Reviewing the revenue forecast
Provides a forecast-adjusted view of the open pipeline grounded in previous deal outcomes. Highlights where deal-level evidence supports or challenges forecast expectations.
— Sales & Growth
Deal win-likelihood scorer
Learns from your closed deals to score every open opportunity out of 100, names the factors moving each score, flags deals priced or sized well outside the norm, and returns a forecast-adjusted pipeline view.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~15 minutes
For a full population, not a sample
No coding required
- Input
- CRM opportunity records covering historical closed deals and the open pipeline
- Output
- Win-likelihood scores from 0–100 for each open opportunity
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Deal win-likelihood scorer for
Prioritising sales attention
Gives each open opportunity a 0–100 win-likelihood score and explains the factors influencing it. Supports a more focused discussion of which deals merit attention.
Scrutinising unusual deal terms
Flags opportunities with pricing, discounts or deal sizes well outside historical norms. Makes exceptions visible for commercial review without treating every unusual deal as unlikely to close.
Preparing leadership pipeline reviews
Provides a concise pipeline brief that combines deal assessments, forecast implications and commercial exceptions. Gives leadership a shared evidence base for discussing revenue risk.
What it works from
- CRM opportunity records covering historical closed deals and the open pipeline
- Won or lost outcomes for historical opportunities
- Deal names, accounts, owners and sales stages
- Deal amounts, discount percentages and product details where available
What you get back
- Win-likelihood scores from 0–100 for each open opportunity
- Deal-level explanations of the factors influencing scores
- Flags for unusual pricing, discounts and deal sizes
- Forecast-adjusted pipeline brief
— HOW IT BEHAVES
How Deal win-likelihood scorer 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.
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.
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
Sales pipelines often mix credible opportunities with deals whose value, discount or progress makes the forecast harder to trust. In practice it shows up as reviewing the revenue forecast: provides a forecast-adjusted view of the open pipeline grounded in previous deal outcomes. Highlights where deal-level evidence supports or challenges forecast expectations. 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
Skillsize turns that work into a Skill: you supply the material, and what comes back is win-likelihood scores from 0–100 for each open opportunity, with deal-level explanations of the factors influencing scores. 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. In effect, 1–2 weeks of senior time compresses into ~15 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
Deal win-likelihood scorer 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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