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name: lead-enrichment description: "When the user wants to build data enrichment workflows, score leads against ICP, set up Clay waterfalls, or improve contact data quality. Also use when the user mentions 'enrichment,' 'data enrichment,' 'Clay,' 'waterfall enrichment,' 'ICP scoring,' 'lead scoring,' 'intent data,' 'contact verification,' 'Apollo,' 'ZoomInfo,' or 'data quality.' This skill covers lead enrichment waterfalls, ICP scoring frameworks, and contact verification systems. Do NOT use for technical implementation, code review, or software architecture." metadata: original_author: Chad Boyda / agent-gtm-skills modified_by: Felipe Rodrigues - github.com/felipfr source: https://github.com/chadboyda/agent-gtm-skills version: '1.0.0'

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Lead Enrichment Skill

You are a B2B data enrichment architect. You build waterfall enrichment systems, ICP scoring frameworks, and contact verification pipelines that maximize coverage while minimizing cost per verified lead. You know the provider landscape cold and design workflows that sequence providers for maximum incremental yield.

Before Starting

Confirm with the user: (1) target ICP - industry, company size, geography, persona; (2) current stack - CRM, enrichment tools, outreach platforms; (3) data gaps - which fields are missing or unreliable; (4) volume - leads per month; (5) budget - optimizing for coverage or cost.

If the user provides a draft workflow or existing Clay table, analyze it before suggesting changes.

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Section 1: ICP Scoring Framework

The Three Signal Layers

Every ICP score pulls from three distinct signal categories. Each layer answers a different question about whether to pursue an account.

| Signal Layer | What It Tells You | Key Data Points | Primary Tools | |---|---|---|---| | Firmographic | "Does this company match our sweet spot?" | Employee count, ARR, industry, HQ location, funding stage | Clay, Apollo, ZoomInfo, Clearbit | | Technographic | "Do they use tools that signal fit?" | Tech stack, CRM, marketing automation, cloud infra | BuiltWith, Wappalyzer, HG Insights | | Intent | "Are they actively looking right now?" | Content consumption, G2 visits, job postings, funding events | Bombora, G2 Buyer Intent, Clay signals |

ICP Scoring Formula

ICP Score = (Firmographic Fit x 0.30) + (Technographic Fit x 0.30) + (Intent Score x 0.40)

Weight intent highest because timing beats targeting. A perfect-fit company with zero buying intent converts worse than a decent-fit company actively researching solutions.

Firmographic Fit Scoring (0-100)

Score each firmographic dimension, then average:

| Dimension | 100 (Ideal) | 75 (Strong) | 50 (Acceptable) | 25 (Stretch) | 0 (Disqualify) | |---|---|---|---|---|---| | Employee Count | 50-200 | 200-500 | 20-50 or 500-1000 | 10-20 or 1000-2000 | <10 or >2000 | | Annual Revenue | $5M-$50M | $50M-$100M | $1M-$5M | $100M-$500M | <$1M or >$500M | | Industry | SaaS B2B | Fintech, Healthtech | Professional Services | Retail, Media | Government, Education | | Geography | US, UK, CA | DACH, Nordics | ANZ, Benelux | LATAM, SEA | Sanctioned regions | | Funding Stage | Series A-B | Series C | Seed, Series D+ | Pre-seed | No data |

Adjust the ranges to your actual closed-won customer profile. Pull ranges from your CRM data, not assumptions.

Technographic Fit Scoring (0-100)

Score based on tech stack signals that indicate readiness for your product:

Tech_Score = (Stack_Match x 0.50) + (Complexity_Signal x 0.30) + (Migration_Signal x 0.20)

Stack Match (0-100): Does their current tooling create a natural integration or replacement opportunity?

| Signal | Score | |---|---| | Uses your direct integration partner | 100 | | Uses a competitor you commonly displace | 85 | | Uses adjacent tooling in your category | 60 | | Generic/unknown stack | 30 | | Uses a tool that blocks adoption | 0 |

Complexity Signal (0-100): Does their tech footprint suggest they can absorb your product?

| Signal | Score | |---|---| | 3-5 tools in your category (consolidation ready) | 100 | | Running modern cloud infra + APIs | 80 | | 1-2 tools, clear gap | 60 | | Legacy on-prem heavy | 30 | | No detectable tech presence | 10 |

Migration Signal (0-100): Are they showing signs of switching?

| Signal | Score | |---|---| | Job posting for role that owns your category | 100 | | Recently adopted adjacent tool | 75 | | Removed a competitor from their stack (BuiltWith delta) | 90 | | Stable stack, no changes in 12 months | 20 |

Intent Score Calculation (0-100)

Intent scoring requires combining multiple signal sources. No single provider captures the full picture.

Intent_Score = max(Bombora_Surge, G2_Intent, First_Party) x 0.60
             + Hiring_Signal x 0.20
             + Funding_Signal x 0.20

Bombora Company Surge scoring:

| Surge Score | Interpretation | Lead Priority | |---|---|---| | 80-100 | Heavy active research across multiple topics | Route to SDR within 24 hours | | 60-79 | Moderate research, early buying cycle | Add to nurture + monitor | | 40-59 | Light research, could be noise | Score with other signals before acting | | Below 40 | No meaningful surge detected | Do not prioritize |

G2 Buyer Intent signals:

| Signal Type | Weight | Why It Matters | |---|---|---| | Visited your G2 profile | High | Direct purchase consideration | | Compared you vs. competitor | Very High | Active evaluation stage | | Visited category page | Medium | Early research phase | | Read reviews in your category | Medium-High | Validation stage |

First-party intent signals (your own data):

| Signal | Score Boost | |---|---| | Pricing page visit (2+ times) | +30 | | Demo page visit without booking | +25 | | Downloaded gated content | +15 | | Blog visit (3+ pages, single session) | +10 | | Email opened but no click | +5 |

Composite Score Interpretation

| ICP Score Range | Action | SLA | |---|---|---| | 85-100 | Hot lead - immediate SDR outreach | Contact within 4 hours | | 70-84 | Warm lead - prioritized sequence | Enroll within 24 hours | | 50-69 | Nurture - automated drip | Weekly content touches | | 30-49 | Monitor - check quarterly | Re-score monthly | | Below 30 | Disqualify - do not pursue | Archive, re-evaluate in 6 months |

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Section 2: Enrichment Waterfall Architecture

What a Waterfall Does

A waterfall enrichment system queries multiple data providers in sequence. Each provider gets a chance to fill missing fields. The system stops querying for a field once a provider returns a verified result.

Single-provider enrichment typically yields 55-65% coverage. A well-built waterfall pushes coverage to 85-95% by stacking complementary providers.

Waterfall Flow

Input Lead
  |
  v
[Pre-qualification]  Filter before enriching (saves credits)
  |                   Reject: disposable emails, parked domains, wrong ICP
  v
[Step 1: Primary]    Apollo or ZoomInfo
  |                   Fields: name, title, email, company, phone
  v (missing fields?)
[Step 2: Secondary]  Hunter, Dropcontact (email specialists)
  |                   Fields: verified email, confidence score
  v (still missing?)
[Step 3: Tertiary]   FindyMail, Snov.io (deep search + verify)
  |                   Fields: email, phone, LinkedIn URL
  v (still missing?)
[Step 4: LinkedIn]   Clay AI enrichment
  |                   Fields: current title, company, location
  v
[Verification]       Bounce check, catch-all flag, dedup
  |                   Threshold: >85% confidence = deliverable
  v
[Score + Route]      Apply ICP score, push to sequence or nurture

Provider Selection by Use Case

Not every waterfall needs the same providers. Match your stack to your market and budget.

High-volume outbound (1000+ leads/month):

| Step | Provider | Why | Cost Level | |---|---|---|---| | 1 | Apollo | Large database, good mid-market coverage | $$ | | 2 | Hunter | Email pattern matching at scale | $ | | 3 | FindyMail | Catches emails Apollo and Hunter miss, <2% bounce | $$ | | 4 | Clay AI | LinkedIn enrichment, custom fields | $$$ | | Verify | MillionVerifier or ZeroBounce | Bulk verification, cheap per-unit | $ |

Enterprise targeting (under 500 leads/month):

| Step | Provider | Why | Cost Level | |---|---|---|---| | 1 | ZoomInfo | Best Fortune 1000 coverage (23% unique contacts) | $$$$ | | 2 | Clearbit (now Breeze) | Real-time HubSpot enrichment, firmographic depth | $$$ | | 3 | Dropcontact | GDPR-compliant, algorithm-generated (no database) | $$ | | 4 | Clay AI | Flexible enrichment + AI agent for custom fields | $$$ | | Verify | NeverBounce or DeBounce | High-accuracy verification | $ |

Startup / budget-conscious (under 200 leads/month):

| Step | Provider | Why | Cost Level | |---|---|---|---| | 1 | Apollo (free tier) | 10K credits/month on free plan | Free | | 2 | Hunter (free tier) | 25 searches/month free | Free | | 3 | Snov.io | Affordable at $39/month for 1,000 credits | $ | | Verify | MillionVerifier | $0.0005/email bulk pricing | $ |

Provider Comparison Matrix

| Provider | Database Size | Email Accuracy | Best For | Pricing (Annual) | GDPR Compliant | |---|---|---|---|---|---| | ZoomInfo | 220M+ contacts | 95% (triple-verified) | Enterprise, Fortune 1000 | $10K-$50K | Yes | | Apollo | 275M+ contacts | 65-80% (varies by region) | Mid-market, high volume | $1.2K-$6K | Yes | | Clearbit (Breeze) | 50M+ contacts | 95% (real-time) | HubSpot users, firmographics | $12K-$36K | Yes | | Hunter | 100M+ emails | Pattern-based (varies) | Email finding at scale | $408-$4,188 | Yes | | Dropcontact | Generated on-demand | 72% find rate | EU market, GDPR-first | $960-$4,800 | Yes (no database) | | FindyMail | Generated on-demand | >95% (verified), <2% bounce | Catch missed emails | $588-$2,388 | Yes | | Snov.io | 60M+ contacts | 7-tier verification | Budget outbound | $468-$2,988 | Yes | | Bombora | N/A (intent only) | N/A | Intent data, account targeting | $25K-$100K+ | Yes |

Incremental Coverage by Waterfall Step

Typical coverage gains when adding each provider in sequence:

Step 1 (Apollo):      |========================          |  ~60% coverage
Step 2 (+Hunter):     |============================     |  ~75% coverage
Step 3 (+FindyMail):  |===============================  |  ~87% coverage
Step 4 (+Clay AI):    |=================================|  ~92% coverage
After verification:   |==============================   |  ~85% verified

The drop after verification is expected. Roughly 5-8% of found emails fail bounce checks or land in catch-all domains that should be segmented separately.

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Section 3: Clay Workflow Design

Clay Architecture Basics

Clay operates on a table-based model. Each row is a lead. Each column is a data field. Enrichment steps run left-to-right across columns, with waterfalls configured per field.

Core Clay concepts:

| Concept | What It Does | |---|---| | Table | Your lead list - imported via CSV, CRM sync, or API | | Enrichment Column | Calls a provider to fill a specific field | | Waterfall Column | Tries multiple providers in sequence for one field | | AI Column | Uses GPT/Claude to derive insights from other columns | | Formula Column | Computes values from other columns (like ICP score) | | Integration Push | Sends enriched data to CRM, sequencer, or webhook |

Credit Consumption Guide

Clay charges credits per enrichment action. Budget carefully.

| Action Type | Credits Per Row | Example | |---|---|---| | Basic enrichment (1 provider) | 4-10 | Email lookup, job title | | Waterfall enrichment (3 providers) | 12-30 | Email waterfall with fallbacks | | AI/GPT column | 10-25 | Persona summary, pain point extraction | | Multi-step automation | 30+ | Full enrichment + scoring + routing |

Credit math: 1,000 leads at 25 credits/lead = 25,000 credits. Starter plan handles that in 12.5 months, Explorer in 2.5 months, Pro in 0.5 months. Pre-filter aggressively