정보
이 스킬은 사용자가 시장 규모를 측정하기 위해 TAM, SAM, SOM을 계산하는 과정을 대화형으로 안내하며, 적응형 질문을 통해 방어 가능한 추정치를 구축합니다. 제품 관리자가 비즈니스 케이스를 작성하거나 투자자 프레젠테이션을 준비할 때 활용하도록 설계되었으며, 명시적 가정과 실제 데이터 인용을 강조합니다. 개발자는 이 구조화된 대화형 계산기를 제품 검증 및 자금 요청 워크플로우에 통합할 수 있습니다.
빠른 설치
Claude Code
추천npx skills add deanpeters/Product-Manager-Skills -a claude-code/plugin add https://github.com/deanpeters/Product-Manager-Skillsgit clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/tam-sam-som-calculatorClaude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요
문서
Purpose
Guide product managers through calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for a product idea by asking adaptive, contextually relevant questions. Use this to build defensible market size estimates backed by real-world citations, economic projections, and population data—essential for pitching to investors, securing budget, or validating product-market fit.
This is not a back-of-napkin guess—it's a structured, citation-backed analysis that withstands scrutiny.
Key Concepts
TAM/SAM/SOM Framework
The three-tier market sizing model:
Total Addressable Market (TAM):
- The total market demand for a product or service
- "If we captured 100% of the market, what's the revenue?"
- Broadest possible market (no constraints)
Serviceable Available Market (SAM):
- The segment of TAM your company can realistically target
- Narrowed by geography, firmographics, demographics, or product constraints
- "Who can we actually reach with our product?"
Serviceable Obtainable Market (SOM):
- The portion of SAM you can realistically capture
- Accounts for competition, market constraints, go-to-market capacity
- "What can we capture in the next 1-3 years?"
Why This Works
- Top-down validation: TAM → SAM → SOM ensures estimates are grounded in reality
- Investor-friendly: Standard framework VCs and execs understand
- Citation-backed: Real data sources (Census, Statista, World Bank) add credibility
- Adaptive: Questions adjust based on context (B2B vs. B2C, US vs. global, etc.)
Anti-Patterns (What This Is NOT)
- Not a single-number guess: "The market is $10B" without supporting data
- Not static: Markets evolve—reassess annually
- Not a substitute for customer validation: Market size ≠ product-market fit
When to Use This
- Pitching to investors or execs (need market size in deck)
- Validating product ideas (is the market big enough?)
- Prioritizing product lines (which has bigger opportunity?)
- Setting growth targets (what's realistic to capture?)
When NOT to Use This
- For internal tools with captive users (no external market)
- Before defining the problem (market sizing requires clear problem space)
- As the only validation (pair with customer research)
Facilitation Source of Truth
Use workshop-facilitation as the default interaction protocol for this skill.
It defines:
- session heads-up + entry mode (Guided, Context dump, Best guess)
- one-question turns with plain-language prompts
- progress labels (for example, Context Qx/8 and Scoring Qx/5)
- interruption handling and pause/resume behavior
- numbered recommendations at decision points
- quick-select numbered response options for regular questions (include
Other (specify)when useful)
This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.
Application
Use template.md for the full fill-in structure.
This interactive skill asks up to 4 adaptive questions, offering enumerated context-aware options at each step. The agent adapts questions based on previous responses.
Step 0: Gather Context (Before Questions)
Agent suggests:
Before we begin, it's helpful to have product context. If available, please share:
For Your Own Product:
- Website copy (homepage, product pages, value prop statements)
- Marketing emails or landing pages
- Product descriptions or positioning statements
- Case studies or customer testimonials
- Sales deck or pitch materials
If You Don't Have a Product Yet:
- Find a similar or adjacent product (competitor or analog)
- Copy their website homepage, product description, or landing page
- We'll use this as a reference point for market sizing
You can paste this content directly, or we can proceed with a brief description.
Why this helps:
- Marketing materials already contain target audience, pain points, and value props
- Analyzing real content (yours or competitors') grounds the analysis in reality
- You can benchmark against similar products' market positioning
Optional Helper Script (Deterministic Math)
If you already have population and ARPU numbers (or a TAM estimate), you can run a deterministic helper to compute TAM/SAM/SOM and generate a Markdown table. This script does not fetch data or write files.
python3 scripts/market-sizing.py --population 5400000 --arpu 1000 --sam-share 30% --som-share 10%
Question 1: Problem Space
Agent asks: "Based on the context you've provided (or will describe), what problem space are you exploring for market sizing?"
Offer 4 enumerated examples (user can select by number or write custom):
- B2B SaaS productivity — E.g., "Workflow automation for small business operations" (like Zapier, Integromat)
- Consumer fintech — E.g., "Personal budgeting app for Gen Z users" (like Mint, YNAB)
- Healthcare/telehealth — E.g., "Mental health support for remote workers" (like BetterHelp, Talkspace)
- E-commerce enablement — E.g., "Payment processing for online sellers" (like Stripe, Square)
Or write your own problem space description based on the marketing materials you shared.
Tip: If you provided website copy or marketing materials, the agent can extract the problem space from phrases like:
- "We help [target] solve [problem]"
- "The #1 solution for [use case]"
- Customer pain points in testimonials or case studies
User response: [Selection or custom description]
Question 2: Geographic Region
Agent asks: "What geographic region are you targeting?"
Offer 4 enumerated options (adapted based on problem space):
- United States — Best for detailed Census Bureau data, BLS stats, robust industry reports
- European Union — Use Eurostat, local statistical agencies; note GDPR/compliance considerations
- Global — World Bank, IMF data; broader but less granular
- Specific country/region — E.g., "Canada," "Southeast Asia," "Latin America"
Or specify your own region.
User response: [Selection or custom]
Adaptation logic:
- If user selected B2B SaaS (Question 1, Option 1) → Emphasize US/EU markets (mature SaaS adoption)
- If user selected Consumer fintech (Question 1, Option 2) → Mention emerging markets (higher mobile adoption)
Question 3: Industry/Market Segments
Agent asks: "What specific industry or market segments does this problem space relate to?"
Offer 4 enumerated options (adapted based on problem space + geography):
Example (if Question 1 = B2B SaaS, Question 2 = US):
- SMB services sector — 5.4M businesses, $1.2T revenue (US Census, 2023)
- Professional services (legal, accounting) — 1.1M firms, $850B revenue (IBISWorld, 2023)
- Healthcare providers — 900K practices, $4T industry (BLS, 2023)
- Tech/software companies — 500K firms, $1.8T revenue (Statista, 2023)
Or describe your own industry segment.
User response: [Selection or custom]
Adaptation logic:
- If Question 1 = Consumer fintech, offer consumer segments (e.g., "Gen Z 18-25," "Millennials 25-40")
- If Question 1 = Healthcare, offer segments (e.g., "Primary care physicians," "Therapists/counselors")
Question 4: Potential Customers (Demographics/Firmographics)
Agent asks: "Who are the potential customers affected by this problem?"
Offer 4 enumerated options (adapted based on previous answers):
Example (if Question 1 = B2B SaaS, Question 3 = SMB services sector):
- SMBs with 10-50 employees — 1.2M businesses, $400B revenue (Census Bureau, 2023)
- SMBs with 50-250 employees — 600K businesses, $800B revenue (Census Bureau, 2023)
- Solo entrepreneurs/freelancers — 3.5M self-employed, $200B revenue (BLS, 2023)
- Service businesses with online presence — 2M businesses, $600B e-commerce (Statista, 2023)
Or describe your own customer segment (firmographics, demographics, income, etc.).
User response: [Selection or custom]
Output: Generate TAM/SAM/SOM Analysis
After collecting responses, the agent generates a structured analysis:
# TAM/SAM/SOM Analysis
**Problem Space:** [User's input from Question 1]
**Geographic Region:** [User's input from Question 2]
**Industry/Market Segments:** [User's input from Question 3]
**Potential Customers:** [User's input from Question 4]
---
## Total Addressable Market (TAM)
**Definition:** The total market demand if you captured 100% of potential customers in the problem space.
**Population Estimate:** [Calculated from data sources]
- **Source:** [Citation, e.g., "US Census Bureau, 2023"]
- **Calculation:** [Show math, e.g., "5.4M SMBs × $1.2T revenue = $1.2T TAM"]
**Market Size Estimate:** $[X] billion/million
- **Source:** [Industry report citation]
- **URL:** [Clickable link to source]
---
## Serviceable Available Market (SAM)
**Definition:** The segment of TAM you can realistically target with your product (narrowed by geography, firmographics, product fit).
**Segment of TAM:** [User's narrowed segment from Question 4]
**Population Estimate:** [Calculated]
- **Source:** [Citation]
- **Calculation:** [Show math, e.g., "1.2M SMBs with 10-50 employees"]
**Market Size Estimate:** $[X] billion/million
- **Source:** [Citation]
- **URL:** [Link]
**Assumptions:**
- [List key assumptions, e.g., "Assumes 50% of SMBs have budget for automation tools"]
---
## Serviceable Obtainable Market (SOM)
**Definition:** The portion of SAM you can realistically capture in the next 1-3 years, accounting for competition and market constraints.
**Realistically Capturable Market:** [Agent's estimation based on market maturity, competition]
**Population Estimate:** [Calculated]
- **Source:** [Citation]
- **Calculation:** [Show math, e.g., "1.2M SMBs × 5% market share (Year 1) = 60K customers"]
**Market Size Estimate:** $[X] million
- **Assumptions:**
- [Competition assumption, e.g., "5 major competitors, market leader has 15% share"]
- [GTM assumption, e.g., "Sales capacity: 50 customers/month in Year 1"]
- [Conversion assumption, e.g., "10% trial-to-paid conversion"]
**Year 1-3 Projections:**
- **Year 1:** [X]K customers, $[X]M revenue (5% of SAM)
- **Year 2:** [X]K customers, $[X]M revenue (10% of SAM)
- **Year 3:** [X]K customers, $[X]M revenue (15% of SAM)
---
## Data Sources & Citations
- [Source 1: e.g., "US Census Bureau (2023). County Business Patterns. URL: census.gov"]
- [Source 2: e.g., "IBISWorld (2023). Professional Services Industry Report. URL: ibisworld.com"]
- [Source 3: e.g., "Statista (2023). SMB Software Market Size. URL: statista.com"]
- [Add all sources used]
---
## Validation Questions
1. **Does TAM align with industry reports?** [Compare to 3rd-party market research]
2. **Is SAM realistically serviceable?** [Can your GTM motion reach this segment?]
3. **Is SOM achievable given competition?** [Is 5-15% market share realistic in 3 years?]
---
## Next Steps
1. **Validate with customer interviews:** Does the problem resonate with target segment?
2. **Benchmark against competitors:** What market share do incumbents have?
3. **Refine SOM based on GTM capacity:** Can sales/marketing support this growth?
4. **Update annually:** Markets shift—reassess TAM/SAM/SOM yearly
---
**Would you like to refine any assumptions or explore a different segment?**
Examples
See examples/sample.md for a full TAM/SAM/SOM analysis example.
Mini example excerpt:
**TAM:** 5.4M SMBs × $2,000 ARPA = $10.8B
**SAM:** 1.2M SMBs × $2,000 ARPA = $2.4B
**SOM:** 5% of SAM = $120M
Common Pitfalls
Pitfall 1: TAM Without Citations
Symptom: "The market is $50B" (no source)
Consequence: Can't defend the number to investors or execs.
Fix: Cite industry reports (Gartner, IBISWorld, Statista) with URLs.
Pitfall 2: SOM Equals SAM
Symptom: "SAM is $5B, SOM is $5B" (assuming 100% capture)
Consequence: Unrealistic projection—no market has zero competition.
Fix: SOM should be 1-20% of SAM in Year 1-3, accounting for competition.
Pitfall 3: No Population Estimates
Symptom: Only dollar amounts, no customer counts
Consequence: Can't build sales/marketing plans without knowing customer volume.
Fix: Always include population (e.g., "1.2M businesses" or "60K customers in Year 1").
Pitfall 4: Static Assumptions
Symptom: TAM/SAM/SOM calculated once, never updated
Consequence: Stale data as markets shift.
Fix: Reassess annually. Markets grow/shrink, competition changes, new data emerges.
Pitfall 5: Ignoring GTM Constraints
Symptom: "SOM is 50% of SAM in Year 1" (but no sales team)
Consequence: SOM isn't realistic given GTM capacity.
Fix: Ground SOM in GTM constraints (sales capacity, marketing budget, conversion rates).
References
Related Skills
skills/positioning-statement/SKILL.md— TAM/SAM/SOM informs "For [target]" segment sizeskills/problem-statement/SKILL.md— Problem space defines the marketskills/recommendation-canvas/SKILL.md— Market sizing informs business outcome projections
Optional Helpers
skills/tam-sam-som-calculator/scripts/market-sizing.py— Deterministic TAM/SAM/SOM calculator (no network access)
External Frameworks
- Steve Blank, The Four Steps to the Epiphany (2005) — Market sizing for startups
- Lean Startup methodology — Validate market size with experiments, not just desk research
Data Sources (For Citations)
- US: US Census Bureau, Bureau of Labor Statistics, IBISWorld, Statista
- Europe: Eurostat, local statistical agencies
- Global: World Bank, IMF, Gartner, Forrester
Dean's Work
- TAM/SAM/SOM Prompt Generator (multi-turn adaptive market sizing)
Skill type: Interactive
Suggested filename: tam-sam-som-calculator.md
Suggested placement: /skills/interactive/
Dependencies: None (standalone interactive skill)
GitHub 저장소
Frequently asked questions
What is the tam-sam-som-calculator skill?
tam-sam-som-calculator is a Claude Skill by deanpeters. Skills package instructions and resources that Claude loads on demand, so Claude can perform tam-sam-som-calculator-related tasks without extra prompting.
How do I install tam-sam-som-calculator?
Use the install commands on this page: add tam-sam-som-calculator to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does tam-sam-som-calculator belong to?
tam-sam-som-calculator is in the Other category, tagged general.
Is tam-sam-som-calculator free to use?
Yes. tam-sam-som-calculator is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
연관 스킬
LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.
이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.
이 Claude Skill은 스프레드, 오버/언더, 프로프 베트를 포함한 스포츠 베팅 시장을 분석합니다. 역사적 추이와 상황별 통계를 검토하여 가치 베트를 발견하고, 교육적 목적으로 실행 가능한 권장 사항이 담긴 구조화된 마크다운 결과를 제공합니다. 개발자는 이 기능을 스포츠 베팅 분석 도구에 활용할 수 있으며, 단순히 엔터테인먼트/교육 목적으로만 설계되었음을 유의해야 합니다.
이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.
