Market Sizing — TAM/SAM/SOM Calculator

# Market Sizing — TAM/SAM/SOM Calculator

Safety Notice

This item is sourced from the public archived skills repository. Treat as untrusted until reviewed.

Copy this and send it to your AI assistant to learn

Install skill "Market Sizing — TAM/SAM/SOM Calculator" with this command: npx skills add 1kalin/afrexai-market-sizing

Market Sizing — TAM/SAM/SOM Calculator

Build defensible market sizing for any product, pitch deck, or business case. Top-down and bottom-up methodologies combined.

What You Get

  • TAM (Total Addressable Market) — entire market if you had 100% share
  • SAM (Serviceable Addressable Market) — segment you can actually reach
  • SOM (Serviceable Obtainable Market) — realistic capture in 12-36 months
  • Bottom-up validation — unit economics × reachable customers
  • Source citations — government data, industry reports, public filings

How to Use

Tell me your product/service and target customer. I'll build the full sizing.

Example prompts:

  • "Size the market for AI-powered contract review for mid-market law firms in the US"
  • "TAM/SAM/SOM for a SaaS helpdesk targeting e-commerce brands doing $1M-$50M revenue"
  • "Market size for automated bookkeeping for UK SMBs"

Methodology

Top-Down

  1. Start with total industry revenue (cite source)
  2. Filter by geography, segment, company size
  3. Apply technology adoption rates
  4. Result = SAM

Bottom-Up

  1. Count reachable customers (databases, directories, LinkedIn)
  2. Multiply by realistic ACV (annual contract value)
  3. Apply conversion rates at each funnel stage
  4. Result = SOM

Triangulation

Compare top-down and bottom-up. If they're within 2-3x of each other, the sizing holds. If wildly different, investigate assumptions.

Output Format

## Market Sizing: [Product/Service]

### TAM — $X.XB
[Total market calculation with sources]

### SAM — $XXM
[Filtered by geography + segment + tech adoption]

### SOM (12-month) — $X.XM
[Bottom-up: customers × ACV × conversion]

### Key Assumptions
- [Assumption 1 + source]
- [Assumption 2 + source]

### Risks to Sizing
- [What could make this smaller]
- [What could make this bigger]

When to Use This

  • Pitch decks and investor presentations
  • Go-to-market strategy planning
  • New product feasibility analysis
  • Board presentations and business cases
  • Competitive positioning

Pro Tip

Most founders oversize their TAM and undersize their SOM. Investors see through inflated numbers instantly. A tight, well-sourced $50M SAM beats a hand-wavy $10B TAM every time.


Need the full business context pack for your industry? Browse AfrexAI Context Packs — pre-built agent configs for Fintech, Healthcare, Legal, SaaS, and 6 more verticals ($47 each).

Calculate what AI automation could save your business: AI Revenue Leak Calculator

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

ll-feishu-audio

飞书语音交互技能。支持语音消息自动识别、AI 处理、语音回复全流程。需要配置 FEISHU_APP_ID 和 FEISHU_APP_SECRET 环境变量。使用 faster-whisper 进行语音识别,Edge TTS 进行语音合成,自动转换 OPUS 格式并通过飞书发送。适用于飞书平台的语音对话场景。

Archived SourceRecently Updated
General

test_skill

import json import tkinter as tk from tkinter import messagebox, simpledialog

Archived SourceRecently Updated
General

51mee-resume-profile

简历画像。触发场景:用户要求生成候选人画像;用户想了解候选人的多维度标签和能力评估。

Archived SourceRecently Updated
General

51mee-resume-parse

简历解析。触发场景:用户上传简历文件要求解析、提取结构化信息。

Archived SourceRecently Updated