Complete AI Training

Prompt · Chief Sales Officers (CSOs)

Estimate Total Addressable Market

Use this when you need a structured estimate of market size and potential share for a product or category.

All 11 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 builds transparent, methodology-driven TAM estimates rather than pulling numbers from thin air.

Context you provide

  • {{product_or_category}} — the product or product category being sized
  • {{target_region}} — the geographic market in scope
  • {{available_data}} — any data you already have (industry reports, customer counts, pricing, competitor revenue)
  • {{estimation_goal}} — what the estimate will be used for (e.g., investor pitch, sales quota planning, business case)

Instructions

  1. Ask for the product, region, and available data if not provided.
  2. Choose and explain an appropriate methodology (top-down from industry data, bottom-up from unit economics, or a blend) based on what data is available.
  3. Walk through the estimate step by step, showing the assumptions and data sources used at each step.
  4. Provide a range rather than a single number, and explain what drives the low and high ends.
  5. List the key assumptions clearly so they can be challenged or updated later.

Output format — A short methodology explanation, then a step-by-step calculation with assumptions labeled, ending in a TAM range. Close with a bulleted list of the assumptions most worth validating.

Guardrails

  • Do not present invented statistics as verified data; label every figure as an assumption, an estimate, or sourced data only if the user provided a source.
  • Show the calculation logic, not just a final number.
  • Flag where real market research or a paid data source would materially improve accuracy.

Example — {{product_or_category}} = mid-market expense management software; {{target_region}} = North America; {{available_data}} = number of mid-market companies and average software spend from a public report.

Follow-up prompts

  • What would change this estimate most if our pricing assumption were wrong?
  • How should we present this range to investors without overstating confidence?
  • What real data source would most improve this estimate?