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market-sizing市场规模

Agent Skill

market-sizing 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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请帮我安装这个 Agent Skill:market-sizing(市场规模)
来源仓库:https://github.com/atypica-ai/marketing-skills
仓库路径:skills/market-sizing
安装命令:
npx skills add https://github.com/atypica-ai/marketing-skills --skill market-sizing
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skills.shnpx skills
npx skills add https://github.com/atypica-ai/marketing-skills --skill market-sizing

简介

market-sizing 提供自下而上与自上而下两种市场规模估算方法。

  • 适用于创业计划、融资BP和市场进入策略制定场景。
  • 输出 TAM/SAM/SOM 范围及敏感性分析,附带命名数据来源。
  • 安装命令为 npx skills add https://github.com/atypica-ai/marketing-skills --skill market-sizing。
  • 数字仅为估算值,实际市场表现受多种变量影响,需谨慎引用。

SKILL.md

TAM / SAM / SOM Market Sizing

Produce both a bottom-up and a top-down analysis for every engagement. Both methods are required — they serve different purposes and must be reconciled. The goal is a defensible, sourced output — not a fast estimate.


When to use this skill

Trigger on any prompt that contains:

  • "TAM", "SAM", "SOM", or "market size"
  • "How big is the market for X"
  • "Total addressable market", "serviceable market", "obtainable market"
  • "Market sizing for [product/company]"
  • Investor deck context requiring market opportunity quantification

Loading examples

Before calculating, check the examples/ directory for a file matching the product's category. Load the closest match to calibrate data sources, filter logic, and ACV anchors before you begin.

FileUse when product is...
examples/b2b-saas.mdSoftware / AI tool sold to businesses
examples/b2c-consumer-brand.mdConsumer packaged goods, DTC brands, subscriptions
examples/b2b-physical.mdPhysical goods or materials sold to businesses

If no exact match exists, load the closest file and adapt.


Pre-flight: Classify the product before doing anything else

Before searching or calculating, classify the product along these two axes. The classification directly determines which data sources to use and which pricing method applies.

Axis 1 — Business model

CodeTypeRevenue unitPricing anchor
B2B-SaaSSoftware sold to businessesAnnual contract (ACV)10–20% of economic value created
B2B-PhysicalPhysical goods sold to businessesPer-unit price × annual volumeGross margin benchmarks by industry
B2C-BrandConsumer product (any category)Revenue per customer per yearAvg. purchase price × purchase frequency
B2C-SubscriptionConsumer subscriptionMonthly/annual subscription feeStated price or comp pricing
Marketplace/PlatformTakes % of GMVTake rate × GMVIndustry take rate benchmarks

Axis 2 — Market geography

  • US-only: Use US Census, NAICS codes, US industry associations
  • Global: Use global market reports, then apply regional share (US ~25–30% of global GDP; NA ~32%; APAC ~38%)
  • Single city/region: Use metro area population data + category penetration rates

Step-by-step workflow (universal)

Step 0 - Data Gathering

Since accuracy and reliability are key of this task, make sure you collect enough reliable data from reliable sources before the calculation. Limited estimation is fine with enough evidence support, but any kind of data making up or hallucination without evidence during the calculation and output phase is a fraud.

STEP 1 — Define the revenue unit

Before any market data search, lock down exactly what the product sells, to whom, and at what price.

Required inputs:

  • Who is the buyer? (job title / consumer demographic)
  • What is the unit of sale? (per seat, per SKU, per kg, per subscription, per transaction)
  • What is the price? (stated, or estimated by comp analysis)
  • What is the purchase frequency? (one-time, monthly, annual, recurring)

If price is unknown: Search "[product category] average price" OR "[closest competitor] pricing" and anchor to the median. For B2B-SaaS, also apply the 10–20% value rule: price = 10–20% of annual economic value the product creates for the customer.

Output of Step 1: A single sentence — *"The revenue unit is [X] sold to [Y] at [Z]/year."*

STEP 2 — TAM (Total Addressable Market)

TAM = the maximum theoretical revenue if every potential buyer purchased the product.

2A — Bottom-up method (PREFERRED for all product types)

For B2B products:

  1. Search for the number of firms in the target industry

- US: Use NAICS code lookup — search "NAICS [code] number of firms US" or site:siccode.com NAICS [code] - Global: Use IBISWorld, Statista, or national business registries

  1. Estimate the % that are genuinely relevant (apply product-fit filter at this stage only if very obvious — e.g. residential-only firms for a commercial B2B product)
  2. TAM = Total relevant firms × ACV

For B2C products:

  1. Find the total consumer population in the target geography (US Census, APPA, Statista, trade associations)
  2. Find the % in the target demographic/behavioral segment
  3. TAM = Segment population × annual spend per person

Search queries to use:

  • "[industry] number of [businesses/firms/brands] US 2024"
  • "NAICS [code] firm count employees revenue"
  • "[consumer category] number of [households/users/owners] US"
  • "[category] market size 2024" (for cross-reference only — not the primary method)

2B — Top-down method (SECONDARY cross-check)

  1. Find total category market size from analyst reports (Grand View, IMARC, Mordor, Precedence, IBISWorld)
  2. Apply funnel: Total market → relevant segment → product-type share → geography
  3. Search: "[category] software/product market size 2024" or "[category] industry revenue US 2024"

Rule: Bottom-up is the primary method. Top-down is the cross-check. Both results should be reported. If they diverge significantly (>2×), explain why (e.g. top-down only counts current spenders; bottom-up counts total potential).

TAM output format:

  • Single dollar figure (round to nearest $100M for large markets, $10M for mid-size)
  • Conservative range (low–high)
  • The math shown explicitly: N firms × $X ACV = $Y or N consumers × $Z annual spend = $Y

STEP 3 — SAM (Serviceable Addressable Market)

SAM = the subset of TAM that the specific product can actually serve today, given its business model, geography, language, and product fit.

Universal filter checklist — apply all that are relevant:

FilterTypical reductionHow to estimate
GeographyVariesIf US-only product, filter out non-US. If city-level, apply metro population fraction.
Product-fit segment30–60% reductionRemove segments the product doesn't serve (e.g. residential-only for commercial SaaS; solo operators who can't justify the cost)
Tech/channel readiness10–30% reduction% of target customers with internet access, cloud tools, or relevant distribution channel access
Language/regulatoryVariesOnly relevant for global products
Willingness to pay tier20–40% reductionRemove segments priced out of the product's tier

Formula: SAM = TAM × Filter₁ × Filter₂ ×... × Filterₙ

Blended ACV for SAM: If there are multiple customer segments (SME vs. mid-market, or mass vs. premium), calculate a weighted average ACV: Blended ACV = (Segment_A_ACV × weight_A) + (Segment_B_ACV × weight_B)

Search queries for filter data:

  • "[industry] percentage [commercial/residential/enterprise/SMB]"
  • "[industry] small business cloud software adoption rate"
  • "[consumer category] premium vs mass market share"

SAM output format:

  • Single dollar figure with conservative range
  • Every filter listed with its % reduction and data source
  • Blended ACV (if applicable)

STEP 4 — SOM (Serviceable Obtainable Market)

SOM = the portion of SAM the product can realistically capture in a defined timeframe (typically 3 years / Year 1–3 ramp).

Penetration rate benchmarks by product type:

Product typeYear 1Year 3Source basis
New B2B SaaS (no category)0.1–0.3% of SAM1.5–3% of SAMAndreessen Horowitz, OpenView benchmarks
New B2B SaaS (established category)0.3–0.8%3–7%Category incumbents' early growth data
New B2C consumer brand (retail)0.05–0.2% of SAM0.5–2%Nielsen new brand launch data
New B2C DTC subscription0.1–0.5%1–4%DTC cohort benchmarks
New B2B physical product0.2–0.5%2–5%Trade distribution ramp benchmarks

Cross-validation method: Find 2–3 comparable companies (same category, similar stage) and anchor SOM to their reported early ARR/revenue. Search: "[similar company] [Series A / early revenue / ARR] raised [year]"

Formula:

  • SOM (Year 1) = SAM × Year 1 penetration rate
  • SOM (Year 3) = SAM × Year 3 penetration rate
  • Customer count = SOM ÷ Blended ACV (sanity check — does this number of customers feel achievable given go-to-market?)

SOM output format:

  • Year 1 and Year 3 ARR/revenue targets
  • Customer count at each stage
  • Named comparable company validations
  • Penetration rate used (with justification)

STEP 5 — Reconcile and flag conflicts

After running both methods:

  1. If bottom-up TAM > top-down TAM: Usually because the product creates new demand (not just shifting existing spend). State this explicitly.
  2. If bottom-up TAM < top-down TAM: Often because the product is a niche within a large category. State which definition is being used.
  3. Always present a range, not a single number, for TAM and SAM. Single numbers imply false precision.
  4. Flag the 3 most sensitive assumptions — the ones where a change of ±20% would materially shift the output. These are the assumptions to validate with primary research.

Source hierarchy (use in this order, stop when satisfied)

Tier 1 — Primary/official sources (highest credibility)

  • US Census Bureau / Economic Census — firm counts, employment, revenue by NAICS code
  • NAICS/SIC code databases (siccode.com, census.gov) — industry firm counts
  • Industry trade associations (APPA for pets, CFMA for construction, NMMA for marine, etc.)
  • SEC filings / public company 10-Ks — addressable market disclosures
  • Government labour/business statistics (BLS, SBA, ONS)

Tier 2 — Reputable market research

  • IBISWorld, Grand View Research, Mordor Intelligence, IMARC Group, Precedence Research, Statista
  • Use these for: category market size, CAGR, segment shares
  • Cross-reference at least 2 sources — market research firms often disagree by 20–40%

Tier 3 — Comparable company evidence

  • Funding announcements (TechCrunch, Crunchbase, Construction Dive, etc.)
  • Public company revenue disclosures at IPO/Series B
  • Use for: SOM cross-validation, ACV benchmarks, growth rate validation

Tier 4 — Pricing/ACV benchmarks

  • Competitor pricing pages (direct)
  • G2, Capterra, Trustpilot reviews mentioning price
  • Stripe, Monetizely, OpenView pricing guides (for B2B SaaS)

ACV / pricing benchmarks by product type

Product typeTypical ACV/ASPNotes
SMB B2B SaaS (1–50 employees)$3,000–$15,000/yr10–20% of value created
Mid-market B2B SaaS (50–500 employees)$15,000–$60,000/yr
Enterprise B2B SaaS (500+ employees)$60,000–$500,000+/yr
Consumer subscription (premium)$100–$600/yr
Consumer packaged goods (premium)$30–$150/order, 6–12×/yr
B2B physical product (SMB)$5,000–$50,000/yrDepends heavily on industry
B2C DTC brand (avg. LTV proxy)2–3× first-order value

Common errors — check before finalising

ErrorHow to catch it
"1% of a $10B market" without proofAlways verify with bottom-up customer count
Top-down only, no bottom-upAlways run both methods
Using global TAM when product is US-onlyApply geography filter explicitly
Including non-buyers in TAM (e.g. residential firms in a commercial B2B)Apply product-fit filter before TAM finalisation
SOM penetration rate not benchmarkedAlways cite at least one comp company
ACV not grounded in value or comp pricingAlways show the value math or comp reference
Treating TAM as "market size from report" uncriticallyCheck the report's definition matches your product's scope
Single number without rangeAlways show low–high range

Industry-specific data source cheat sheet

IndustryBest firm-count sourceBest market-size sourceKey trade association
Construction / TradesNAICS via siccode.com or CensusIBISWorld, CFMA BenchmarkerCFMA, AGC, NECA
Pet / Consumer packaged goodsCensus NAICS 311APPA, Grand View, IMARCAPPA, PFI
Food & BeverageCensus NAICS 311/312Mordor, Grand View, MintelFMI, GMA
Beauty / Personal careCensus NAICS 325620Euromonitor, Grand ViewPBA, CEW
Fashion / ApparelCensus NAICS 315McKinsey Fashion Report, StatistaAAFA
SaaS / SoftwareCrunchbase, G2 categoriesGartner, IDC, ForresterVaries
AutomotiveNAICS 441/336Ward's Auto, IHS MarkitNADA, SEMA
B2B PackagingCensus NAICS 322/326Grand View, MordorPMMI, FPA

Output format

The output file is where you calculations are made. After collecting necessary data, make a step-by-step analysis & calculation on the file, finally yielding a summary and a comparison. Produce a structured markdown document containing:

  • Both methods in full, each covering TAM → SAM → SOM completely before moving to the next method. Each method include 3 sections (TAM, SAM, SOM), each with: headline figure + range, full calculation shown line by line, and key assumptions&sources listed
  • A standalone section for all used figures' sources & assumptions.
  • A summary: table with TAM / SAM / SOM figures (bottom-up and top-down side by side); a method comparison note explaining divergence if bottom-up and top-down differ by >2×

The following is an example markdown output with a structure you should follow:

"""

Market Sizing: BuildFlow AI

*AI-powered invoicing automation for HVAC and Electrical subcontractors — United States*


Method 1: Bottom-up

TAM

InputValueSource
HVAC firms (NAICS 238220)88,738siccode.com / US Census
Electrical firms (NAICS 238210)55,951siccode.com / US Census
% doing GC-facing commercial work55%SBA subcontractor mix data
ACV — HVAC SME tier$7,500/yr$1.5M avg. revenue × 0.5%; CFMA 2024
ACV — Electrical SME tier$10,000/yr$2.0M avg. revenue × 0.5%; CFMA 2024
ACV — mid-market tier (>$5M rev)$24,000/yrValue-based; comp: Siteline pricing
HVAC SME (48,806 × 82%):           40,021 × $7,500  = $300M
HVAC mid-market (48,806 × 18%):     8,785 × $24,000  = $211M
Electrical SME (30,773 × 82%):     25,234 × $10,000  = $252M
Electrical mid-market (30,773×18%): 5,539 × $24,000  = $133M
Value-based ceiling (5% leakage × 10–20% SaaS capture): ~$1.87B

→ TAM: $1.87B (range: $1.5B–$2.2B)

SAM

FilterReductionRemainingSource
Remove residential-only operators−40%47,748 firmsSBA / IBISWorld industry mix
Remove solo / owner-only operators−35%31,037 firmsCensus nonemployer statistics
Remove cloud / tech-unready firms−25%23,278 firmsConstruction tech adoption surveys 2024
Blended ACV:
  SME tier (70%):     $7,500  × 0.70 = $5,250
  Mid-market (30%):   $18,000 × 0.30 = $5,400
  Blended:                            = $10,650/yr

23,278 firms × $10,650 + mid-market uplift ≈ $560M

→ SAM: $560M (range: $430M–$690M)

SOM

Year% of SAMARRCustomers
Year 10.27%~$1.5M~140
Year 20.85%~$4.8M~450
Year 31.5–3.0%$8.4M–$16.8M790–1,580

*Benchmark: 1.5–3% of SAM by Year 3 — a.H./OpenView SaaS benchmarks for new B2B entrant in established category*

CompSignal
PayraRaised $15M (Edison Partners); AP automation for construction subcontractors
SitelineRaised $18M Series A; subcontractor billing / SOV management, comparable ICP
Adaptive Construction SolutionsBootstrapped to ~$5M ARR in 3 years; construction AR automation

→ SOM: $1.5M ARR (Year 1) / $8.4M–$16.8M ARR (Year 3), ~790–1,580 customers


Method 2: Top-down

TAM

Global construction accounting software (2025):  $2.64B  [Precedence Research]
× US share (76%):                                 $2.0B
× AP/AR segment (54.4%):                          $1.09B

→ TAM: $1.09B

SAM

Top-down TAM:                        $1.09B
× Specialty trade sub-share (~28%):  $305M
× HVAC + Electrical only (~50%):     $152M

→ SAM: $152M

SOM

SAM (top-down):              $152M
× 3% Year 3 penetration:     $4.6M

→ SOM: $0.4M ARR (Year 1) / $4.6M ARR (Year 3)


Sources

InputValue assumedSource
HVAC firms (NAICS 238220)88,738siccode.com / US Census
Electrical firms (NAICS 238210)55,951siccode.com / US Census
% GC-facing commercial work55%SBA subcontractor mix data
Avg. HVAC subcontractor revenue$1.5M/yrCFMA 2024 Benchmarker
Avg. Electrical subcontractor revenue$2.0M/yrCFMA 2024 Benchmarker
Subcontractor net profit margin2.2–3.5%CFMA 2024 Benchmarker
Profit lost to unbilled/errors~5% of revenueIndustry estimate
Global construction accounting software market$2.64B (2025)Precedence Research
AP/AR segment share54.4%Precedence Research
Cloud/tech readiness of small construction firms~75%Construction tech adoption surveys 2024
SaaS penetration benchmark (Year 3)1.5–3% of SAMa.H./OpenView SaaS benchmarks
Comp: Payra raise$15MEdison Partners announcement
Comp: Siteline raise$18M Series APublic announcement

Summary

Bottom-upTop-down
TAM$1.87B$1.09B
SAM$560M$152M
SOM — Year 1 / Year 3$1.5M / $8.4M–$16.8M ARR$0.4M / $4.6M ARR
Bottom-up preferred — top-down counts only existing software spend and misses the large paper/spreadsheet segment. Use top-down as the floor / downside scenario. """

Quality checklist before delivering output

  • Both bottom-up AND top-down methods calculated
  • Every input number has a named source
  • TAM and SAM shown as ranges, not single numbers
  • Blended ACV used where multiple customer segments exist
  • SOM penetration rate benchmarked against 1–2 comp companies
  • At least 3 sensitivity risks flagged
  • Part 1 markdown follows the Output Example structure exactly
  • Prose narration follows all 6 workflow steps
  • Top-down vs bottom-up divergence explained if >2×

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