Structure for capturing qualitative + quantitative win/loss insights
View on GitHubgtmagents/gtm-agents
competitive-intelligence
January 22, 2026
Select agents to install to:
npx add-skill https://github.com/gtmagents/gtm-agents/blob/main/plugins/competitive-intelligence/skills/win-loss-dataset/SKILL.md -a claude-code --skill win-loss-datasetInstallation paths:
.claude/skills/win-loss-dataset/# 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. ---