tam-scoring

TAM Scoring

Assign every company a numeric score from observable signals, then bucket into tiers. ColdIQ models run 80–100 points across 4–6 signal groups. The math is local; this skill notes which ColdIQ endpoint provides each scored data point so the model runs on fresh data.

ColdIQ Marketplace Endpoints (data sources for scored fields)

Scored data point	Method	Path	Credits	Endpoint ID	Notes
Firmographics (size, revenue, industry, funding)	POST	/v1/limadata/enrich/company	1	limadata.enrich.company	One call for most groups
Large-scale company list	POST	/v1/ai-ark/companies	per result	ai_ark.companies.search	Build the list to score
Tech-stack signal	POST	/v1/builtwith/domain	flat	builtwith.domain	Complexity/specialization group
Funding / PE backing	GET	/v1/signalbase/funding-signals	unknown	signalbase.funding_signals	Ownership/bonus groups

Score from enriched data — don't pay to enrich rows you'll DQ. Filter obvious DQs first, then: → POST /v1/limadata/enrich/company · 1 cr · limadata.enrich.company

Standard tier thresholds

Tier	Score	Action
Tier 1	65+	Top priority, send first, best personalization
Tier 2	50–64	Strong fit, second wave
Tier 3	35–49	Marginal, volume plays only
DQ	<35	Do not send

Common signal groups

1. Scale (10–25): 10k+ → 25, 5k–9,999 → 20, 2k–4,999 → 15, 500–1,999 → 10, 50–499 → 5, <50 → 0.
2. Revenue (15–20): $50M–$500M → 20 (sweet spot), $25–50M → 15, $500M–1B → 10, $10–25M → 8, $1B+ → 5.
3. Industry (10–15): core → 15, adjacent → 10, stretch → 5, unknown → 5, excluded → −100 (auto-DQ).
4. Complexity / specialization (15–30): the client-specific signal (multi-location count, market count, competitor/complementary tech, digital maturity).
5. Ownership / PE (10–20): PE-backed → 20, PE subsidiary → 15, VC/growth → 10, unknown → 5, founder/non-profit → 0.
6. Bonus (5–10): franchise HQ, cash-flow keywords, rapid growth, recent funding, relevant hiring.

Data cleanup before scoring

BOGUS_NAMES = {"local my business", "google ai plugin", "auto-entrepreneur"}
def is_bogus(name):
    l = name.strip().lower()
    return l in BOGUS_NAMES or any(s in l for s in ["follow us", "test account"])
# Remove slug franchises (keller-williams-realty-dpr), flag extreme values, dedup by name (keep top score).

Python scorer pattern

def score_company(row):
    total, breakdown = 0, []
    for label, fn, field in [("Scale", score_scale, "# Employees"),
                             ("Revenue", score_revenue, "Annual Revenue"),
                             ("Industry", score_industry, "Industry"),
                             ("Custom", score_custom, None)]:
        pts, reason = fn(row if field is None else parse_number(row.get(field, 0)))
        total += pts; breakdown.append(f"{label}: {pts} ({reason})")
    if any("EXCLUDED" in b for b in breakdown): return -1, "DQ", breakdown
    tier = "Tier 1" if total>=65 else "Tier 2" if total>=50 else "Tier 3" if total>=35 else "DQ"
    return total, tier, breakdown
# load → clean(is_bogus) → score → dedup by name(keep highest) → sort desc → export CSV with Score/Tier/Breakdown

How to build a new model

1. Pick 4–6 signal groups summing to 80–100. 2. Set thresholds. 3. Write scoring rules per signal.
2. Clean data. 5. Run on the enriched export. 6. Generate per-tier CSVs. 7. Send a sample to the client for approval. 8. Re-run when new enrichment data arrives. Dedup first (list-dedup) so you never score a company twice.