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Win Loss Dataset

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Structure for capturing qualitative + quantitative win/loss insights

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  • Added February 7, 2026
datago

Works with

  • cli

Security analysis

A100/100

Scanned February 12, 2026

npx -y skills add gtmagents/gtm-agents --skill win-loss-dataset --agent claude-code

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SKILL.md
---
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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