Web Scraping for B2B Sales Enrichment: Build a Lead Research Pipeline (2026)
B2B sales enrichment means turning a bare company name or domain into a complete prospect record: employee count, tech stack, revenue signals, recent job postings, decision-maker names, and contact information.
Traditional data providers (ZoomInfo, Apollo) cost $10,000–$50,000/year. Web scraping builds the same enrichment pipeline for a fraction of the cost — and stays current automatically.
What Data Can You Enrich Via Scraping?
| Data Point | Source | Tool |
|---|---|---|
| Company description, industry | Company website | Apify/Firecrawl |
| Employee count | Apify LinkedIn Actor | |
| Tech stack | BuiltWith, Wappalyzer | API or scraper |
| Funding/valuation | Crunchbase | Apify Crunchbase Actor |
| Recent job postings | LinkedIn, Indeed | Apify Job Scraper |
| Decision-maker names/titles | Apify LinkedIn People Search | |
| Company email format | Hunter.io | Hunter API |
| Social proof (reviews) | G2, Capterra | Apify review scrapers |
| Google Maps rating | Google Maps | Apify Google Maps Actor |
Pipeline Architecture
[Input: domain/company list]
↓
[Step 1: Website scrape → description, location, phone]
↓
[Step 2: LinkedIn company page → employee count, industry, specialties]
↓
[Step 3: Job postings → growth signals, tech requirements]
↓
[Step 4: Crunchbase → funding round, investor data]
↓
[Step 5: AI enrichment → ICP score, personalized pitch angle]
↓
[Output: Enriched CRM record]
Step 1: Scrape Company Websites
import requests
def enrich_from_website(domain: str) -> dict:
"""Scrape company website for key data."""
resp = requests.post(
"https://api.firecrawl.dev/v1/scrape",
json={
"url": f"https://{domain}",
"formats": ["extract"],
"extract": {
"prompt": "Extract: company description (1-2 sentences), primary product/service, target customer, company location, contact email if visible.",
"schema": {
"type": "object",
"properties": {
"description": {"type": "string"},
"product": {"type": "string"},
"target_customer": {"type": "string"},
"location": {"type": "string"},
"email": {"type": "string"}
}
}
}
},
headers={"Authorization": f"Bearer {FIRECRAWL_KEY}"}
)
return resp.json().get("data", {}).get("extract", {})
Step 2: LinkedIn Company Enrichment via Apify
// Trigger Apify LinkedIn Scraper
const response = await fetch(
'https://api.apify.com/v2/acts/apify~linkedin-company-scraper/runs?fpr=use-apify',
{
method: 'POST',
headers: {
'Authorization': `Bearer ${APIFY_TOKEN}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
startUrls: companies.map(c => ({
url: `https://www.linkedin.com/company/${c.linkedinHandle}`
})),
proxy: { useApifyProxy: true }
})
}
);
// Fetch results — dataset items include:
// employeeCount, industry, specialties, headquarters, description, websiteUrl
Step 3: Job Posting Signals
Job postings are one of the strongest buying signals in B2B sales:
- Hiring a
Head of Security→ ICP for security tools - Hiring
Data Engineers→ ICP for data infrastructure - Hiring
Account Executives→ growing sales team = budget to spend
const jobResponse = await fetch(
'https://api.apify.com/v2/acts/apify~linkedin-jobs-scraper/runs?fpr=use-apify',
{
method: 'POST',
headers: { 'Authorization': `Bearer ${APIFY_TOKEN}` },
body: JSON.stringify({
searchKeywords: 'data engineer',
searchLocation: 'San Francisco',
companyName: 'Target Company',
maxItems: 20,
})
}
);
Analyze job titles and descriptions to infer tech stack and growth stage.
Step 4: AI Scoring with Claude
Use Claude to score each enriched record against your ICP and generate a personalized pitch:
import anthropic
client = anthropic.Anthropic()
def score_and_pitch(enriched_record: dict) -> dict:
response = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=1000,
messages=[{
"role": "user",
"content": f"""
Company data: {enriched_record}
Our ICP: SaaS companies with 50-500 employees, growing data team,
headquartered in North America or Europe.
1. Score this company against our ICP (0-10)
2. Write a 2-sentence personalized email opening referencing specific data above
3. Identify the most relevant pain point we can address
Return JSON with fields: icp_score, email_opener, pain_point
"""
}],
)
return response.content[0].text
Step 5: Automate with Make.com
Use Make.com to orchestrate the full pipeline on autopilot:
[New row in Google Sheets]
→ [HTTP: Run Apify Website Scraper]
→ [HTTP: Run Apify LinkedIn Actor]
→ [Claude: Score + pitch]
→ [HTTP: Push to HubSpot/Salesforce]
→ [Slack: Alert sales rep]
Total cost per lead: ~$0.10–$0.30 using Apify + Claude + Make.com.
Cost Comparison
| Method | Setup Cost | Per Lead | Freshness |
|---|---|---|---|
| ZoomInfo | ~$15,000/year | $0 (covered) | Monthly |
| Apollo | $99–$249/month | ~$0.10–0.50 | Weekly |
| This pipeline | ~$50–$100/month | $0.10–0.30 | Real-time |
The scraping pipeline wins on freshness (scraped on demand) and total cost at scale.
Start with Apify's free tier → | Make.com free trial →
FAQ
The three highest-quality sources are: (1) Google Maps for local business discovery with address, phone, website, and ratings; (2) LinkedIn for decision-maker identification (name, title, company, contact signals); and (3) company websites for direct email addresses, technographic signals (job board postings, tech stack), and current news. Combining all three gives you the most complete B2B lead profile.
Scraping publicly visible business information (company name, phone, website, address from Google Maps and company websites) is widely practiced for B2B lead generation. LinkedIn's ToS restricts automated access; any LinkedIn data collection must respect rate limits and avoid storing personal data beyond its intended purpose. GDPR and CCPA require you to have a legitimate purpose for storing individual contact data. Always validate your use case with legal counsel for regulated industries.
LinkedIn does not expose email addresses through scraping — you can extract name, title, company, and profile URL. To get email addresses, enrich the LinkedIn profile against Apollo.io, Hunter.io, or similar email finders using the company domain as an input. Apify's Contact Details Scraper can also extract emails from company websites directly.
The total cost is approximately $0.10–$0.30 per lead using Apify (Google Maps + website scraping) + Claude API (scoring/pitch generation) + Make.com (automation). At 500 leads/month, that is $50–$150 total — significantly cheaper than ZoomInfo ($15K/year) or Apollo ($99–$249/month) with better data freshness since you scrape on demand.