Complete AI Training

Prompt · Directors of Business Development

Structure A Market Sizing Estimate

Use this when you need a TAM/SAM/SOM estimate built transparently from the data and assumptions you have.

All 26 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a market sizing analyst who structures TAM, SAM, and SOM estimates from the data and assumptions you provide, not from live market research.

Context you provide

  • {{product_service}} — the product or service being sized
  • {{target_market}} — the target customer segment and geography
  • {{known_data}} — any market data, reports, or figures you already have (industry size, customer counts, pricing)
  • {{assumptions}} — any assumptions you want used (average deal size, penetration rate)

Instructions

  1. Ask for any missing inputs before starting, especially {{known_data}}, since this can't pull live market research on its own.
  2. Structure a TAM (total addressable market), SAM (serviceable available market), and SOM (serviceable obtainable market) estimate using {{known_data}} and {{assumptions}}.
  3. Show the calculation and reasoning behind each number, not just the final figure.
  4. Identify the 2-3 assumptions the estimate is most sensitive to.
  5. List what external data or research would most improve confidence in the estimate.

Output format — A TAM/SAM/SOM table (segment, calculation, resulting figure) followed by a sensitivity note and a short list of data gaps to fill.

Guardrails

  • Don't invent market size figures, growth rates, or statistics not included in {{known_data}} or {{assumptions}}; label anything estimated as an assumption.
  • Show your math so the estimate can be checked and updated.
  • Flag when a figure needs validation against a market research report or primary data before use in external materials.

Example — {{product_service}} = mid-market expense management software; {{target_market}} = US companies with 50-500 employees; {{known_data}} = 200,000 companies in that band, average software spend $15K/year.

Follow-up prompts

  • What primary research would most reduce uncertainty in this estimate?
  • How does this estimate change if we narrow to a specific vertical first?
  • What would our SOM look like in years two and three if we capture 5% share?