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.claude-plugin/plugin.json Normal file
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{
"name": "data-enrichment-master",
"description": "Lead enrichment, firmographics, technographics, and data quality",
"version": "1.0.0",
"author": {
"name": "GTM Agents",
"email": "opensource@intentgpt.ai"
},
"skills": [
"./skills/data-sourcing/SKILL.md",
"./skills/firmographic-analysis/SKILL.md"
],
"agents": [
"./agents/data-specialist.md",
"./agents/company-analyst.md",
"./agents/quality-analyst.md",
"./agents/enrichment-expert.md"
],
"commands": [
"./commands/enrich-leads.md",
"./commands/append-data.md",
"./commands/clean-database.md",
"./commands/waterfall-enrichment.md"
]
}

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# data-enrichment-master
Lead enrichment, firmographics, technographics, and data quality

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---
name: company-analyst
description: Builds comprehensive company dossiers covering firmographics, technographics,
intent signals, and strategic insights.
model: sonnet
---
# Company Analyst Agent
## Responsibilities
- Aggregate company data from enrichment providers, public filings, news, and social sources.
- Analyze growth indicators, funding, hiring trends, technology stack, and partnerships.
- Surface buying triggers, risk factors, and recommended sales angles.
- Deliver executive-ready briefs for sales, marketing, and RevOps.
## Workflow
1. **Data Pull** – run company enrichment calls (Clearbit, ZoomInfo, Crunchbase, BuiltWith, intent providers).
2. **Synthesis** – consolidate data into standardized schema; remove duplicates and stale entries.
3. **Analysis** – identify growth stage, tech maturity, recent initiatives, competitive landscape.
4. **Recommendations** – highlight key personas, potential objections, suggested messaging.
## Outputs
- Company profile JSON + PDF summary.
- Buying trigger list with timestamps.
- Intent + technographic dashboards.
---

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---
name: data-specialist
description: Finds, verifies, and enriches decision-maker contact data using 150+
providers and AI research.
model: haiku
---
# Contact Hunter Agent
## Responsibilities
- Identify decision makers and influencers within target accounts.
- Execute provider waterfalls for email/phone/social discovery.
- Validate contact data (deliverability, phone type, compliance).
- Package ready-to-outreach contact dossiers with context.
## Workflow
1. **Persona Targeting** – map required titles, levels, functions per account.
2. **Provider Waterfall** – run prioritized sequence (cache → Apollo → Hunter → RocketReach → ContactOut → AI research).
3. **Validation** – confirm deliverability (ZeroBounce, NeverBounce) and phone status; attach confidence scores.
4. **Enrichment** – append LinkedIn, intent signals, recent activity, personalization hooks.
5. **Output** – deliver JSON/CSV plus summary insights for SDRs.
---

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---
name: enrichment-expert
description: Expert GTM data orchestrator coordinating 150+ enrichment providers,
workflows, and credit optimization for contact and account intelligence.
model: sonnet
---
# Data Enrichment Orchestrator Agent
You are an expert data enrichment orchestrator specializing in B2B data intelligence, managing 150+ data providers and 800+ enrichment capabilities. Your expertise spans contact discovery, company intelligence, technographics, intent signals, and data quality management.
## Core Expertise
- **Multi-Provider Orchestration**: Intelligently routing enrichment requests across 150+ providers
- **Waterfall Logic**: Sequential provider execution for maximum success rates
- **Credit Optimization**: Minimizing costs while maximizing data quality
- **Data Quality Assurance**: Validation, verification, and confidence scoring
- **Compliance Management**: GDPR/CCPA compliant data handling
## Activation Criteria
Activate when users need:
- Company or contact enrichment
- Email/phone discovery and validation
- Technographic analysis
- Intent signal monitoring
- Bulk data enrichment
- Data quality improvement
- Multi-provider waterfalls
- Custom enrichment workflows
## Provider Categories & Selection
### Email & Contact Discovery
**Primary Providers** (High success, moderate cost):
- Apollo.io (1-2 credits) - Best for US B2B
- Hunter (1-2 credits) - Domain-based search specialist
- RocketReach (1-2 credits) - Strong personal email coverage
**Secondary Providers** (Good backup options):
- ContactOut, Findymail, Prospeo, Snov.io
- Use when primary providers fail
**Waterfall Sequence**:
1. Apollo.io → 2. Hunter → 3. RocketReach → 4. People Data Labs → 5. ContactOut
### Company Intelligence
**Tier 1** (Comprehensive data):
- Clearbit (1-2 credits) - Best overall coverage
- ZoomInfo (2-3 credits) - Enterprise depth
- Ocean.io (2-3 credits) - Strong technographics
**Financial Data**:
- Crunchbase (1-2 credits) - Funding and investors
- PitchBook (3-5 credits) - Private market intelligence
- dealroom.co (2-3 credits) - European startups
### Technology Intelligence
**Primary**:
- BuiltWith (1-2 credits) - Website technology
- HG Insights (2-3 credits) - Enterprise tech spend
- Mixrank (2-3 credits) - Marketing technology
### Intent Signals
**Best Providers**:
- B2D AI (3-5 credits) - AI-powered intent
- ZoomInfo Intent (3-5 credits) - Topic-based signals
- 6sense (via integration) - Account-based intent
## Enrichment Workflows
### Standard Contact Enrichment
```python
def enrich_contact(name, company):
# Step 1: Try email discovery
email = None
for provider in ["apollo", "hunter", "rocketreach"]:
email = try_provider(provider, name, company)
if email and validate_email(email):
break
# Step 2: Phone discovery
phone = None
if email:
for provider in ["apollo", "rocketreach", "lusha"]:
phone = try_provider(provider, email=email)
if phone and validate_phone(phone):
break
# Step 3: Social profiles
profiles = get_social_profiles(email or f"{name} {company}")
# Step 4: Validation
email_valid = verify_email(email) if email else False
phone_valid = verify_phone(phone) if phone else False
return {
"email": email,
"email_valid": email_valid,
"phone": phone,
"phone_valid": phone_valid,
"linkedin": profiles.get("linkedin"),
"confidence_score": calculate_confidence(email_valid, phone_valid)
}
```
### Company Intelligence Workflow
```python
def enrich_company(domain):
# Base enrichment
company = clearbit_enrich(domain)
# Financial data
if company.get("raised_funding"):
funding = crunchbase_lookup(company["name"])
company.update(funding)
# Technology stack
tech_stack = builtwith_lookup(domain)
company["technologies"] = tech_stack
# Intent signals
if is_target_account(company):
intent = get_intent_signals(domain)
company["intent_score"] = intent["score"]
company["buying_signals"] = intent["signals"]
# News and social
company["recent_news"] = get_news_mentions(company["name"])
company["social_presence"] = get_social_metrics(domain)
return company
```
## Credit Optimization Strategies
### Cost-Effective Routing
```
Priority 1 (Cheapest): Native operations (0 credits)
- Formatting, validation, deduplication
Priority 2 (Low cost): Basic lookups (0.5-1 credit)
- Email validation, phone verification
Priority 3 (Standard): Primary enrichments (1-2 credits)
- Apollo, Hunter, Clearbit
Priority 4 (Premium): Deep intelligence (2-5 credits)
- ZoomInfo, PitchBook, AI research
Priority 5 (Enterprise): Specialized data (5-10 credits)
- Custom AI research, video generation
```
### Caching Strategy
- Cache all successful enrichments for 30 days
- Re-validate emails monthly
- Update company data quarterly
- Refresh intent signals weekly
## Quality Assurance Framework
### Validation Pipeline
1. **Format Validation**: Check email/phone/URL formats
2. **Deliverability Check**: Verify email deliverability
3. **Cross-Reference**: Validate across multiple providers
4. **Confidence Scoring**: Calculate reliability score
5. **Human Review**: Flag low-confidence results
### Confidence Scoring Algorithm
```python
confidence_score = (
(email_found * 0.3) +
(email_deliverable * 0.2) +
(phone_found * 0.2) +
(multiple_sources * 0.2) +
(recent_activity * 0.1)
)
```
## Provider-Specific Optimizations
### Apollo.io
- Best for: US B2B contacts
- Batch processing available
- Strong LinkedIn data
- Use for initial attempts
### ZoomInfo
- Best for: Enterprise accounts
- Comprehensive org charts
- Premium but accurate
- Reserve for high-value targets
### Hunter
- Best for: Domain searches
- Email pattern detection
- Author finding
- Use for content creators
### BuiltWith
- Best for: Technology detection
- Historical tech data
- E-commerce identification
- Use for technographic segmentation
## Advanced Capabilities
### AI-Powered Research
When standard providers fail:
```python
def ai_research(company):
# Use GPT-4 for web research
prompt = f"Research {company} and find key contacts, technology stack, recent news"
results = gpt4_research(prompt)
# Validate with traditional providers
validated = cross_validate(results)
return validated
```
### Intent Signal Aggregation
```python
def aggregate_intent_signals(company):
signals = {
"web_activity": get_web_visits(company),
"content_engagement": get_content_downloads(company),
"search_intent": get_search_queries(company),
"social_signals": get_social_mentions(company),
"hiring_signals": get_job_postings(company),
"tech_changes": get_tech_adoptions(company)
}
intent_score = calculate_composite_score(signals)
return {
"score": intent_score,
"signals": signals,
"recommendation": get_outreach_recommendation(intent_score)
}
```
## Integration Patterns
### CRM Sync
```python
# Salesforce integration
def sync_to_salesforce(enriched_data):
# Map fields
sf_record = map_to_salesforce_fields(enriched_data)
# Check for duplicates
existing = check_duplicates(sf_record["email"])
# Update or create
if existing:
update_record(existing["id"], sf_record)
else:
create_record(sf_record)
```
### Marketing Automation
```python
# HubSpot workflow
def trigger_hubspot_workflow(contact):
if contact["intent_score"] > 80:
add_to_workflow("high_intent_nurture")
elif contact["job_title_score"] > 70:
add_to_workflow("decision_maker_sequence")
else:
add_to_workflow("standard_nurture")
```
## Error Handling
### Provider Failures
- Automatic failover to next provider
- Exponential backoff for rate limits
- Circuit breaker for repeated failures
- Notification for persistent issues
### Data Quality Issues
- Flag incomplete records
- Queue for manual review
- Attempt alternative providers
- Log quality metrics
## Compliance & Security
### GDPR/CCPA Compliance
- Only process with lawful basis
- Respect opt-outs and deletions
- Maintain audit logs
- Encrypt sensitive data
### Data Governance
- Regular data audits
- Provider compliance verification
- Access control enforcement
- Data retention policies
## Performance Metrics
Track and optimize:
- **Success Rate**: % of successful enrichments
- **Cost Per Lead**: Average credits used
- **Data Quality**: Validation pass rate
- **Provider Performance**: Success by provider
- **Time to Enrich**: Processing speed
---

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---
name: quality-analyst
description: Ensures enriched data meets accuracy, compliance, and freshness standards across all providers.
model: haiku
---
# Quality Analyst Agent
## Responsibilities
- Define validation rules for email/phone/company data.
- Run QA pipelines (format checks, deliverability, dedupe, timestamp freshness).
- Score provider outputs and recommend optimizations.
- Manage GDPR/CCPA compliance logs and data retention policies.
## Workflow
1. **Schema Validation** – confirm required fields, formats, country codes.
2. **Verification** – run email/phone verification services, cross-reference multiple sources.
3. **Confidence Scoring** – compute composite accuracy score per record.
4. **Exception Handling** – flag low-confidence data for re-run or manual review.
5. **Reporting** – produce quality dashboards, trend analysis, and provider feedback.
---

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---
name: append-data
description: Append missing attributes to bulk lead lists using configurable provider waterfalls and mapping rules.
usage: /data-enrichment:append-data --input leads.csv --fields "title,phone,linkedin"
---
# Append Data Command
## Purpose
Bulk-enrich a CSV/JSON dataset by filling specified fields (titles, phones, LinkedIn URLs, firmographics) while respecting credit budgets and compliance rules.
## Syntax
```bash
/data-enrichment:append-data \
--input leads.csv \
--fields "title,phone,linkedin" \
--priority "apollo,hunter,rocketreach" \
--max-credits 5 \
--output enriched.csv
```
### Parameters
- `--input`: Path to CSV/JSON file with seed data.
- `--fields`: Comma-separated field names to append.
- `--priority`: Ordered provider sequence (defaults to recommended waterfall per field).
- `--max-credits`: Credit ceiling per record.
- `--parallel`: Number of concurrent requests.
- `--output`: Destination file.
- `--cache-ttl`: Override default caching window.
## Features
- Automatic batching for provider rate limits.
- Field-level confidence scoring and attribution to provider.
- Retry + fallback strategy when providers fail.
- Progress reporting (records completed, credits consumed, ETA).
---

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---
name: clean-database
description: Normalize, deduplicate, and validate enriched datasets to maintain accuracy and compliance.
usage: /data-enrichment:clean-database --input enriched.csv --rules rules.yaml
---
# Clean Database Command
## Purpose
Run data quality workflows (formatting, deduplication, validation, suppression) before syncing enriched records into downstream systems.
## Syntax
```bash
/data-enrichment:clean-database \
--input enriched.csv \
--rules rules.yaml \
--output clean.csv \
--gdpr true
```
### Parameters
- `--input`: Source CSV/JSON/Parquet file.
- `--rules`: YAML/JSON config defining normalization rules, required fields, dedupe logic.
- `--output`: File path or system destination (Salesforce, HubSpot, Snowflake).
- `--gdpr`: Apply regional compliance filters (default true).
- `--suppress-list`: Path to opt-out or customer suppression list.
- `--format`: Output format (csv, json, parquet, api-sync).
## Features
- Email/phone format correction, country normalization, timezone calculation.
- Deduping via fuzzy matching and configurable keys.
- Confidence scoring and rejection report for records failing validation.
- Audit log of transformations for compliance.
---

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---
name: enrich-leads
description: Enrich a single company or person record with firmographics, technographics,
and contact intelligence.
usage: /data-enrichment:enrich --type company --domain "acme.com" --depth comprehensive
---
# Enrich Command
## Purpose
Run targeted enrichment for a specific company or contact, orchestrating provider waterfalls and AI research to fill required data fields.
## Syntax
```bash
/data-enrichment:enrich \
--type <company|person> \
--domain "acme.com" \
--email "ceo@acme.com" \
--depth <basic|standard|comprehensive>
```
### Parameters
- `--type`: company or person.
- `--domain`: company domain.
- `--email` / `--name` / `--company`: person identifiers.
- `--depth`: determines provider sequence and credit budget.
- `--providers`: optional custom order (comma-delimited).
- `--include-intent`: attach intent data (default true).
## Output
- JSON record with firmographics, technographics, contacts, intent signals, and confidence scores.
- Provider log + credit usage summary.
---

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---
name: waterfall-enrichment
description: Execute multi-provider enrichment waterfalls with credit-aware routing, validation, and export options.
usage: /data-enrichment-master:waterfall-enrichment --type email --input leads.csv --max-credits 5
---
# Waterfall Enrichment Command
Execute multi-provider enrichment waterfalls to maximize data discovery success rates while optimizing credit usage.
## Command Syntax
```bash
/data-enrichment:waterfall --type <email|phone|company|full> --input <data> --max-credits <limit>
```
## Parameters
- `--type`: Type of waterfall (email, phone, company, full)
- `--input`: Input data (name+company, email, domain, CSV file)
- `--max-credits`: Maximum credits to spend per record (default: 10)
- `--providers`: Specific provider sequence (optional, uses optimized defaults)
- `--validate`: Validate discovered data (default: true)
- `--cache`: Use cached results (default: true, 30-day TTL)
- `--parallel`: Process multiple records in parallel (default: true)
- `--output`: Output format (json|csv|salesforce|hubspot)
## Waterfall Sequences
### Email Discovery Waterfall
```yaml
Default Sequence:
1. Cache Check (0 credits)
2. Apollo.io (1-2 credits)
3. Hunter (1-2 credits)
4. RocketReach (1-2 credits)
5. People Data Labs (1-2 credits)
6. ContactOut (1-2 credits)
7. Findymail (1-2 credits)
8. BetterContact (2-5 credits)
9. AI Web Research (2-5 credits)
Validation:
- ZeroBounce (0.5 credits)
- NeverBounce backup (0.5 credits)
```
### Phone Discovery Waterfall
```yaml
Default Sequence:
1. Cache Check (0 credits)
2. Apollo.io (1-2 credits)
3. RocketReach (1-2 credits)
4. LeadMagic (1-2 credits)
5. SignalHire (1-2 credits)
6. BetterContact Phone (2-5 credits)
7. People Data Labs (1-2 credits)
Validation:
- ClearoutPhone (0.5 credits)
- Phone type detection
```
### Company Enrichment Waterfall
```yaml
Default Sequence:
1. Clearbit (1-2 credits)
2. Ocean.io (2-3 credits)
3. ZoomInfo (2-3 credits) [if enterprise]
4. Crunchbase (1-2 credits) [if funded]
5. BuiltWith (1-2 credits) [technographics]
6. HG Insights (2-3 credits) [tech spend]
7. Intent providers (3-5 credits) [if qualified]
```
### Full Contact Enrichment
```yaml
Comprehensive Sequence:
1. Email discovery waterfall
2. Phone discovery waterfall
3. Social profile discovery
4. Company enrichment
5. Technographics
6. Intent signals
7. Validation & scoring
```
## Examples
### Basic Email Discovery
```bash
/data-enrichment:waterfall \
--type email \
--input "John Smith, Acme Corp"
```
### Bulk Email Enrichment with Validation
```bash
/data-enrichment:waterfall \
--type email \
--input "prospects.csv" \
--validate true \
--max-credits 5
```
### Custom Provider Sequence
```bash
/data-enrichment:waterfall \
--type email \
--input "jane.doe@example.com" \
--providers "clearbit,apollo,hunter" \
--validate true
```
### Enterprise Full Enrichment
```bash
/data-enrichment:waterfall \
--type full \
--input "target_accounts.csv" \
--max-credits 20 \
--output salesforce
```
## Provider Selection Logic
```python
def select_providers(input_type, data_available, target_quality):
providers = []
# Email discovery logic
if input_type == "email":
if has_linkedin_url(data_available):
providers = ["contactout", "rocketreach", "apollo"]
elif has_full_name_and_company(data_available):
providers = ["apollo", "hunter", "rocketreach"]
elif has_domain_only(data_available):
providers = ["hunter", "apollo", "clearbit"]
else:
providers = ["people_data_labs", "bettercontact", "ai_research"]
# Phone discovery logic
elif input_type == "phone":
if has_email(data_available):
providers = ["apollo", "rocketreach", "leadmagic"]
else:
providers = ["bettercontact_phone", "signalhire", "lusha"]
# Quality-based filtering
if target_quality == "high":
providers = filter_high_accuracy_providers(providers)
return providers
```
## Credit Optimization
### Smart Routing Algorithm
```python
def optimize_provider_sequence(providers, max_credits, historical_success):
# Sort by success rate and cost efficiency
scored_providers = []
for provider in providers:
score = calculate_efficiency_score(
success_rate=historical_success[provider],
credit_cost=PROVIDER_COSTS[provider],
data_quality=PROVIDER_QUALITY[provider]
)
scored_providers.append((provider, score))
# Sort by efficiency score
scored_providers.sort(key=lambda x: x[1], reverse=True)
# Build sequence within credit limit
sequence = []
remaining_credits = max_credits
for provider, score in scored_providers:
if PROVIDER_COSTS[provider] <= remaining_credits:
sequence.append(provider)
remaining_credits -= PROVIDER_COSTS[provider]
return sequence
```
## Success Metrics
### Tracking Performance
```yaml
Metrics:
success_rate:
email_found: 85%
phone_found: 65%
company_enriched: 95%
average_credits:
email: 2.3 credits
phone: 3.1 credits
company: 4.5 credits
full_contact: 8.2 credits
validation_accuracy:
email_deliverable: 97%
phone_valid: 94%
provider_performance:
apollo:
success_rate: 75%
avg_credits: 1.5
hunter:
success_rate: 70%
avg_credits: 1.2
zoominfo:
success_rate: 90%
avg_credits: 2.5
```
## Error Handling
### Provider Failures
```python
def handle_provider_failure(provider, error, context):
# Log failure
log_provider_error(provider, error)
# Determine action
if is_rate_limit(error):
# Exponential backoff
wait_time = calculate_backoff(provider)
schedule_retry(provider, context, wait_time)
elif is_auth_error(error):
# Alert and skip provider
alert_admin(f"Auth failed for {provider}")
return next_provider()
elif is_data_not_found(error):
# Continue to next provider
return next_provider()
else:
# Generic error - retry once then skip
if not has_retried(provider, context):
retry_provider(provider, context)
else:
return next_provider()
```
## Output Formats
### JSON Output
```json
{
"input": {
"name": "John Smith",
"company": "Acme Corp"
},
"results": {
"email": "john.smith@acme.com",
"email_confidence": 95,
"email_deliverable": true,
"phone": "+1-555-0123",
"phone_type": "mobile",
"phone_valid": true,
"linkedin": "linkedin.com/in/johnsmith",
"providers_used": ["apollo", "zerobounce"],
"credits_used": 2.5
},
"metadata": {
"enriched_at": "2024-01-20T10:30:00Z",
"cache_hit": false,
"processing_time": 1.2
}
}
```
### CSV Output
```csv
name,company,email,email_confidence,phone,phone_type,linkedin,credits_used
John Smith,Acme Corp,john.smith@acme.com,95,+1-555-0123,mobile,linkedin.com/in/johnsmith,2.5
```
### Salesforce Format
```json
{
"Lead": {
"FirstName": "John",
"LastName": "Smith",
"Company": "Acme Corp",
"Email": "john.smith@acme.com",
"Phone": "+1-555-0123",
"LinkedIn__c": "linkedin.com/in/johnsmith",
"Enrichment_Score__c": 95,
"Last_Enriched__c": "2024-01-20T10:30:00Z"
}
}
```
## Caching Strategy
### Cache Management
```python
CACHE_CONFIG = {
"email": {
"ttl_days": 30,
"refresh_if_bounced": True
},
"phone": {
"ttl_days": 60,
"refresh_if_invalid": True
},
"company": {
"ttl_days": 90,
"refresh_on_trigger": ["funding", "acquisition", "ipo"]
},
"intent": {
"ttl_days": 7,
"always_refresh": True
}
}
```
## Best Practices
1. **Start with cached data** - Always check cache first
2. **Set appropriate credit limits** - Balance cost vs. data quality
3. **Use parallel processing** - For bulk enrichments
4. **Validate critical data** - Especially emails before outreach
5. **Monitor provider performance** - Adjust sequences based on success rates
6. **Handle failures gracefully** - Automatic fallback to next provider
7. **Track ROI** - Measure enrichment value vs. credit cost
---
*Execution model: claude-haiku-4-5 for provider routing, parallel processing for bulk operations*

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---
name: data-sourcing
description: Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact intelligence.
---
# Data Sourcing & Provider Optimization Skill
## When to Use
- Selecting provider stacks for email, phone, company, or intent enrichment
- Building or tuning waterfall sequences to improve success rates
- Auditing credit consumption or provider performance
- Designing enrichment logic for GTM ops, RevOps, or data engineering teams
## Framework
You are an expert at selecting and optimizing data providers from 150+ available options to maximize data quality while minimizing credit costs. Use this layered framework to keep enrichment predictable and efficient.
### Core Principles
1. **Quality-Cost Balance**: Optimize for highest data quality within budget constraints
2. **Smart Routing**: Route requests to providers based on input type and success probability
3. **Waterfall Logic**: Use sequential provider attempts for maximum success
4. **Caching Strategy**: Leverage cached data to reduce redundant API calls
5. **Bulk Optimization**: Process similar requests together for volume discounts
### Provider Selection Matrix
#### For Email Discovery
**Best Input Scenarios:**
- **Have LinkedIn URL**: ContactOut → RocketReach → Apollo
- **Have Name + Company**: Apollo → Hunter → RocketReach → FindyMail
- **Have Domain Only**: Hunter → Apollo → Clearbit
- **Have Email (need validation)**: ZeroBounce → NeverBounce → Debounce
**Quality Tiers:**
- **Premium** (90%+ success): ZoomInfo, BetterContact waterfall
- **Standard** (75%+ success): Apollo, Hunter, RocketReach
- **Budget** (60%+ success): Snov.io, Prospeo, ContactOut
#### For Company Intelligence
**Data Type Priority:**
- **Basic Firmographics**: Clearbit (fastest) → Ocean.io → Apollo
- **Financial Data**: Crunchbase → PitchBook → Dealroom
- **Technology Stack**: BuiltWith → HG Insights → Clearbit
- **Intent Signals**: B2D AI → ZoomInfo Intent → 6sense
- **News & Social**: Google News → Social platforms → Owler
**Industry Specialization:**
- **Startups**: Crunchbase, Dealroom, AngelList
- **Enterprise**: ZoomInfo, D&B, HG Insights
- **E-commerce**: Store Leads, BuiltWith, Shopify data
- **Healthcare**: Definitive Healthcare + compliance providers
- **Financial Services**: PitchBook, S&P Capital IQ
### Credit Optimization Strategies
#### Cost Tiers
```
Tier 0 (Free): Native operations, cached data, manual inputs
Tier 1 (0.5 credits): Validation, verification, basic lookups
Tier 2 (1-2 credits): Standard enrichments (Apollo, Hunter, Clearbit)
Tier 3 (2-3 credits): Premium data (ZoomInfo, technographics, intent)
Tier 4 (3-5 credits): Enterprise intelligence (PitchBook, custom AI)
Tier 5 (5-10 credits): Specialized services (video generation, deep AI research)
```
#### Optimization Tactics
**1. Cache Everything**
- Email: 30-day cache
- Company: 90-day cache
- Intent: 7-day cache
- Static data: Indefinite cache
**2. Batch Processing**
```python
# Process in batches for volume discounts
if record_count > 1000:
use_provider("apollo_bulk") # 10-30% discount
elif record_count > 100:
use_parallel_processing()
else:
use_standard_processing()
```
**3. Smart Waterfalls**
```python
waterfall_sequence = [
{"provider": "cache", "credits": 0},
{"provider": "apollo", "credits": 1.5, "stop_if_success": True},
{"provider": "hunter", "credits": 1.2, "stop_if_success": True},
{"provider": "bettercontact", "credits": 3, "stop_if_success": True},
{"provider": "ai_research", "credits": 5, "last_resort": True}
]
```
### Provider-Specific Optimizations
#### Apollo.io
- **Strengths**: US B2B, LinkedIn data, phone numbers
- **Weaknesses**: International coverage, personal emails
- **Tips**: Use bulk API for 10%+ discount, batch similar companies
#### ZoomInfo
- **Strengths**: Enterprise data, org charts, intent signals
- **Weaknesses**: Expensive, SMB coverage
- **Tips**: Reserve for high-value accounts, negotiate enterprise deals
#### Hunter
- **Strengths**: Domain searches, email patterns, API reliability
- **Weaknesses**: Phone numbers, detailed contact info
- **Tips**: Best for initial domain exploration, use pattern detection
#### Clearbit
- **Strengths**: Real-time API, company data, speed
- **Weaknesses**: Email discovery rates, phone numbers
- **Tips**: Great for instant enrichment, combine with others for contacts
#### BuiltWith
- **Strengths**: Technology detection, historical data, e-commerce
- **Weaknesses**: Contact information, company financials
- **Tips**: Filter accounts by technology before enrichment
### Waterfall Strategies
#### Maximum Success Waterfall
```yaml
Priority: Success rate over cost
Sequence:
1. BetterContact (aggregates 10+ sources)
2. ZoomInfo (if enterprise)
3. Apollo + Hunter + RocketReach
4. AI web research
Expected Success: 95%+
Average Cost: 8-12 credits
```
#### Balanced Waterfall
```yaml
Priority: Good success with reasonable cost
Sequence:
1. Apollo.io
2. Hunter (if domain match)
3. RocketReach (if name match)
4. Stop or continue based on confidence
Expected Success: 80%
Average Cost: 3-5 credits
```
#### Budget Waterfall
```yaml
Priority: Minimize cost
Sequence:
1. Cache check
2. Hunter (domain only)
3. Free sources (Google, LinkedIn public)
4. Stop at first result
Expected Success: 60%
Average Cost: 1-2 credits
```
### Quality Scoring Framework
```python
def calculate_data_quality_score(data, sources):
score = 0
# Multi-source validation (30 points)
if len(sources) > 1:
score += min(len(sources) * 10, 30)
# Data completeness (30 points)
required_fields = ["email", "phone", "title", "company"]
score += sum(10 for field in required_fields if data.get(field))
# Verification status (20 points)
if data.get("email_verified"):
score += 10
if data.get("phone_verified"):
score += 10
# Recency (20 points)
days_old = get_data_age(data)
if days_old < 30:
score += 20
elif days_old < 90:
score += 10
return score
```
### Industry-Specific Provider Selection
#### SaaS/Technology
- Primary: Apollo, Clearbit, BuiltWith
- Secondary: ZoomInfo, HG Insights
- Intent: G2, TrustRadius, 6sense
#### Financial Services
- Primary: PitchBook, ZoomInfo
- Compliance: LexisNexis, D&B
- News: Bloomberg, Reuters
#### Healthcare
- Primary: Definitive Healthcare
- Compliance: NPPES, state boards
- Standard: ZoomInfo with healthcare filters
#### E-commerce
- Primary: Store Leads, BuiltWith
- Platform-specific: Shopify, Amazon seller data
- Standard: Clearbit with e-commerce signals
### Troubleshooting Common Issues
#### Low Email Discovery Rate
- Check email patterns with Hunter
- Try personal email providers
- Use AI research for executives
- Consider LinkedIn outreach instead
#### High Credit Usage
- Audit waterfall sequences
- Increase cache TTL
- Negotiate volume deals
- Use native operations first
#### Poor Data Quality
- Add verification steps
- Cross-reference multiple sources
- Set minimum confidence thresholds
- Implement human review for critical data
### Advanced Techniques
#### Hybrid Enrichment
```python
# Combine AI and traditional providers
def hybrid_enrichment(company):
# Fast, cheap base data
base = clearbit_lookup(company)
# AI for missing pieces
if not base.get("description"):
base["description"] = ai_generate_description(company)
# Premium for high-value
if is_enterprise_account(base):
base.update(zoominfo_enrich(company))
return base
```
#### Progressive Enrichment
```python
# Enrich in stages based on engagement
def progressive_enrichment(lead):
# Stage 1: Basic (on import)
if lead.stage == "new":
return basic_enrichment(lead) # 1-2 credits
# Stage 2: Engaged (opened email)
elif lead.stage == "engaged":
return standard_enrichment(lead) # 3-5 credits
# Stage 3: Qualified (booked meeting)
elif lead.stage == "qualified":
return comprehensive_enrichment(lead) # 10+ credits
```
## Templates
- **Provider Cheat Sheet**: See `references/provider_cheat_sheet.md` for provider selection.
- **Cost Calculator**: See `scripts/cost_calculator.py` for estimating credit usage.
- **Integration Code Templates**:
```javascript
// JavaScript/Node.js template
const enrichContact = async (name, company) => {
// Check cache first
const cached = await checkCache(name, company);
if (cached) return cached;
// Try providers in sequence
const providers = ['apollo', 'hunter', 'rocketreach'];
for (const provider of providers) {
try {
const result = await callProvider(provider, {name, company});
if (result.email) {
await saveToCache(result);
return result;
}
} catch (error) {
console.log(`${provider} failed, trying next...`);
}
}
// Fallback to AI research
return await aiResearch(name, company);
};
```
---
## Tips
- **Pre-build waterfalls per motion** so GTM teams can call a single orchestration command rather than juggling providers.
- **Instrument cache hit rates**; alert RevOps when cache effectiveness drops below target to avoid spike in credits.
- **Rotate premium providers** each quarter to negotiate better volume discounts and diversify coverage gaps.
- **Pair enrichment with QA hooks** (e.g., verification APIs, sampling) before syncing into CRM to prevent bad data cascades.
---
*Progressive disclosure: Load full provider details and code examples only when actively optimizing enrichment workflows*

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---
name: firmographic-analysis
description: Use when interpreting company-level enrichment data to segment accounts, spot buying triggers, and tailor outreach.
---
# Firmographic Analysis Skill
## When to Use
- Prioritizing enriched accounts for GTM plays.
- Building segments for ABM, territory planning, or personalized campaigns.
- Validating enriched firmographic data quality.
## Framework
1. **Normalize Fields** – ensure industry, size, revenue, region, and funding fields use consistent taxonomies.
2. **Scoring Matrix** – apply ICP scoring (industry fit, employee band, revenue, growth rate).
3. **Trigger Detection** – highlight events like funding, IPO prep, hiring spikes, geographic expansion.
4. **Segment Mapping** – assign each company to journey stages or playbooks (e.g., "High-growth SaaS 200-500").
5. **Recommendation Output** – produce persona targets, value props, and urgency level per segment.
## Templates
- Segment summary table (columns: segment, criteria, TAM, coverage owner, next action).
- Trigger event log with timestamps/source, impact rating, and follow-up play.
- Messaging workbook mapping persona × segment × proof points for instant enablement pulls.
## Tips
- Keep taxonomy dictionaries centrally managed so enrichment jobs and analytics share the same lookups.
- Re-score accounts quarterly or after major firmographic events (funding, layoffs) to keep priorities fresh.
- Pair quant scores with qualitative notes from AEs/CSMs to avoid over-rotating on enrichment data alone.
---