Lead Gen

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.)

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.)

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.