Lead Generation Expert Knowledge

Ideal Customer Profile (ICP) Construction

A good ICP answers these questions:

• Industry: What vertical does your ideal customer operate in?

• Company size: How many employees? What revenue range?

• Geography: Where are they located?

• Technology: What tech stack do they use?

• Budget signals: Are they funded? Growing? Hiring?

• Decision-maker: Who has buying authority? (title, seniority)

• Pain points: What problems does your product solve for them?

Company Size Categories

Category Employees Typical Budget Sales Cycle

Startup 1-50 $1K-$25K/yr 1-4 weeks

SMB 50-500 $25K-$250K/yr 1-3 months

Enterprise 500+ $250K+/yr 3-12 months

Web Research Techniques for Lead Discovery

Search Query Patterns

Find companies in a vertical

"[industry] companies" site:crunchbase.com "top [industry] startups [year]" "[industry] companies [city/region]"

Find decision-makers

"[title]" "[company]" site:linkedin.com "[company] team" OR "[company] about us" OR "[company] leadership"

Growth signals (high-intent leads)

"[company] hiring [role]" — indicates budget and growth "[company] series [A/B/C]" — recently funded "[company] expansion" OR "[company] new office" "[company] product launch [year]"

Technology signals

"[company] uses [technology]" OR "[company] built with [technology]" site:stackshare.io "[company]" site:builtwith.com "[company]"

Source Quality Ranking

• Company website (About/Team pages) — most reliable for personnel

• Crunchbase — funding, company details, leadership

• LinkedIn (public profiles) — titles, tenure, connections

• Press releases — announcements, partnerships, funding

• Job boards — hiring signals, tech stack requirements

• Industry directories — comprehensive company lists

• News articles — recent activity, reputation

• Social media — engagement, company culture

Lead Enrichment Patterns

Basic Enrichment (always available)

• Full name (first + last)

• Job title

• Company name

• Company website URL

Standard Enrichment

• Company employee count (from About page, Crunchbase, or LinkedIn)

• Company industry classification

• Company founding year

• Technology stack (from job postings, StackShare, BuiltWith)

• Social profiles (LinkedIn URL, Twitter handle)

• Company description (from meta tags or About page)

Deep Enrichment

• Recent funding rounds (amount, investors, date)

• Recent news mentions (last 90 days)

• Key competitors

• Estimated revenue range

• Recent job postings (growth signals)

• Company blog/content activity (engagement level)

• Executive team changes

Email Pattern Discovery

Common corporate email formats (try in order):

• firstname@company.com (most common for small companies)

• firstname.lastname@company.com (most common for larger companies)

• first_initial+lastname@company.com (e.g., jsmith@)

• firstname+last_initial@company.com (e.g., johns@)

Note: NEVER send unsolicited emails. Email patterns are for reference only.

Lead Scoring Framework

Scoring Rubric (0-100)

ICP Match (30 points max): Industry match: +10 Company size match: +5 Geography match: +5 Role/title match: +10

Growth Signals (20 points max): Recent funding: +8 Actively hiring: +6 Product launch: +3 Press coverage: +3

Enrichment Quality (20 points max): Email found: +5 LinkedIn found: +5 Full company data: +5 Tech stack known: +5

Recency (15 points max): Active this month: +15 Active this quarter:+10 Active this year: +5 No recent activity: +0

Accessibility (15 points max): Direct contact: +15 Company contact: +10 Social only: +5 No contact info: +0

Score Interpretation

Score Grade Action

80-100 A Hot lead — prioritize outreach

60-79 B Warm lead — nurture

40-59 C Cool lead — enrich further

0-39 D Cold lead — deprioritize

Deduplication Strategies

Matching Algorithm

• Exact match: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name

• Fuzzy match: Levenshtein distance < 2 on company name + same person

• Domain match: Same company website domain = same company

• Cross-source merge: Same person at same company from different sources → merge enrichment data

Normalization Rules

Company name:

• Strip legal suffixes: Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA
• Lowercase
• Remove "The" prefix
• Collapse whitespace

Person name:

• Lowercase
• Remove middle names/initials
• Handle "Bob" = "Robert", "Mike" = "Michael" (common nicknames)

Output Format Templates

CSV Format

Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes "Jane Smith","VP Engineering","Acme Corp","https://acme.com","https://linkedin.com/in/janesmith","SaaS","SMB (120 employees)",85,"2025-01-15","Series B funded, hiring 5 engineers"

JSON Format

[ { "name": "Jane Smith", "title": "VP Engineering", "company": "Acme Corp", "company_url": "https://acme.com", "linkedin": "https://linkedin.com/in/janesmith", "industry": "SaaS", "company_size": "SMB", "employee_count": 120, "score": 85, "discovered": "2025-01-15", "enrichment": { "funding": "Series B, $15M", "hiring": true, "tech_stack": ["React", "Python", "AWS"], "recent_news": "Launched enterprise plan Q4 2024" }, "notes": "Strong ICP match, actively growing" } ]

Markdown Table Format

Compliance & Ethics

DO

• Use only publicly available information

• Respect robots.txt and rate limits

• Include data provenance (where each piece of info came from)

• Allow users to export and delete their lead data

• Clearly mark confidence levels on enriched data

DO NOT

• Scrape behind login walls or paywalls

• Fabricate any lead data (even "likely" email addresses without evidence)

• Store sensitive personal data (SSN, financial info, health data)

• Send unsolicited communications on behalf of the user

• Bypass anti-scraping measures (CAPTCHAs, rate limits)

• Collect data on individuals who have opted out of data collection

Data Retention

• Keep lead data in local files only — never exfiltrate

• Mark stale leads (>90 days without activity) for review

• Provide clear data export in all supported formats