–3 weeks of manual work.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; tiered account churn-risk assessment comes out, alongside calculated changes in support ticket activity. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. In effect, 2–3 weeks of senior time compresses into ~40 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
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
Churn early-warning & save plan 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.
ChatGPTClaudeCopilotAI Products (MCP)
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