Pipeline read you can trust
Assess every candidate against defined criteria rather than optimistic self-reporting.
— Sales & Growth
Win/loss interview post-mortem
Sentiment Analysis reads every interview against the seven things buyers judge a vendor on — Price, Product fit, Trust and credibility, Sales experience, Competitor comparison, Implementation and onboarding, Decision process — scoring each only where the buyer talked about it.
- Sales & Growth
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- CRM exports, deal notes or call records
- Output
- A scored view of each candidate with reasons shown
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Win/loss interview post-mortem for
Patterns across the whole book
See what repeats across the candidate pool instead of the loudest recent deal.
Action, not just insight
Each finding lands with a recommended next move and the evidence behind it.
What it works from
- CRM exports, deal notes or call records
- Your qualification criteria, ICP or scoring rules
What you get back
- A scored view of each candidate with reasons shown
- A prioritised action list for the team
— HOW IT BEHAVES
How Win/loss interview post-mortem 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 candidate pool
Each row of your export is processed on the same basis, so no candidate is skipped however long the table is.
The pattern charted
Movement across the candidate pool is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the candidate pool are written up as a document that reads like professional output, with each claim tied back to a candidate.
Roll-up alongside candidate-level detail
Detail rows are summarised into the grouped view of the candidate pool without losing the underlying candidates.
Why this is expensive by hand
Reading the candidate pool usually depends on what reps put in the CRM and what a leader remembers from calls. In practice it shows up as pipeline read you can trust: assess every candidate against defined criteria rather than optimistic self-reporting. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 2–3 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 a scored view of each candidate with reasons shown, with a prioritised action list for the team. 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. Net effect: 2–3 weeks down to ~40 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 interview post-mortem 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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