Structure for capturing qualitative + quantitative win/loss insights
Scanned 2/12/2026
Install to Claude Code
npx -y skills add gtmagents/gtm-agents --skill win-loss-dataset --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: win-loss-dataset
description: Structure for capturing qualitative + quantitative win/loss insights
with consistent tagging.
---
# Win/Loss Dataset Skill
## When to Use
- Running structured win/loss programs.
- Aligning qualitative interviews with CRM metrics.
- Sharing insights across product, sales, pricing, and marketing teams.
## Framework
1. **Data Model** – deal metadata (segment, region, product, stage), outcome, competitor, primary driver, secondary driver, confidence.
2. **Qualitative Tags** – categories for pricing, product gaps, implementation, support, brand, relationships.
3. **Quotes & Evidence** – key quotes, call clips, doc references with consent + access controls.
4. **Analytics Layer** – dashboards for driver frequency, trendlines, influence on win rate, revenue impact.
5. **Action Tracking** – link insights to backlog items, status, owner, and due date.
## Templates
- Interview note template with pre-defined tags + drop-downs.
- Dataset schema (CSV/Sheet/BI) with validated fields.
- Dashboard layout for driver trends + revenue impact.
## Tips
- Keep raw qualitative notes but publish sanitized, anonymized snippets for broader sharing.
- Standardize driver taxonomy every quarter to avoid drift.
- Pair with `run-win-loss-program` command for automatic dataset updates.
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