Market Sizing

Quick facts

Phase: Discover | Version: 1.0.0 | Category: strategy | License: Apache-2.0

Try it: /market-sizing "Your context here"

You produce a multi-framework market-sizing meta-analysis covering TAM (Total Addressable Market), SAM (Serviceable Addressable Market), and SOM (Serviceable Obtainable Market). You run all applicable sizing frameworks (top-down, bottom-up, comparable company, analogous market), compare where they converge and diverge, and synthesize a calibrated estimate with a recommendation. Divergence between frameworks is often the most valuable finding. Your job is to produce a defensible artifact and explain the reasoning.

How to Use

Use the /market-sizing slash command:

/market-sizing "Your context here"

Or reference the skill file directly: skills/discover-market-sizing/SKILL.md

Output Template

Market Sizing: [Product / Market]

Executive Summary

[Summary]

Market Definition

Included: [Boundary]
Excluded: [What is deliberately out]
Geography / horizon: [Scope and time frame]

Top-Down Sizing

Layer	Number	Method	Source / Assumption	Confidence
TAM	$[X]	[Industry report]	[Source, page]	[High/Medium/Low]
SAM	$[X]	[Filter on TAM]	[Filter logic]	[Medium]
SOM	$[X]	[Market-share assumption]	[% of SAM in N years]	[Medium/Low]

Bottom-Up Sizing

Segment	# Customers	Revenue / Customer	Sub-total	Method	Source
[Segment A]	[X]	$[Y]	$[X*Y]	Bottom-up	[Source / Assumption]

Multi-Framework Synthesis

Where the frameworks agree: [Convergence and what it implies]
Where they diverge (10x or more): [The gap and why - scope, definition, growth-rate]
Synthesized estimate: [Central estimate with low/high range]
Synthesis confidence: [High/Medium/Low] - [why]

Sensitivity Analysis

Assumption varied	Low	Mid	High
[Market growth rate]	[X%] (TAM = $[X])	[Y%] (TAM = $[Y])	[Z%] (TAM = $[Z])
[Market share captured]	[1%] (SOM = $[A])	[5%] (SOM = $[B])	[10%] (SOM = $[C])

Key Assumptions

Assumption	Source / Rationale	Confidence	What changes if wrong
[Assumption]	[Source]	[High/Medium/Low]	[Sensitivity link]

Confidence and Limitations

Most confident: [Where]
Least confident: [Where]
Would improve confidence: [Specific research]
Not addressed: [Competition, time-to-market, regulatory, etc.]

Next Steps

[Next discovery work if proceeding]
[Conviction threshold needed to justify investment]
[Research that would close the largest remaining unknown]

Example Output

Market Sizing: AI Code-Review SaaS (US, companies with 50+ engineers)

Figures below are illustrative and built on explicitly stated assumptions. For an investment case, replace each assumption with a cited primary source. The point of this example is the multi-framework method, not the specific numbers.

Executive Summary

We size the US market for an AI code-review tool sold per-seat to companies with 50 or more engineers. Two independent frameworks - top-down (developer population x per-seat value) and bottom-up (target company count x annual contract value) - produce TAM estimates that converge within roughly 1.1x ($862M top-down vs. $936M bottom-up), which raises confidence in a central TAM near $900M. SAM (US, 50+ engineer companies able to adopt cloud AI) is roughly $630M, and a 3-year SOM at 4% share is roughly $25M. The single most important assumption is per-seat annual value ($620); the estimate is most sensitive to it. Overall confidence: Medium (one real population anchor, the rest stated assumptions).

Market Definition

Included: AI-assisted code-review tooling sold per developer seat to US-headquartered companies employing 50 or more engineers
Excluded: Free / open-source self-hosted tools, sub-50-engineer companies (different buying motion), non-US markets, fully air-gapped regulated environments that cannot use cloud AI
Geography / horizon: United States; 3-year horizon

Top-Down Sizing

Layer	Number	Method	Source / Assumption	Confidence
TAM	~$862M	Addressable developers x per-seat value	~1.85M US software developers (public US labor statistics; verify current figure) x 75% at 50+ engineer firms = ~1.39M seats x $620/seat/yr	Medium
SAM	~$630M	Filter on TAM	~73% of addressable seats are at firms able to adopt cloud AI code review (excludes air-gapped / regulated)	Medium
SOM	~$25M	Market-share assumption	4% of SAM captured by year 3	Low

Bottom-Up Sizing

Segment	# Customers	Revenue / Customer (ACV)	Sub-total	Method	Source
50-200 engineers	12,000 firms	$18K	$216M	Bottom-up	Firm count and ACV both assumptions
200-1000 engineers	3,500 firms	$90K	$315M	Bottom-up	Assumptions
1000+ engineers	900 firms	$450K	$405M	Bottom-up	Assumptions
Total	16,400 firms	-	~$936M	-	-

Multi-Framework Synthesis

Where the frameworks agree: Top-down (~~$862M) and bottom-up (~~$936M) land within ~1.1x of each other. Independent methods converging this tightly is the strongest confidence signal available without primary market data.
What the convergence depends on: It only holds because the top-down per-seat value ($620) was reconciled against the bottom-up ACVs. The bottom-up ACVs imply an effective per-seat spend of roughly $600-680 once spread across each firm’s developers; the top-down $620 was chosen to match. Had we used a naive $240/seat (a common under-estimate), top-down would be ~$333M - roughly 3x below bottom-up. The per-seat figure is the swing factor, and the divergence it would create is the finding that forces an explicit, defensible number.
Synthesized estimate: TAM ~$900M (central), low $620M / high $1.3B. SAM ~$630M, SOM ~$25M at 4% in 3 years.
Synthesis confidence: Medium. The convergence is reassuring, but only the developer population rests on a real source; per-seat value and firm counts are assumptions.

Sensitivity Analysis

Assumption varied	Low	Mid	High
Per-seat annual value	$240 (TAM = $0.33B)	$620 (TAM = $0.86B)	$900 (TAM = $1.25B)
Year-3 market share (SOM)	1% (SOM = $6M)	4% (SOM = $25M)	8% (SOM = $50M)

Key Assumptions

Assumption	Source / Rationale	Confidence	What changes if wrong
~1.85M US software developers	Public US labor statistics (occupational employment)	Medium	Scales TAM linearly
75% are at 50+ engineer firms	Firmographic assumption	Low	Scales addressable seats
Effective per-seat value ~$620/yr	Reconciled from bottom-up ACVs	Low	Largest swing factor (see sensitivity)
16,400 US firms with 50+ engineers	Derived assumption; not from a firmographic source	Low	Scales bottom-up directly
4% year-3 share	GTM judgment for a new entrant	Low	Scales SOM directly

Confidence and Limitations

Most confident: Developer population order of magnitude (real labor data)
Least confident: Per-seat value and firm counts (both assumptions)
Would improve confidence: A firmographic data pull for the count of US firms by engineering headcount; a pricing study for per-seat willingness to pay
Not addressed: Competitive displacement cost, time-to-market, the build-vs-buy preference of large engineering orgs, international expansion

Next Steps

Buy a firmographic data pull to replace the assumed 16,400 firm count with a sourced number
Run a small pricing study to anchor the per-seat-value assumption that the estimate is most sensitive to
Conviction threshold: a SAM near $630M with a credible path to 4% share generally clears the bar for a seed-stage investment case; confirm the per-seat value before committing

Real-World Examples

See this skill applied to three different product contexts:

Storevine (B2B): Storevine B2B platform - sizing the market for AI inventory forecasting for mid-market e-commerce

Prompt:

/market-sizing

size the market for storevine's AI inventory forecasting. B2B SaaS, sold to
mid-market e-commerce companies (200-2000 employees). ACV around $40k.

i have a rough top-down number from an e-commerce-software market report,
and i can build a bottom-up from company counts. i suspect they won't match -
walk me through reconciling them.

Output:

Market Sizing: Storevine AI Inventory Forecasting (Mid-Market E-Commerce)

Figures are illustrative and built on stated assumptions; replace with cited primary sources for an investment case.

Brainshelf (Consumer): Brainshelf consumer subscription - sizing the US market for AI-curated book recommendations

Prompt:

/market-sizing

size the US market for brainshelf - an AI book-recommendation subscription
for avid readers. consumer subscription, ~$8/month. need it for a seed deck.

i don't have a bought market report. use public consumer book-spending data
as the top-down anchor and build a bottom-up from addressable readers x ARPU.
show me where the two methods agree.

Output:

Market Sizing: Brainshelf AI Book-Curation Subscription (US)

Figures are illustrative and built on stated assumptions; replace with cited primary sources before using in a live raise.

Workbench (Enterprise): Workbench - sizing the external market for developer-experience tooling platforms, quick-estimate mode

Prompt:

/market-sizing

we're thinking about commercializing workbench (our internal dev-experience
platform). size the external market for DevEx tooling platforms. sold per-seat
to engineering orgs. i do NOT have a market report - just give me a defensible
quick estimate with explicit assumptions and wide ranges, and tell me what
would tighten it.

Output:

Market Sizing: Developer-Experience (DevEx) Tooling Platforms

QUICK ESTIMATE. This is built on stated assumptions, not cited sources. Every figure is Low or Medium confidence with wide bands. For an investment decision, replace assumptions with a market report and a firmographic pull. Do not put these numbers in a board deck without that.

Quality Checklist

Before finalizing, verify:

Market definition states an explicit boundary (what is in, what is out)
At least two sizing frameworks were run (top-down + bottom-up where data permits)
Multi-framework synthesis explains convergence and divergence, not just an average
Every dollar figure traces to a cited source, a stated assumption, or a sensitivity range
Confidence labels are source-calibrated, not blanket Low
Sensitivity analysis shows how the estimate moves under key assumptions
TAM is not presented as a revenue projection