data-enrichment

name	data-enrichment
description	Design data enrichment pipelines that augment first-party data with external sources. Outputs enrichment strategy, provider evaluation, pipeline design, match rate optimisation, and quality controls.
argument-hint	["data types to enrich","use cases","budget","privacy requirements","existing data assets"]
allowed-tools	Read, Write

Data Enrichment

Data enrichment adds context to your first-party data using external sources. A customer record enriched with firmographic data (company size, industry) enables better segmentation, scoring, and personalisation. The challenge is match rates, data quality, freshness, and cost.

Enrichment Use Cases

FIRMOGRAPHIC (B2B)
  Sources: Clearbit, ZoomInfo, Apollo, LinkedIn, Crunchbase
  Adds: company_size, industry, revenue_range, funding_stage, tech_stack
  Use: Lead scoring, ICP matching, tier assignment

DEMOGRAPHIC (B2C)
  Sources: Experian, Acxiom, first-party surveys
  Adds: age_range, income_bracket, household_size, location_type
  Use: Personalisation, product recommendations

BEHAVIOURAL ENRICHMENT
  Sources: Intent data providers (Bombora, G2), review sites
  Adds: in_market_signals, competitor_usage, buying_intent
  Use: Sales prioritisation, timing of outreach

GEOGRAPHIC
  Sources: Google Maps API, MaxMind, IP geolocation
  Adds: timezone, metro_area, country, region, lat/lng
  Use: Regional pricing, localisation, compliance

TECHNOGRAPHIC
  Sources: BuiltWith, Wappalyzer, SimilarTech
  Adds: tech_stack, cms, ecommerce_platform, analytics_tools
  Use: Integration prioritisation, competitive intelligence

Enrichment Pipeline

import httpx
from pydantic import BaseModel
from typing import Optional
import asyncio

class ClearbitEnrichment(BaseModel):
    company_name: Optional[str] = None
    company_domain: Optional[str] = None
    company_size: Optional[str] = None      # "1-10", "11-50", "51-200", etc.
    industry: Optional[str] = None
    country: Optional[str] = None
    funding_stage: Optional[str] = None
    annual_revenue_range: Optional[str] = None
    linkedin_url: Optional[str] = None
    enriched_at: Optional[str] = None
    match_confidence: Optional[float] = None

class DataEnricher:
    def __init__(self, clearbit_api_key: str):
        self.client = httpx.AsyncClient(
            base_url="https://company.clearbit.com/v2",
            headers={"Authorization": },
            timeout=,
        )
    
      () -> ClearbitEnrichment:
        :
            resp =  .client.get(
                ,
                params={: email},
            )
             resp.status_code == :
                data = resp.json()
                company = data.get(, {})
                 ClearbitEnrichment(
                    company_name=company.get(),
                    company_domain=company.get(),
                    company_size=company.get(, {}).get(),
                    industry=company.get(, {}).get(),
                    country=company.get(, {}).get(),
                    funding_stage=company.get(, {}).get(),
                    enriched_at=datetime.utcnow().isoformat(),
                    match_confidence=,
                )
             resp.status_code == :
                 ClearbitEnrichment(match_confidence=)
             resp.status_code == :
                
                 ClearbitEnrichment(match_confidence=)
         httpx.TimeoutException:
             ClearbitEnrichment(match_confidence=)
    
      () -> [, ClearbitEnrichment]:
        semaphore = asyncio.Semaphore()
          ():
              semaphore:
                result =  .enrich_by_email(email)
                 asyncio.sleep()  
                 email, result
        
        results =  asyncio.gather(*[enrich_one(e)  e  emails])
         (results)

Match Rate Optimisation

-- Measure enrichment match rates
SELECT
    DATE_TRUNC('week', enriched_at) AS week,
    COUNT(*) AS total_records,
    SUM(CASE WHEN match_confidence > 0 THEN 1 ELSE 0 END) AS matched,
    SUM(CASE WHEN match_confidence IS NULL THEN 1 ELSE 0 END) AS pending,
    ROUND(100.0 * SUM(CASE WHEN match_confidence > 0 THEN 1 ELSE 0 END)
          / COUNT(*), 1) AS match_rate,
    AVG(CASE WHEN match_confidence > 0 THEN match_confidence END) AS avg_confidence
FROM customer_enrichment
GROUP  
   ;



 email, created_at
 customers
 enriched_at  
   email   
   email   
   email   
  created_at 
LIMIT ;

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
Enriching without consent	GDPR violation in EU	Review legal basis; document legitimate interest
Storing enriched PII without TTL	Data minimisation violation	Enrich at point of use; or set retention policy
Single provider dependency	Provider outage or price increase	Multi-provider strategy with fallback
No match confidence tracking	Low-quality matches corrupt downstream models	Track confidence; use threshold for scoring models
Enriching all records	Cost waste on inactive accounts	Prioritise high-value or recently active accounts

10 Rules

1. Legal basis for enrichment must be documented — GDPR legitimate interest or consent.
2. Match confidence is a first-class metric — low-confidence enrichment degrades model quality.
3. Enrich on demand or at point of use — not for all records by default.
4. Prioritise enrichment budget on high-value segments — not uniform across all customers.
5. Multi-provider waterfall: try primary, fall back to secondary for misses.
6. Freshness matters — firmographic data changes; re-enrich key accounts quarterly.
7. Store raw enriched data separate from derived attributes — enables reprocessing.
8. Track enrichment coverage by segment — "we have company_size for 70% of enterprise accounts".
9. Personal email domains (gmail, yahoo) match poorly — focus on business emails.
10. Enrichment is a complement to first-party data — never more trusted than your own signals.