tam-sizer

name

tam-sizer

description

Size the total addressable market (TAM) for an Ideal Customer Profile (ICP) using ZoomInfo's verified company database. Iteratively refine the firmographic and technographic filter set with the user until the account universe matches their intent — then return both the count and the working filter set that other skills (build-list, score-accounts) can consume. Use for territory and capacity design, investor-ready market sizing, and ICP sharpening. Triggers on phrases like "size the market", "TAM for", "addressable market", "how many companies match", "is my ICP too broad/narrow", "refine my ICP filters".

TAM Sizer

Iteratively refine a company-level ICP filter set against ZoomInfo's company database. Each pass returns a count, a banded sizing read, labelled sample views, and concrete refinement options. Terminates when the user finalizes — output is both the count and a structured filter-set artifact ready for build-list, score-accounts, or find-similar.

When to use

• tam-sizer — user wants count + shape + working filter set, willing to iterate.
• build-list — filter set already settled; user wants the exportable list.
• find-similar — user has a seed account, not a filter-based market.

Scope

TAM here = company count AND working filter set, both first-class outputs.

This skill does NOT size contacts. Buyer-persona criteria ("CTOs", "VP Sales") are recorded but NOT applied to the count — they describe who you sell into, not who the account is. Persona discovery is build-list / search-contacts once the filter set is settled.

Input

• ICP description (recommended) — natural language, OR "my ICP" / "our ICP" / nothing (fall back to get_gtm_context).
• Use case (optional) — territory design / investor sizing / ICP sharpening (default).
• SAM hypothesis inputs (optional) — addressable_fraction (0–1) and arpa_usd.

Workflow

1. Pull GTM context (always)

Call get_gtm_context(detailed: true) first. Use throughout — for filter defaults when the user is vague, sanity-check expectations on the sample, and refinement recommendations. If empty, proceed with user filters only and surface the absence.

2. Parse + merge ICP

Reconcile user text and GTM context. User text wins direct conflicts ("SF" overrides GTM's "North America"); GTM fills gaps. Tag every dimension as user-specified / inherited from GTM / unspecified. Persona criteria recorded but not applied.

3. Disambiguate ambiguous regions BEFORE the search

• "EU" can mean European Union (27 countries) or Europe (continent) — continent: Europe includes Russia, Turkey, UK, Switzerland, Norway. Ask one clarifying question if "EU" is unclarified.
• "Asia" includes Russia; "Americas" vs "North America" vs "US/Canada" — confirm if uncertain.

4. Lookup all filter values

Call lookup to resolve free text to standardized values. Don't guess.

lookup with multi-field fuzzyMatch may return empty data for the second field — call once per fieldName when using fuzzyMatch.

Field	fieldName	Notes
Industries	industries	Passed to search_companies by attributes.name (e.g., "Software"), not id. Narrow to sub-industries ("Customer Relationship Management (CRM) Software") over top-level.
Employee bands	employee-count	Enum values (50to99, etc.) in employeeCount.
Revenue bands	revenue-ranges	Or revenueMin/revenueMax (thousands USD).
Geography	metro-regions / states / countries / continents
Technographics	tech-vendors → tech-products filtered by vendor
NAICS / SIC	naics-codes / sic-codes
Rankings	company-rankings

Taxonomy-gap gate (mandatory)

For every industry term (user-supplied or from GTM context), call lookup industries fuzzyMatch=<term> first.

• ≥1 match → use the resolved industry name in industryCodes.
• 0 matches → do NOT silently fall back to industryKeywords. Surface: "No matching industry in ZI's taxonomy for <term>." Recommend one of: (a) seed company + find-similar, (b) external list import via a company-match service, (c) explicit confirmation to proceed with industryKeywords knowing the noise risk. Wait for user confirmation.

Common gaps: climate-tech, sustainability, cleantech, sales-engagement, RevOps, agentic-AI, vector-databases.

5. Get the count

search_companies with resolved filters, pageSize: 1. Use meta.totalResults.

meta.fieldResolution does NOT echo continent / revenueMin/Max / fundingAmountMin/Max / employeeRangeMin/Max — always populate "Filters Applied" from input parsing.

Data-sparsity probe (mandatory when funding/revenue filters applied)

ZI's coverage of fundingAmountMin/Max, fundingStartDate/EndDate, revenueMin/Max is sparse for many segments. A filter collapsing the count to 0 may be a data gap, not a narrow ICP.

1. Run count with filter (count_filtered).
2. Run count without the funding/revenue filter (count_unfiltered), other filters intact.
3. If count_filtered / count_unfiltered < 0.1 (filter drops >90%):
   • Treat as data-sparse, not narrow ICP.
   • Use count_unfiltered as operative TAM for banding.
   • Surface: "ZI's coverage of [field] is sparse for this segment. Filtered: X. Operative TAM: Y (unfiltered)."
4. Otherwise: count_filtered is the operative TAM.

Both numbers always shown.

6. Classify the sizing band

TAM size	Band	Read
> 50,000	Too broad	Probably not operational.
5,000 – 50,000	Healthy enterprise/mid-market	Suggest tier segmentation.
1,000 – 5,000	Sweet spot	Focused primary-tier list.
250 – 1,000	Tight/niche	Flag capacity feasibility.
< 250	Too narrow	Coverage risk; suggest widening.

7. Fetch representative-account sample (two views)

search_companies twice in parallel, each pageSize: 12:

• Trophy view — default sort. ZI's internal ranking (revenue-biased). Biggest logos.
• Anchor view — sort: "-employeeCount". Largest by headcount.

search_companies.sort does NOT support relevance — only name / employeeCount / revenue and - variants. Dedupe by companyId; label each row with its view.

8. Directional shape sample + noise rate

search_companies with pageSize: 100, default sort. Revenue-skewed — NOT a census. Use for:

• ICP sanity check — do top names look like the ICP, or contain conglomerate/BPO/staffing noise?
• Geographic + sub-industry shape — directionally useful even when revenue is biased.
• Noise-rate estimation — count rows in top 25 that visibly don't match the ICP. Rate = noisy_rows / 25.

Do NOT compute revenue-band or employee-band % from this sample. Skip the directional table entirely when TAM > 50,000.

Noise-adjusted TAM (mandatory when noise ≥20%)

• tam_noise_adjusted = round(raw_count × (1 − noise_rate), 2 sig figs).
• Re-classify the band against tam_noise_adjusted.
• Cite specific noisy samples ("of top 25, 6 are wineries / solar installers").
• Report order: raw count → noise rate → adjusted TAM → band.

20–60% noise is common for keyword-fallback or taxonomy-gap searches — a stronger signal to revisit the filter set than to ship the count.

9. SAM hypothesis (only if both inputs supplied)

• SAM count = TAM × addressable_fraction
• SAM revenue = SAM count × arpa_usd
• Label: Hypothesis, not forecast.

10. Self-check before output

• ☑ Headline count to ≤2 sig figs.
• ☑ Taxonomy gap gate cleared — every industry term resolved via lookup, OR user explicitly confirmed industryKeywords proceed.
• ☑ Data-sparsity probe run when funding/revenue filters applied.
• ☑ Noise-adjusted TAM computed when top-25 noise ≥20%.
• ☑ Every unspecified dimension flagged from input parsing (not fieldResolution).
• ☑ GTM-inherited filters labeled separately from user-specified.
• ☑ Persona criteria marked "not applied to company TAM."
• ☑ Both sample views shown, labeled.
• ☑ Directional shape only if count ≤ 50k; no revenue/employee % bands ever.
• ☑ Refinement names specific dimension + estimated post-refinement count.
• ☑ Region disambiguation resolved.

11. Present refinement options + loop

• Too broad (>50k) → 2–3 narrowing options with estimated impact.
• Too narrow (<250) → 2–3 widening options.
• Healthy band → tier segmentation OR finalize.
• Always → offer "save filters" exit to chain targets.

Re-run from step 4 when filter changes. Surface filter-set diff each pass.

Terminates on finalize or hand-off.

Output Format

TL;DR — TAM Sizing for [ICP one-liner] · Pass [N]

Use case: [restate].

Headline. ~[count] companies. Band: [too broad / healthy / sweet spot / tight / too narrow].

If data-sparsity probe fired: Raw filtered: X · Unfiltered: Y · Operative TAM: Y (filter is data-sparse).

If noise-adjusted: Raw: A · Sample noise: ~B% · Noise-adjusted: C (band classified against C).

Read. [1–2 sentences: operational? dominant skew? most consequential refinement?]

This pass's filter diff: [for pass N>1]

---

Filters Applied

Dimension	Value	Source

Source legend: user-specified · inherited from GTM · unspecified · approximation. Persona criteria → ℹ️ NOT applied to company TAM.

Representative Accounts (≤25, two views)

Trophy = ZI internal ranking (revenue-biased) · Anchor = -employeeCount.

| # | Company | Industry | Employees | Revenue | Country | View | ZoomInfo ID |

Sanity check. Do these look like the ICP, or include conglomerate / BPO / staffing noise? If noise → name the specific narrowing filter that removes it.

Directional Shape (only if count ≤ 50,000)

By country (top 5) · By sub-industry (top 5). Sample is revenue-biased; directional only.

Sizing Band & Refinement

Band: [letter]. Why this band. [1–2 sentences on count + operational implication.] Refinement options:

1. [Dimension change + estimated post-refinement count]
2. [Alternative]
3. [Optional]

Or: finalize and hand off to build-list / score-accounts / find-similar.

Caveats (when relevant)

• Industry-classification looseness — top-level industries classify conglomerates inside. Narrow to sub-industries for B2B.
• Funding approximation — fundingAmountMin captures total raised, not stage.
• Region scope — "EU" was resolved as [EU-27 / Europe-continent]. Confirm if mismatch.
• Subsidiary records — search_companies returns parent + subsidiary. Consider subUnitTypes filter for investor-ready TAM.

SAM Hypothesis (only if both inputs supplied)

Hypothesis, not forecast.

Metric	Value
TAM count
Addressable fraction
SAM count
ARPA (USD)
SAM revenue

Final Filter Set (on finalize)

{
  "industryCodes": "...",
  "employeeCount": "...",
  "metroRegion": "...",
  "continent": "...",
  "revenueMin": ...,
  "fundingAmountMin": ...,
  "techAttributeTagList": "...",
  "_meta": {"tam_count": ..., "band": "...", "pass_count": ..., "use_case": "..."}
}

Chain Targets

• build-list → exportable account list.
• search-contacts → buyer-persona discovery at these accounts.
• score-accounts → rank by buying signal and ICP fit.
• find-similar → adjacent accounts from a seed.