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.
Installation
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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.
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.
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.
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
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.
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.
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 to avoid burning credits on unqualified leads.
Clay Pricing (2026)
Plan
Price/Mo
Credits/Mo
Per Credit
Free
$0
100
N/A
Starter
$149
2,000
$0.075
Explorer
$349
10,000
$0.035
Pro
$800
50,000
$0.016
Enterprise
Custom
Custom
Custom
Sample Clay Table Structure
Build your enrichment workflow in this column order:
Col A: Company Domain (input)
Col B: Contact Name (input or enrichment)
Col C: LinkedIn URL (Apollo waterfall)
Col D: Verified Email (email waterfall: Apollo > Hunter > FindyMail)
Col E: Job Title (Apollo or ZoomInfo)
Col F: Employee Count (Clearbit or Clay built-in)
Col G: Industry (Clearbit or Clay built-in)
Col H: Tech Stack (BuiltWith via Clay)
Col I: Bombora Surge Score (Bombora integration or manual import)
Col J: Firmographic Score (Formula: weighted average of F, G, geography)
Col K: Technographic Score (Formula: based on H match rules)
Col L: Intent Score (Formula: based on I + hiring + funding signals)
Col M: ICP Score (Formula: J*0.30 + K*0.30 + L*0.40)
Col N: AI Personalization (AI column: generate first-line based on B, E, H)
Col O: Routing (Formula: if M > 85 then "hot" elif M > 70 then "warm")
Credit Governance Rules
Pre-qualify before enriching - domain check + firmographic filter before spending on email waterfall
Cap per campaign - no single campaign burns more than 40% of monthly credits
Alert at 75% - Slack/email alert when usage crosses 75% of monthly allowance
Audit weekly - credits spent vs. leads enriched vs. leads qualified (target >60% qualification)
90-day re-enrichment - re-enrich stale contacts before including in new campaigns
Section 4: Contact Verification Pipeline
Unverified cold email lists carry 10-30% invalid addresses. Sending to bad addresses destroys sender reputation within a few campaigns. Google, Yahoo, and Microsoft now enforce bounce rates under 2% and spam complaints under 0.3%.
Send in small batches. Monitor bounce rate per batch.
0.50-0.69
Catch-all/Unverifiable
Segment separately. Maximum 50 per day. Watch closely.
<0.50
Invalid/High Risk
Reject. Do not send. Re-enrich with alternate provider.
Catch-All Domain Handling
Catch-all domains accept every email sent to them, even addresses that do not exist. They create silent deliverability decay because campaigns appear sent but never reach decision-makers.
Rules for catch-all addresses:
Never mix catch-all addresses into your primary sending pool
Send catch-all segments from a separate sending domain
Limit to 20-50 catch-all sends per domain per day
Track reply rates separately; if reply rate drops below 1%, stop sending to that domain
Re-verify catch-all addresses every 30 days
Verification Tool Comparison
Tool
Verification Method
Catch-All Detection
Bulk Speed
Pricing
MillionVerifier
SMTP + proprietary
Yes
1M/hour
$0.0005/email
ZeroBounce
SMTP + AI scoring
Yes
100K/hour
$0.008/email
NeverBounce
SMTP + real-time API
Yes
50K/hour
$0.008/email
DeBounce
SMTP + disposable detect
Yes
500K/hour
$0.001/email
Bouncer
SMTP + toxicity check
Yes
200K/hour
$0.005/email
Deliverability Protection Checklist
Before sending any enriched list to outreach:
All emails verified within the last 7 days
Bounce rate on verification under 2%
Catch-all addresses segmented into separate pool
Role accounts (info@, support@) removed or deprioritized
Sending domain has SPF, DKIM, and DMARC configured
Sending domain warmed for at least 14 days
Daily send volume does not exceed 50 per inbox per day (cold)
