Lead Gen — The List and the Model That Ranks It

You turn "we sell X to Y" into a deduplicated, scored, compliance-cleared roster of named accounts and people. You define the target, you build the list, you rank it — then you stop. What you produce is a prioritized roster plus the rationale that ranked it; the hand-off is the finish line, not the campaign.

The pipeline — three phases, two gates

Run these in order. Each gate is a hard stop: do not advance until the prior phase produced its artifact.

1. Define the target → a falsifiable ICP + persona. Why: you cannot dedupe or score against a vibe; a vague ICP guarantees reps chase the wrong companies.
2. Build the list → sourced, deduped, verified rows with provenance. Why: an unverified or undocumented list is a deliverability and legal liability before a single email goes out.
3. Score & prioritize → tiered list (A/B/C) with subscores + handoff packet. Why: an unsorted list means reps work the easy-to-reach names, not the right ones.

Between phase 2 and the handoff sits the compliance gate (GDPR LIA + CAN-SPAM). Run it before you hand anything off, never after the first send.

Phase 1 — Define a falsifiable ICP

An ICP is falsifiable when you can look at any company and answer "in or out?" with no judgement call. Write three blocks:

• Firmographic — headcount band, revenue band, region/country, industry/NAICS, funding stage. Numbers, not adjectives.
• Technographic / intent — required stack (e.g. "runs Salesforce"), or an active trigger (hiring for role X, recently raised, surging on a topic). Apollo filters on 1,500+ technologies and active job postings, so make these checkable. (docs.apollo.io People API Search, accessed 2026-06-02.)
• Negative criteria — the disqualify if… list. This is the half everyone skips and the half that saves the most rep time.

Then write the buyer persona(s) inside the account: title, seniority, the pain they own, the trigger that makes now the moment.

BAD ICP (un-falsifiable — every company "kind of" fits):
  "Mid-market SaaS companies that could use better analytics."

GOOD ICP (any company resolves to in/out):
  Firmographic:   50–500 employees · $5M–$50M ARR · US + EU · B2B SaaS
  Technographic:  runs Snowflake OR BigQuery · hiring a "Data Analyst" now
  Negative:       DISQUALIFY IF <50 employees · agency/reseller · no data team
  Persona:        Head of Data / VP Eng · owns dashboard sprawl · triggered by
                  a recent funding round (new headcount to equip)

Decision — pick the qualification framework by deal size

Do not default to BANT. The framework must match the deal's size and cycle, or you qualify on the wrong signals. (leadsatscale.com / callingagency.com qualification guides, accessed 2026-06-02.)

Framework	Stands for	Use when
BANT	Budget · Authority · Need · Timeline	High-velocity SMB, deals under ~$50K ARR, short cycle, 1–2 stakeholders
CHAMP	Challenges · Authority · Money · Prioritization	Consultative selling — lead with the prospect's problem, not your budget question
MEDDIC	Metrics · Economic-buyer · Decision-criteria · Decision-process · Identify-pain · Champion	Enterprise, deals over ~$100K, 5+ stakeholders, long cycle

The framework you pick becomes the qualification fields on every row — so choose it before you score, not after.

Phase 2 — Build the list

Source selection. No single database wins, so the 2025 norm is a waterfall: layer providers and stop at the first verified hit. (starnus.com / cleanlist.ai provider comparisons, accessed 2026-06-02.)

Provider	Rough coverage	Note
Apollo	~200M contacts	Search is free + credit-free; enrichment costs credits; ~78% email accuracy
ZoomInfo	321M+ contacts / 104M+ companies	~84% email accuracy; strongest firmographics
People Data Labs	Broad person/company graph	Good as a waterfall fill layer
Clay	Orchestrates 100+ sources	The waterfall engine — runs the layering for you

The waterfall rule: order providers by accuracy-per-dollar, query the next layer only for rows the previous one missed or could not verify, and stop at the first verified hit. You pay once per contact, not once per provider.

Apollo People Search → Enrichment flow. Search and enrichment are two different endpoints — search finds people but returns no emails/phones; enrichment (credit-consuming) returns the contact data. (docs.apollo.io, accessed 2026-06-02.)

1. POST /api/v1/mixed_people/api_search   (free, no credits)
   filters: person_titles, person_seniorities, organization_locations,
            organization_num_employees_ranges, q_organization_keyword_tags,
            currently_using_any_of_technology_uids, q_organization_job_titles
   → returns up to 50,000 records (100/page × 500 pages) — IDs + firmographics,
     NO email/phone.

2. POST /api/v1/people/bulk_match     (consumes credits)
   → enriches the IDs you actually want with email + phone.

Search broad and free first, then spend credits enriching only the rows that survive your ICP filter and dedupe.

Dedupe against the CRM. Before enriching, strip rows that already exist in the CRM (match on company domain + person email/LinkedIn). You do not pay to re-source a known account, and you do not want a rep cold-touching an active opportunity.

Verify — mandatory, not optional. Apollo (~78%) and ZoomInfo (~84%) email accuracy both sit at the edge of the high-volume-sender red-flag line. (cleanlist.ai / fundraiseinsider.com, accessed 2026-06-02.) Run a verification pass (bounce-check the address) before any row is handed off — a stale list is a lead-gen defect, not a copy or deliverability problem.

Full provider comparison, the waterfall ordering heuristic, and the provenance/compliance field spec each row must carry → references/data-sources.md.

Phase 3 — Score & prioritize

Use a composite 100-point model. Single-signal scoring fails; the proven split is ~30 fit + ~50 engagement + ~20 intent. (houseofmartech.com / theinsightcollective.com intent-scoring guides, accessed 2026-06-02.)

• Fit (≈30) — how well the account matches the ICP firmographics/technographics.
• Engagement (≈50) — behavioral signals: site visits, content downloads, replies, demo views.
• Intent (≈20) — third-party intent surge on your category/keywords.

The load-bearing rule: intent without fit is noise. A 10-person company surging on "enterprise CRM" is not your buyer — fit gates the score. Never let an intent spike alone tier a row up.

Tier on the total:

Tier	Score	SLA
A	90–100	Route now, first contact within 24h
B	75–89	48h SLA
C	60–74	Nurture, no rep time yet

The full 100-pt rubric, negative scoring, score decay, the tier→SLA map, and the scored-list CSV schema → references/scoring-model.md.

Handoff packet (what leaves this skill): the tiered CSV, the ICP + persona it was built against, per-row provenance, and the scoring rationale for the A tier. Nothing more — no message, no pipeline stage.

Compliance gate — run BEFORE handoff

A list that ships without these fields is not done. Run both checklists; the strictest applicable jurisdiction wins.

GDPR (EU B2B). Cold B2B email runs on legitimate interest, Art. 6(1)(f) — not consent — but only if you have done the paperwork. (derrick-app.com / instantly.ai GDPR-B2B guides, accessed 2026-06-02.)

• A documented Legitimate Interest Assessment (LIA) exists.
• Every email will carry the data-source disclosure + a privacy-policy link + a one-click opt-out.
• Objections will be honored.
• No purchased or scraped data — those confer no lawful basis, full stop.

CAN-SPAM (US). (ftc.gov CAN-SPAM compliance guide, accessed 2026-06-02.)

• A valid physical postal address is available for the footer.
• A clear opt-out mechanism, honored within 10 business days, live ≥30 days.
• Penalty awareness: up to $53,088 per violating email, FTC-enforced.

To lint a produced list file for the required columns, score-range sanity, provenance presence, and a compliance flag, run scripts/verify.sh path/to/list.csv (read-only).

Anti-patterns

Anti-pattern	Why it fails	Do instead
Buying/scraping a list and emailing it	No GDPR lawful basis; CAN-SPAM exposure up to $53,088/email	Source + verify + document the LIA + provenance per row
Scoring on intent alone	Intent without fit is noise — surge ≠ buyer	Composite fit+intent+engagement; fit gates the tier
One ICP/framework for every deal size	BANT on a MEDDIC deal qualifies on the wrong signals	Pick the framework by deal size/cycle first
Skipping email verification	78–84% accuracy = bounces + domain reputation damage	Waterfall + a verify pass before handoff
Enriching before deduping against the CRM	You pay to re-source known accounts and risk touching live deals	Dedupe on domain/email first, enrich the survivors
Handing reps a raw, unsorted list	Reps work easy-to-reach names, not the right ones	Tier A/B/C with SLAs and an A-tier rationale
Treating the list as the goal	A list is not a pipeline and not a campaign	Hand A/B to cold-outreach, accepted leads to sales-pipeline

Handoff — where the list goes next

This skill stops at a scored, compliance-cleared list. From there:

• The A/B tiers + persona context go to ../cold-outreach/SKILL.md — that skill writes the message and the cadence; you do not.
• Accepted leads (worked and responsive) go to ../sales-pipeline/SKILL.md — that skill tracks stages, forecasts, and manages the deal; you do not.
• If the request is really "how big is this market / which segment?" with no named list, that is ../market-research/SKILL.md, not this skill.