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
- Scale (10–25): 10k+ → 25, 5k–9,999 → 20, 2k–4,999 → 15, 500–1,999 → 10, 50–499 → 5, <50 → 0.
- Revenue (15–20): $50M–$500M → 20 (sweet spot), $25–50M → 15, $500M–1B → 10, $10–25M → 8, $1B+ → 5.
- Industry (10–15): core → 15, adjacent → 10, stretch → 5, unknown → 5, excluded → −100 (auto-DQ).
- Complexity / specialization (15–30): the client-specific signal (multi-location count, market
count, competitor/complementary tech, digital maturity).
- Ownership / PE (10–20): PE-backed → 20, PE subsidiary → 15, VC/growth → 10, unknown → 5, founder/non-profit → 0.
- 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
- Pick 4–6 signal groups summing to 80–100. 2. Set thresholds. 3. Write scoring rules per signal.
- 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.
1---2name: tam-scoring3description: Build and run scoring models that tier raw company lists into campaign-ready segments, and point each scored data point at the ColdIQ endpoint that sources it. Use when building a scoring model, tiering a TAM, defining signal groups and point allocations, setting tier thresholds, cleaning a list before scoring, or running a Python scorer on an export. Triggers on "score companies", "tier the list", "scoring model", "TAM scoring", "ICP fit score", "tier thresholds", "100-point model", "qualify accounts". Do NOT use for enrichment/search to BUILD the list (see apollo-search / coldiq-search-enrich), signal sourcing (see signal-detection), or list dedup (see list-dedup).4---56# TAM Scoring78Assign every company a numeric score from observable signals, then bucket into tiers. ColdIQ9models run 80–100 points across 4–6 signal groups. The math is local; this skill notes which10ColdIQ endpoint provides each scored data point so the model runs on fresh data.1112## ColdIQ Marketplace Endpoints (data sources for scored fields)1314| Scored data point | Method | Path | Credits | Endpoint ID | Notes |15|-------------------|--------|------|---------|-------------|-------|16| Firmographics (size, revenue, industry, funding) | POST | `/v1/limadata/enrich/company` | 1 | `limadata.enrich.company` | One call for most groups |17| Large-scale company list | POST | `/v1/ai-ark/companies` | per result | `ai_ark.companies.search` | Build the list to score |18| Tech-stack signal | POST | `/v1/builtwith/domain` | flat | `builtwith.domain` | Complexity/specialization group |19| Funding / PE backing | GET | `/v1/signalbase/funding-signals` | unknown | `signalbase.funding_signals` | Ownership/bonus groups |2021Score from enriched data — don't pay to enrich rows you'll DQ. Filter obvious DQs first, then:22→ **POST** `/v1/limadata/enrich/company` · 1 cr · `limadata.enrich.company`2324## Standard tier thresholds2526| Tier | Score | Action |27|------|-------|--------|28| Tier 1 | 65+ | Top priority, send first, best personalization |29| Tier 2 | 50–64 | Strong fit, second wave |30| Tier 3 | 35–49 | Marginal, volume plays only |31| DQ | <35 | Do not send |3233## Common signal groups34351. **Scale (10–25):** 10k+ → 25, 5k–9,999 → 20, 2k–4,999 → 15, 500–1,999 → 10, 50–499 → 5, <50 → 0.362. **Revenue (15–20):** $50M–$500M → 20 (sweet spot), $25–50M → 15, $500M–1B → 10, $10–25M → 8, $1B+ → 5.373. **Industry (10–15):** core → 15, adjacent → 10, stretch → 5, unknown → 5, excluded → −100 (auto-DQ).384. **Complexity / specialization (15–30):** the client-specific signal (multi-location count, market39 count, competitor/complementary tech, digital maturity).405. **Ownership / PE (10–20):** PE-backed → 20, PE subsidiary → 15, VC/growth → 10, unknown → 5, founder/non-profit → 0.416. **Bonus (5–10):** franchise HQ, cash-flow keywords, rapid growth, recent funding, relevant hiring.4243## Data cleanup before scoring4445```python46BOGUS_NAMES = {"local my business", "google ai plugin", "auto-entrepreneur"}47def is_bogus(name):48 l = name.strip().lower()49 return l in BOGUS_NAMES or any(s in l for s in ["follow us", "test account"])50# Remove slug franchises (keller-williams-realty-dpr), flag extreme values, dedup by name (keep top score).51```5253## Python scorer pattern5455```python56def score_company(row):57 total, breakdown = 0, []58 for label, fn, field in [("Scale", score_scale, "# Employees"),59 ("Revenue", score_revenue, "Annual Revenue"),60 ("Industry", score_industry, "Industry"),61 ("Custom", score_custom, None)]:62 pts, reason = fn(row if field is None else parse_number(row.get(field, 0)))63 total += pts; breakdown.append(f"{label}: {pts} ({reason})")64 if any("EXCLUDED" in b for b in breakdown): return -1, "DQ", breakdown65 tier = "Tier 1" if total>=65 else "Tier 2" if total>=50 else "Tier 3" if total>=35 else "DQ"66 return total, tier, breakdown67# load → clean(is_bogus) → score → dedup by name(keep highest) → sort desc → export CSV with Score/Tier/Breakdown68```6970## How to build a new model71721. Pick 4–6 signal groups summing to 80–100. 2. Set thresholds. 3. Write scoring rules per signal.734. Clean data. 5. Run on the enriched export. 6. Generate per-tier CSVs. 7. Send a sample to the74client for approval. 8. Re-run when new enrichment data arrives. Dedup first75([list-dedup](../list-dedup/SKILL.md)) so you never score a company twice.
cold-iq