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market-scenario-modeler skill

/plugins/market-research/skills/market-scenario-modeler

This skill helps you build TAM/SAM/SOM models and scenario visuals, enabling stress tests and narrative-ready outputs for executives.

npx playbooks add skill gtmagents/gtm-agents --skill market-scenario-modeler

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: market-scenario-modeler
description: Toolkit for building TAM/SAM/SOM models, sensitivity analyses, and narrative-ready
  visuals.
---

# Market Scenario Modeler Skill

## When to Use
- Translating research findings into financial models and strategic scenarios.
- Stress-testing revenue or adoption assumptions for planning cycles.
- Packaging insights for finance, product, and executive stakeholders.

## Framework
1. **Assumption Library** – document data sources, CAGR, penetration, pricing, channel mix.
2. **Scenario Matrix** – base, upside, downside cases with drivers (pricing, win rate, expansion, macro).
3. **Sensitivity Analysis** – tornado charts, Monte Carlo snippets, or sliders for key variables.
4. **Visualization Layer** – waterfall, area, and heat maps tying numbers to narratives.
5. **Decision Hooks** – highlight trigger points, required investments, and guardrails.

## Templates
- Spreadsheet/notebook skeleton with clearly named inputs/outputs.
- Slide templates for TAM/SAM/SOM, share shifts, and investment asks.
- One-pager summary translating scenarios into actions and risks.

## Tips
- Keep assumptions auditable with source links and timestamps.
- Align with finance on currency, inflation, and exchange assumptions before publishing.
- Pair with `run-market-landscape-study` for turnkey modeling assets.

---

Overview

This skill is a toolkit for building TAM/SAM/SOM models, running sensitivity analyses, and producing narrative-ready visuals for GTM planning. It delivers reusable templates, a clear assumption library, and visualization layers that link numbers to decisions. The goal is to turn research and market inputs into audit-ready scenarios that executives, finance, and product teams can act on.

How this skill works

You start by populating an assumption library with sources, CAGRs, pricing, channel mix, and penetration metrics. The skill generates a scenario matrix (base, upside, downside) and runs sensitivity checks using tornado charts or Monte Carlo snippets. It produces ready-to-use spreadsheet/notebook skeletons and slide-ready visuals (waterfall, area, heat maps). Decision hooks surface trigger points, investment needs, and guardrails tied to each scenario.

When to use it

  • Translating market research into financial forecasts for planning cycles
  • Stress-testing revenue, pricing, or adoption assumptions before board reviews
  • Preparing investment asks and go-to-market resource scenarios
  • Creating audit-ready scenario deliverables for finance and product alignment
  • Converting competitive or customer research into actionable TAM/SAM/SOM outputs

Best practices

  • Keep an auditable assumption library with source links and timestamps for every input
  • Align currency, inflation, and exchange-rate rules with finance before modeling
  • Build base, upside, and downside cases with explicit driver changes rather than opaque tweaks
  • Use sensitivity outputs (tornado, Monte Carlo) to prioritize which levers require validation
  • Bundle a one-page narrative that ties numbers to recommended actions and risks

Example use cases

  • A product team converts survey and usage data into SAM/SOM projections to set launch targets
  • Revenue ops runs Monte Carlo on win-rate and pricing sensitivity to size quota risk
  • Marketing tests channel-mix shifts to quantify cost-per-acquisition impact on SOM
  • Finance requests scenario slides and a one-pager to evaluate an incremental investment ask
  • Strategy uses waterfall and heat maps to justify market-entry timing and required budget

FAQ

Can I link this to my existing spreadsheets or BI tools?

Yes. Templates are designed as spreadsheet/notebook skeletons that export clean inputs/outputs for import into BI tools or existing financial models.

Does it support probabilistic sensitivity like Monte Carlo?

Yes. The skill includes Monte Carlo snippets and deterministic tornado analyses to handle probabilistic and one-way sensitivity testing.