Tam Map

TAM MAP - Total Addressable Market Builder

Build comprehensive business databases for any B2B vertical by discovering, scraping, and deduplicating data from every available source. Produces a unified, deduplicated CSV ready for enrichment.

Origin: A roofing TAM build - 113K raw records from 20+ sources, deduplicated to 89,562 unique businesses, 20,557 campaign-ready leads at ~$15 total cost.

The 7 Phases

Phase 1: Source Discovery (THE MOST IMPORTANT PHASE)

The quality of the TAM depends on source breadth. For every vertical, research and catalog every available data source across ALL of these categories:

Source Categories Checklist

How to Research Sources

1. Web search for "{vertical}" site:*.gov database to find government/regulatory sources
2. Web search for "{vertical}" association directory members to find trade associations
3. Web search for "{vertical}" certified contractors or "{vertical}" partner directory for manufacturer/vendor directories
4. Check NAICS codes at https://www.naics.com/search/ - needed for SAM.gov
5. Check state licensing - search "{vertical}" license {state} for each major state
6. Check review platforms - which platforms does this vertical use? (G2 for SaaS, Houzz for home services, Clutch for agencies)
7. Check if IcyPeas has coverage - run a free count query first

Source Manifest Output

Create manifest.json in the output directory:

{
  "vertical": "plumbing",
  "date": "2026-03-22",
  "total_sources": 18,
  "sources": [
    {
      "name": "icypeas_plumbing",
      "category": "company_database",
      "method": "api",
      "url": "https://app.icypeas.com",
      "estimated_records": 30000,
      "has_owner_names": false,
      "has_emails": false,
      "has_addresses": false,
      "has_phone": false,
      "cost_estimate": "IcyPeas credits",
      "priority": 1,
      "notes": "Broad coverage, domains + LinkedIn URLs"
    }
  ],
  "total_estimated_records": 95000,
  "estimated_unique_after_dedup": 45000
}

Priority Scoring

• Priority 1 (must-have): Sources with owner names baked in (state licenses), broad coverage databases (IcyPeas), Google Maps
• Priority 2 (high value): Industry association directories, manufacturer certs, BBB, SAM.gov
• Priority 3 (nice to have): Review platforms, Facebook pages, awards lists, job board signals

Phase 1.5: Keyword Expansion

Before configuring sources, discover adjacent keywords that target the same TAM. The primary keyword alone often misses 30-60% of the market.

How It Works

1. Start with the primary keyword (e.g., "coffee shop")
2. Brainstorm adjacent terms that describe the same businesses differently:
   • Synonyms: "cafe", "coffeehouse"
   • Subtypes: "espresso bar", "coffee roaster", "coffee stand"
   • Related: "bakery cafe", "tea and coffee"
3. Run IcyPeas free count queries for each candidate keyword to estimate incremental volume
4. Check overlap - many adjacent keywords return 40-70% duplicates. That's fine - dedup handles it. The goal is to catch the 30-60% that only appear under one keyword.

Example: Coffee Shop Expansion

Rules

• Always run counts before pulling - IcyPeas count queries are free. Don't pull 43K "cafe" records if count shows only 2K.
• Dedup cross-keyword overlap - use the same dedup pipeline. Adjacent keywords may overlap 40-70%.
• Run expanded keywords through ZIP scrape too - Google Maps queries for "cafe near [ZIP]" find different businesses than "coffee shop near [ZIP]".
• Document the expansion - save the keyword -> count -> incremental mapping in the manifest for future builds.

Phase 2: Config Generation

Build a TAMConfig JSON from the manifest. Example config structure:

config = TAMConfig(
    vertical_name="plumbing",
    keywords=["plumbing", "plumber", "plumbing contractor", "plumbing company", "plumbing repair"],
    naics_codes=["238220"],
    sources=[
        TAMSource(
            name="icypeas_plumbing",
            category="company_database",
            method="api",
            config={"type": "icypeas"},
            estimated_records=30000,
            priority=1,
        ),
        TAMSource(
            name="sam_gov_plumbing",
            category="federal_contracts",
            method="api",
            config={"type": "sam_gov"},
            estimated_records=2000,
            priority=2,
        ),
        # ... more sources
    ],
    generic_filters=["plumbing contractor", "best plumber", "plumber near me"],
    output_dir="./output/plumbing_20260322",
)
config.save("config.json")

NAICS Code Reference (Common Verticals)

Phase 3: Collection

Automated Sources

These source types can be automated with reusable scraper modules:

Google Maps (via ZIP Code Scraping)

For local/service businesses, run Google Maps scraping via Serper's local search:

Methodology:

1. Load a comprehensive US ZIP code list (~40K ZIPs)
2. For each ZIP, query "{keyword}" near {ZIP} via Serper local results
3. Extract business name, address, phone, website, rating
4. Output to businesses.csv with the unified schema

Bespoke Sources (Write Per-Vertical)

For sources without a reusable scraper (state license DBs, niche directories), write a one-off script. Follow these patterns:

Serper site-scoped scrape (for any website with a directory):

# Pseudocode for site-scoped scraping
# 1. Define query templates: site:{site} inurl:profile "{keyword}" "{state}"
# 2. For each state + keyword combo, run Serper query
# 3. Extract business name, state, source URL from results
# 4. Filter: only keep profile pages (check URL pattern)
# 5. Output to businesses.csv with unified schema

State license database (requires per-state research):

• Check if state has an open data portal (Socrata)
• Check if state has a searchable license lookup
• Some states expose CSV/API (FL DBPR, CA CSLB, IL Socrata)
• Some require Selenium (CAPTCHAs, JS SPAs)
• Owner names from license records are the highest-value free data

Every bespoke scraper MUST output businesses.csv with the unified schema columns.

Phase 4: Deduplication

The dedup engine auto-detects CSV column names and maps them to the unified schema. No hardcoded source mappers needed.

Three-Tier Dedup Algorithm

1. Tier 1: Domain match - Records with the same normalized domain are merged. Keeps the record with the most populated fields, fills empty fields from duplicates.
2. Tier 2: Company name + state - Domainless records matched to domain records by normalized company name + exact state.
3. Tier 3: Phone match - Remaining unmatched records matched by normalized 10-digit phone number.
4. Internal dedup - Remaining unmatched records deduplicated among themselves by name+state.

Output

• all_sources_deduped.csv - unified deduplicated master list
• dedup_stats.json - per-source counts, overlap analysis, dedup rates

Phase 4.5: Existing Leads Exclusion (MANDATORY)

ALWAYS run exclusion before enrichment. Enriching leads that are already in your email platform wastes API credits. In one build, the exclusion rate was 31.7% - that's 31.7% of enrichment spend saved by running exclusion first.

Removes leads already in your email platform or CRM so only net new records proceed to enrichment. Prevents double-contacting prospects and wasting enrichment credits.

How It Works

1. Email platform exclusion: Build a domain cache from all existing leads in your email sending platform using async parallel fetching (e.g., aiohttp with 20 concurrent requests).
2. CRM/database exclusion: Query your CRM or database for all known domains.
3. Merge: Combine both domain sets into a single exclusion list.
4. Filter: Remove TAM records whose domain matches the exclusion set.
5. Output: net_new_leads.csv + exclusion stats in dedup_stats.json.

Performance

Async fetching with 20 concurrent requests achieves ~7-8 pages/sec (vs ~0.4/sec sync). For large workspaces:

Output Stats

The exclusion stats show exactly how much TAM is already contacted:

TAM input:              39,721
Excluded (domain):      10,870
Excluded (email):        1,711
Net new leads:          27,140
Exclusion rate:          31.7%

Cache

• Store domain cache locally (e.g., ./cache/platform_domains.json)
• Contains all unique domains and emails from your email platform
• Auto-refresh after 24 hours
• Force rebuild when needed
• Use workspace-specific API keys if running multiple client workspaces

Phase 5: Enrichment Hand-off

Pre-Flight Checks (BEFORE starting enrichment)

1. Check email verification credits: Ensure your email verification service (e.g., MillionVerifier, ZeroBounce) has sufficient credits - budget for ~1 credit per email, with 30-50% of total leads having emails.
2. Estimate enrichment volume: After exclusion, how many leads need enrichment? If >10K, plan for batched processing.
3. Check API availability: Ensure all enrichment API keys are valid and credits/limits are sufficient.

Adaptive Email Waterfall

For large TAMs (>5K leads), sample before committing to the full waterfall:

1. Sample 100-200 domains from the net new CSV
2. Run email finding on the sample only
3. Measure hit rate - if generic emails (info@, contact@) hit >60%, the full waterfall is worth running. If <30%, consider:
   • Skipping personal email search (expensive, low yield for local businesses)
   • Going straight to pattern-based generic email verification
   • Focusing enrichment budget on the highest-value segments

Post-Enrichment Verification

ALWAYS verify emails before campaign prep. MX-guess generic emails are ~45% undeliverable.

• Keep: valid (code 1) + catch_all (code 2)
• Drop: unknown (3), error (4), disposable (5), invalid (6)

After Enrichment

The enriched output feeds into the standard campaign workflow:

1. Segment by geography, company size, or industry vertical
2. Write copy personalized per segment
3. Launch campaigns

Cost Model

Typical TAM build: $10-20 in API costs for 50K-100K records (excluding ZIP scrape).

Reference Builds

Roofing TAM (First Build)

Coffee Shop TAM (Second Build)

Important Notes

• Source discovery is the bottleneck, not scraping. Spend the most time on Phase 1. A TAM is only as good as its source coverage.
• State license databases are gold for local services. They often include owner names, which bypasses the most expensive enrichment step.
• Always verify emails before sending. MX-guess generic emails are ~45% undeliverable. Keep valid (code 1) + catch_all (code 2), drop everything else.
• IcyPeas /find-companies/count is free - use it to estimate volume before committing credits.
• Every scraper outputs businesses.csv with the unified schema. The dedup engine auto-maps columns.
• Bespoke scrapers get saved to sources/{vertical}/ for reuse on future builds.
• ALWAYS run exclusion (Phase 4.5) before enrichment. Saves 20-40% of enrichment spend by not re-enriching already-contacted leads.
• Keyword expansion (Phase 1.5) is high leverage for local businesses. Adjacent terms (cafe vs coffee shop, roaster vs coffee roaster) can uncover 30-60% more of the market.
• Pattern-based email recovery does NOT work for single-location businesses. Tested 10 patterns x 15,934 domains = 0 hits. These businesses simply don't have functional generic email addresses.
• Use workspace-specific API keys for exclusion if running multiple client workspaces.