Quarterly sales performance review
Provides a breakdown of lost annual recurring revenue by root cause, supported by a narrative of recurring loss patterns. Gives leadership three priority fixes to focus the performance discussion.
— Sales & Growth
Win/loss pattern synthesizer
Codes every closed-lost deal to a root cause, themes the language buyers actually used, and sums lost ARR per cluster so the revenue leakage behind each pattern is a real number.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~25 minutes
For a full population, not a sample
No coding required
- Input
- Closed-deal records with account names and outcomes
- Output
- Closed-lost deals categorised by root cause
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Win/loss pattern synthesizer for
Product and pricing discussions
Distinguishes price objections from feature gaps and other loss causes, with the lost revenue associated with each. Grounds commercial and product discussions in recorded deal evidence.
Buying process friction review
Highlights losses associated with procurement, timing, no decision and champion loss. Connects those patterns to buyer language so teams can understand the obstacles behind the categories.
Sales advisory diagnostic
Produces a consistent synthesis of fragmented loss notes, combining root-cause categorisation with revenue exposure. Gives advisers an evidence-based account of recurring issues and three priority recommendations.
What it works from
- Closed-deal records with account names and outcomes
- Annual recurring revenue values for each deal
- Free-text loss reasons and recorded buyer comments
What you get back
- Closed-lost deals categorised by root cause
- Revenue leakage table showing summed lost annual recurring revenue by cause
- Buyer-language themes drawn from loss notes
- Narrative of recurring loss patterns and their commercial significance within the recorded deals, without attributing causality beyond the evidence or claiming statistical significance not established by the data
— HOW IT BEHAVES
How Win/loss pattern synthesizer 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
Free-text loss reasons make it difficult to distinguish isolated deal issues from recurring commercial problems. In practice it shows up as quarterly sales performance review: provides a breakdown of lost annual recurring revenue by root cause, supported by a narrative of recurring loss patterns. Gives leadership three priority fixes to focus the performance discussion. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1–2 weeks of capacity for it.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces closed-lost deals categorised by root cause plus revenue leakage table showing summed lost annual recurring revenue by cause. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. Net effect: 1–2 weeks down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind 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
Win/loss pattern synthesizer 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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