Tam Sizer

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.

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

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

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.

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.