list-building

List Building

Overview

A qualified prospect list is the foundation of every outbound motion. Build it wrong and everything downstream fails — enrichment misses, verification flags half the list, and reps waste time on accounts that were never going to buy.

This skill covers building clean, qualified lists from multiple sources, applying ICP filters early (before expensive enrichment), and maintaining list hygiene. The core principle: qualify first, enrich deep later. Filtering out non-fits before running enrichment cuts costs by 30-40%.

When to Use

• "Build a list of VP Sales at Series B SaaS companies"
• "Find target accounts in the fintech space"
• "Create a prospect list from this conference attendee file"
• "Scrape local businesses from Google Maps for SMB outreach"
• "Build a target account list for our ABM campaign"

Authoritative Foundations

List building follows the Ziellab framework: filter first, enrich second. For phone-led outbound, add Joey Gilkey Phone Intent scoring after ICP filter — prioritize P1 contacts before expensive rep time. Pair pain-based segments (Jordan Crawford PQS) with disposition-driven list iteration (Gilkey Disposition Science). → references/joey-gilkey-bucketing.md

Clay's native table architecture supports this naturally — company enrichment runs first, ICP filters gate the contact enrichment, and only qualified accounts proceed to email and phone waterfalls.

Prerequisites

• ICP defined (use icp-scoring first if not yet defined)
• Source data: can start from a domain list, LinkedIn Sales Nav export, conference attendee list, Google Maps scrape, or industry directory
• Access to Clay or equivalent enrichment platform

Step-by-Step Process

Phase 1: Source Selection

Choose sources based on the target segment:

Source	Best For	Data Quality
LinkedIn Sales Nav	B2B, any industry	High — verified profiles
Apollo Search	US B2B, all sizes	High — 270M+ contacts
Claygent web search	Founders, execs, niche	Medium — web-sourced
Google Maps scrape	Local SMB	Medium — needs enrichment
Conference speaker lists	Industry leaders	Very high — curated
Crunchbase	Funded startups	High — funding-verified
GitHub	Dev tools, tech	High — public activity
Job boards (Lever, Greenhouse)	Growing companies	High — active hiring signal

Phase 2: Company-Level Enrichment

Before finding contacts, enrich company data:

1. Import domains into Clay
2. Run company enrichment (Clay native or Clearbit)
3. Apply ICP filters: company size, industry, geography, funding stage
4. Only companies passing ICP filters proceed to contact finding

This gate typically removes 40-60% of the initial list — those credits are saved on contact enrichment.

Phase 3: Contact Discovery

For qualified companies, find relevant contacts:

1. Define target titles and seniority
2. Run people search (Apollo, Clay People, or LeadMagic People Search)
3. Filter by title match and seniority
4. Target 2-5 contacts per account for multi-threading

Phase 4: List Quality Scorecard

Grade every list across 8 dimensions before uploading to a sequencer:

Dimension	Pass Criteria
Domain validity	All domains resolve
ICP fit	80%+ match defined ICP
Title relevance	70%+ match target titles
Email coverage	85%+ have verified email
Email verification	98%+ verified valid
Duplicate rate	Under 3%
Geographic fit	90%+ in target regions
Company size fit	80%+ in target range

Lists scoring below 70% across dimensions should be reworked before sending.

Output Format

Deliver a CSV with:

• Company domain, company name, industry, employee count
• Contact name, title, email, email verification status
• LinkedIn URL, source attribution
• ICP score and tier

Quality Check

• ICP filters applied before contact enrichment
• All emails verified (verification status column populated)
• Duplicates removed (match on email + company)
• List quality scorecard completed
• Companies source-attributed
• Separate company and person tables maintained (not merged)

Common Pitfalls

1. Enriching before filtering. Running $0.15-0.40/contact enrichment on records that fail ICP wastes 30-40% of budget. Filter first always.

2. One giant table. Combining company and person data in a single table creates duplication and makes re-enrichment impossible. Separate tables with domain as the join key.

3. No list quality baseline. Sending to an unscored list means you can't diagnose why reply rates are low. Score every list before launch.

4. Stale data acceptance. A list built 6 months ago has 12-18% decay. Re-verify and re-enrich before launching any campaign on old data.

5. Single-source dependency. One source never covers your full TAM. Cross-reference at least two sources and reconcile differences.

Execution Artifacts

• references/framework-notes.md — Named frameworks and reference tables
• templates/output-template.md — Deliverable shell for agent output
• scripts/check-output.py — Lightweight deliverable validator
• references/cold-calling-experts-index.md — Phone Intent + bucketing router (repo root)
• references/joey-gilkey-bucketing.md — Phone Intent + disposition list diagnostics (repo root)
• ../../outbound/cold-email-strategy/references/jordan-crawford-blueprint-gtm.md — PQS pain-based segments

Related Skills

• icp-scoring: Define the ICP criteria this skill filters against
• lead-finding: Find individual contacts at target companies
• lead-enrichment: Enrich contacts after list is built
• clay-automation: Full Clay workflow for automated list building
• sales-navigator-prospecting: Morgan Ingram filter-specific Sales Nav workflow after list criteria are set