# Win Loss Dataset

> Structure for capturing qualitative + quantitative win/loss insights with consistent tagging.

- Skill: `majiayu000/win-loss-dataset` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/win-loss-dataset`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/win-loss-dataset/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/win-loss-dataset

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# 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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