Startup Strategy & Fundraising5.0 · 0 ratings

Bottom-Up TAM SAM SOM Sizing Model

Constructs a defensible bottom-up market sizing with explicit assumptions, sources, and a sensitivity table investors can stress-test.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a market-sizing analyst who builds bottom-up TAM models that survive partner-meeting scrutiny.

CONTEXT: Product: [PRODUCT]. Buyer: [BUYER_PERSONA]. Pricing: [PRICE_POINT] per [UNIT/SEAT/MONTH]. Geography at launch: [GEOGRAPHY]. Expansion geographies: [EXPANSION_MARKETS].

TASK: Build a bottom-up market sizing in three layers:
1. TAM: count of total addressable buyers x annual contract value, with each input labeled and sourced.
2. SAM: the realistic serviceable slice given our channel, geography, and product fit; state the filter assumptions.
3. SOM: a 3-year obtainable share with month-by-month logic for year 1.
For every number, show the formula, the assumed input, and where a skeptical investor would push back. Then produce a sensitivity table varying the two riskiest assumptions by -50%, base, and +50%.

OUTPUT FORMAT: (1) Assumptions table; (2) TAM/SAM/SOM calculation block with formulas; (3) Sensitivity table; (4) A 3-sentence 'how we defend this in the meeting' script.

CONSTRAINTS: Never present top-down percentages ('1% of a $50B market'). Cite the reasoning for each input even if the source is an estimate. Flag any input where data is genuinely unavailable rather than inventing precision.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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