Monthly retention risk review
Provides a recurring view of customer sentiment, including what has changed since previous reporting periods and which concerns deserve closer attention.
— Sales & Growth
Customer voice → churn signal monitor
Turns customer feedback into a churn-risk diagnostic, showing which segments are unhappy, what drives their concerns, when sentiment shifts and which accounts warrant attention.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
3–4 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Customer feedback from NPS surveys, support tickets, reviews and call notes
- Output
- Customer sentiment and churn-risk diagnostic brief
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~3–4 weeks per run
— USE CASES
What people use Customer voice → churn signal monitor for
Segment dissatisfaction diagnosis
Shows which customer segments express the most dissatisfaction and which themes drive it. A segment-by-theme heatmap helps focus retention discussions on specific problems.
Account intervention prioritisation
Highlights the forty most negative customer voices with the key phrases behind their assessment, giving account teams an evidence-based shortlist for review.
Emerging sentiment shift assessment
Identifies the month average sentiment changes and projects its direction, helping teams assess whether customer concerns are isolated or part of a broader deterioration.
What it works from
- Customer feedback from NPS surveys, support tickets, reviews and call notes
- Customer identifiers, segment labels, feedback dates and source details
- Previous reporting-period briefs, where available
What you get back
- Customer sentiment and churn-risk diagnostic brief
- Sentiment assessment across six retention-related themes, where mentioned
- Segment sentiment averages and cohort comparisons
- Segment-by-theme sentiment heatmap ModelElement
— HOW IT BEHAVES
How Customer voice → churn signal monitor 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 end-to-end process
Each row of your export is processed on the same basis, so no process step is skipped however long the table is.
The pattern charted
Movement across the end-to-end process is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the end-to-end process are written up as a document that reads like professional output, with each claim tied back to a process step.
Roll-up alongside process step-level detail
Detail rows are summarised into the grouped view of the end-to-end process without losing the underlying process steps.
Why this is expensive by hand
Customer dissatisfaction is often scattered across survey comments, support tickets, reviews and call notes, making it hard to distinguish isolated complaints from emerging retention risks. In practice it shows up as monthly retention risk review: provides a recurring view of customer sentiment, including what has changed since previous reporting periods and which concerns deserve closer attention. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 3–4 weeks of senior time whenever it comes round.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; customer sentiment and churn-risk diagnostic brief comes out, alongside sentiment assessment across six retention-related themes, where mentioned. 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. The practical effect: 3–4 weeks of manual work becomes a ~40 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
Customer voice → churn signal monitor 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)
Related Skill Templates
More Sales & Growth templates →Sales & Growth
Single Customer View
Brings CRM, billing, support and product usage together with a live business source and external research to create one attributed customer brief for account planning and client conversations.
Sales & Growth
Campaign Optimisation Engine
Evaluates live advertising and partner campaign performance against targets, forecasts and recent trends, producing focused recommendations with approval-controlled changes and a record of their impact.
Sales & Growth
Campaign Prediction Engine
Judges a planned campaign before spend is committed, testing its audience, offer, channel mix and budget against how comparable campaigns actually performed, then giving a clear go, amend or hold call with the reasoning and confidence behind it.
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
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
Forecast risk & pipeline hygiene audit
Tests every open deal for stalled activity, unholdable close dates, single-threading and missing next steps, then computes the forecast dollars at risk and an action list per rep.