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win-loss-dataset

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Aktualisiert19. November 2025 um 14:32

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

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gtmagents/gtm-agents

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

Marktforschungsanalysten und MarketingspezialistenWirtschafts- und Finanzberufe·SOC 13-1161

SKILL.md

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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

Data Model – deal metadata (segment, region, product, stage), outcome, competitor, primary driver, secondary driver, confidence.
Qualitative Tags – categories for pricing, product gaps, implementation, support, brand, relationships.
Quotes & Evidence – key quotes, call clips, doc references with consent + access controls.
Analytics Layer – dashboards for driver frequency, trendlines, influence on win rate, revenue impact.
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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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

Data Model – deal metadata (segment, region, product, stage), outcome, competitor, primary driver, secondary driver, confidence.
Qualitative Tags – categories for pricing, product gaps, implementation, support, brand, relationships.
Quotes & Evidence – key quotes, call clips, doc references with consent + access controls.
Analytics Layer – dashboards for driver frequency, trendlines, influence on win rate, revenue impact.
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.