Free TAM / SAM / SOM Sizing Prompt

Short answer: Size the market with explicit definitions, assumptions, and a reachable wedge.

Built by StackSwap · Updated August 28, 2026

OUTPUTMarket-sizing model with assumptions and sensitivity notes.
REPLACESUnqualified top-down market slides.
RUNS INChatGPT · Claude · Codex

BEFORE YOU COPY

Bring the context. Skip the blank page.

Collect customer or prospect examples, observed problem and urgency signals, firmographic or behavioral constraints, disqualifiers, economics, and how the team will act on the segment. The prompt separates facts from assumptions, compares viable paths, and produces the promised artifact instead of generic advice.

  1. Add contextProvide your company, buyer, motion, constraints, and decision.
  2. Run the workflowPaste the free prompt into ChatGPT, Claude, or Codex.
  3. Inspect the artifactReview assumptions, risks, actions, and the quality check.

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## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: tam-sam-som-sizing
description: "Size a B2B SaaS market defensibly, TAM, SAM, and SOM built bottom-up from real units and sanity-checked top-down, with segmentation, GTM-capacity-grounded SOM, defensible assumptions, and a range instead of a single fantasy number. MANDATORY TRIGGERS: 'TAM', 'SAM', 'SOM', 'market sizing', 'how big is the market', 'size my market', 'TAM analysis', 'addressable market', 'bottoms-up TAM'. STRONG TRIGGERS: 'market opportunity', 'is the market big enough', 'investor TAM slide', 'total addressable market', 'serviceable market', 'how much can we realistically capture'. Do NOT trigger on: defining the ideal customer profile qualitatively (use icp-builder), setting the sales number/quota (use quota-and-capacity-planning), or choosing PLG vs sales-led (use gtm-motion-selection). DO trigger when an operator needs to size a market with a number they can defend."
allowed-tools: Read Write WebSearch WebFetch
metadata:
  author: Nick French / StackSwap
  version: '1.0'
  product: Operator Playbook
  website: stackswap.ai/playbook
---

# TAM / SAM / SOM Sizing

Founders size markets two broken ways. The first: pull a "$50B TAM" off a Gartner slide and claim 1% of it, a top-down fantasy every investor has seen a hundred times and discounts to zero. The second: have no number at all, so the board can't plan and the GTM has no denominator. A market size that's worth anything is built from the bottom up, real companies times real ACV, and sanity-checked from the top, with the assumptions on the table and a floor you can defend.

> **A market size is only as good as the assumptions you can defend. Top-down off an analyst slide is a fantasy; bottom-up from real units is a plan. Build it from the bottom, sanity-check from the top, and always show the floor.**

This skill builds the defensible number: TAM/SAM/SOM from a bottom-up model, a top-down reality check, segmentation, a SOM grounded in actual GTM capacity, and a range that survives scrutiny.

---

## When to use this skill

Trigger on:

- "Size my market / how big is the TAM?"
- "Build a TAM/SAM/SOM"
- "Is the market big enough?"
- "Investor market-sizing slide"

Don't run for:

- Defining the ICP qualitatively (use `icp-builder`)
- Setting the sales number (use `quota-and-capacity-planning`)
- Choosing the GTM motion (use `gtm-motion-selection`)

---

## The framework

### 1. The three layers, defined

- **TAM (Total Addressable Market):** total revenue if every entity that *could* buy, did. The theoretical ceiling.
- **SAM (Serviceable Addressable Market):** the slice you can actually serve, your real segment, geography, and product fit.
- **SOM (Serviceable Obtainable Market):** what you can realistically *win* in the next N years given your GTM capacity and competition.

Confusing these is the most common error, TAM is for context, SOM is for planning.

### 2. Bottom-up sizing (the credible method)

The number investors and boards trust: **# of real target accounts × ACV.** Count actual companies that match your ICP (from databases, industry counts, firmographic filters), multiply by a realistic ACV. "There are ~14,000 US SaaS companies with 50-500 employees × $12K ACV = $168M SAM" is defensible. "1% of a $50B market" is not, it's a guess dressed as math.

### 3. Top-down sizing (the sanity check, not the answer)

Analyst reports and adjacent-market proxies have a use: to **bound** the bottom-up, not replace it. If your bottom-up SAM is $168M and the analyst TAM for the category is $40B, fine, you're a slice. If your bottom-up says $168M and top-down says $5M, an assumption is broken. Top-down is the cross-check, never the headline.

### 4. The reality check

Do bottom-up and top-down agree to an order of magnitude? They should be in the same ballpark, derived independently. If they're 100x apart, find the bad assumption before you present anything. Two methods converging is what makes a number believable.

### 5. Segmentation

Size by segment, not as one blob, different segments have different account counts, ACVs, and penetration. This reveals where the money actually is and where to *start* (the beachhead). A single TAM number hides the strategy; a segmented one contains it.

### 6. SOM realism (capacity, not hope)

SOM is where fantasy dies. Your obtainable market is bounded by your actual GTM capacity (reps, marketing reach, sales cycle), the competition, and time. Be honest: a bootstrapped team and a funded blitz have very different SOMs from the same SAM. Ground SOM in what your motion can actually execute (ties to `quota-and-capacity-planning` and `gtm-motion-selection`), not a tidy "10% of SAM."

### 7. The defensible presentation

Show the work: every assumption labeled and sourced, the bottom-up math visible, and a **range** rather than a single number (a credible $140-180M beats a precise-looking $168.3M). Name the floor. The same de-precision discipline that makes any operator number trustworthy.

---

## The process when triggered

### Step 1: Define the three layers for this business
TAM/SAM/SOM scoped to the real segment and product (§1).

### Step 2: Build bottom-up
Target account count × ACV, by segment (§2, §5).

### Step 3: Top-down sanity check
Analyst/proxy bound; reconcile to order of magnitude (§3-4).

### Step 4: Ground the SOM
Capacity, competition, time, honest, not 10%-of-SAM (§6).

### Step 5: Present defensibly
Assumptions sourced, ranged, floor named (§7).

---

## The artifact (template)

```markdown
# Market Sizing, [Company], [Date]

## Definitions
- TAM: [scope] | SAM: [scope] | SOM: [scope + horizon]

## Bottom-up (by segment)
| Segment | # target accounts | ACV | Size | Source |
| --- | --- | --- | --- | --- |
| ... | ... | $... | $... | [DB/industry data] |
| **SAM (sum)** | | | **$...** | |

## Top-down sanity check
- Analyst/proxy TAM: $... → bottom-up is a [__%] slice → reconciles? Y/N

## SOM (obtainable, [N]-year)
- Bounded by: [GTM capacity / competition / cycle]
- SOM: $... ( ___% of SAM, justified by capacity not assumption)

## Assumptions
- [Each labeled + sourced; conservative]

## The number
- SAM: **$[low]–$[high]** | SOM: **$[low]–$[high]** (confidence: ___)
```

---

## Common mistakes

- **Top-down fantasy.** "1% of $50B" is a guess, not a model. Build bottom-up.
- **No bottom-up.** Without real account-count × ACV, the number isn't defensible.
- **Single point estimate.** A precise number invites a precise rebuttal. Range it, floor it.
- **SOM = 10% of SAM.** Ground it in actual GTM capacity, not a round fraction.
- **No segmentation.** One blob hides where the money and the beachhead are.
- **Confusing TAM with SOM.** TAM is context; SOM is the plan. Don't pitch TAM as obtainable.
- **Unsourced assumptions.** Every input needs a source or it's fiction.

---

## How to use the artifact downstream

1. **Sized from the ICP**, `icp-builder` defines the segments this model counts.
2. **Bounds the plan**, SOM informs the target in `quota-and-capacity-planning`.
3. **Shapes the motion**, market size + structure feed `gtm-motion-selection`.
4. **Frames positioning**, the segment and market frame feed `positioning-and-messaging`.

---

**A market size is an argument, and it's only as strong as the assumptions you'll defend in the room. Build it bottom-up from real accounts and ACV, cross-check top-down, segment it, ground the SOM in capacity, and present a sourced range with a floor. Founders who pitch "1% of a huge number" get discounted to zero. Operators who build a defensible model get believed.**

---

_Part of the StackSwap Operator Playbook. → stackswap.ai/playbook_

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QUESTIONS

About this free prompt

What does this tam / sam / som sizing prompt help with?

Size the market with explicit definitions, assumptions, and a reachable wedge.

Who should use this tam / sam / som sizing prompt?

This free GTM prompt is for B2B SaaS founders, GTM leaders, and RevOps operators who need a useful first draft without starting from a blank page.

What should I add before running this tam / sam / som sizing prompt?

Add your company, buyer, GTM motion, constraints, and the decision you need to make. Better context produces a more specific artifact and makes weak assumptions easier to spot.

What output does this tam / sam / som sizing prompt produce?

Market-sizing model with assumptions and sensitivity notes. The workflow is designed to produce that artifact instead of generic GTM advice.

Can I use this tam / sam / som sizing prompt in ChatGPT, Claude, or Codex?

Yes. The workflow is designed for ordinary chat, Claude, and Codex, with platform-specific formats available to copy for free.

How do I get a better result from this tam / sam / som sizing prompt?

Include real customer language, current numbers, and hard constraints, then inspect the assumptions and risks in the result. Treat the first output as a decision artifact to improve, not an unquestionable answer.

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