lead-hand-skill

name	lead-hand-skill
version	1.0.0
description	Expert knowledge for AI lead generation — web research, enrichment, scoring, deduplication, and report generation
runtime	prompt_only

Lead Generation Expert Knowledge

Ideal Customer Profile (ICP) Construction

A good ICP answers these questions:

1. Industry: What vertical does your ideal customer operate in?
2. Company size: How many employees? What revenue range?
3. Geography: Where are they located?
4. Technology: What tech stack do they use?
5. Budget signals: Are they funded? Growing? Hiring?
6. Decision-maker: Who has buying authority? (title, seniority)
7. 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

ICP Refinement Loop

The ICP should not be static. After every 3 report cycles, refine it:

1. Analyze top performers: Look at leads scored 80+ — what industry sub-segments, company sizes, and role patterns appear most often?
2. Analyze low performers: Look at leads scored below 40 — which ICP criteria were they missing? Were there false positives from overly broad keywords?
3. Tighten criteria: Narrow industry keywords (e.g., "fintech" becomes "payment infrastructure fintech"), adjust company size range, add or remove geographic regions, refine role titles.
4. Track revisions: Log each ICP revision with date, changes made, and rationale. This creates an audit trail showing how targeting improved over time.
5. Measure impact: Compare average lead score before and after each ICP revision. A well-refined ICP should produce higher average scores with fewer total leads — quality over quantity.

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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

1. Company website (About/Team pages) — most reliable for personnel
2. Crunchbase — funding, company details, leadership
3. LinkedIn (public profiles) — titles, tenure, connections
4. Press releases — announcements, partnerships, funding
5. Job boards — hiring signals, tech stack requirements
6. Industry directories — comprehensive company lists
7. News articles — recent activity, reputation
8. Social media — engagement, company culture

Industry-Specific Search Patterns

SaaS / Technology

# Company directories
site:g2.com/products "[category]"
site:capterra.com "[category] software"
site:producthunt.com "[product type]" "[year]"
"[category] software" site:crunchbase.com/organization

# Tech stack signals
site:stackshare.io "[technology]" decisions
site:builtwith.com/websites/[technology]

# Growth signals
"[company] SOC 2" OR "[company] ISO 27001"        — enterprise readiness
"[company] API" OR "[company] integration"          — platform maturity
"[company] case study" OR "[company] customer story" — traction evidence

Healthcare

# Directories & registries
site:healthcareittoday.com "[company]"
"digital health companies" site:crunchbase.com
"health tech" "[city/state]" site:angellist.co
"HIPAA compliant" "[category] software"

# Regulatory signals
"[company] FDA clearance" OR "[company] 510(k)"
"[company] HIPAA" OR "[company] HITRUST"
"[company] clinical trial" site:clinicaltrials.gov

Financial Services

# Directories & databases
site:fintechmagazine.com "top" "[category]"
"fintech companies" "[region]" site:crunchbase.com
"banking technology" OR "insurtech" site:cbinsights.com

# Compliance signals
"[company] SOX compliance" OR "[company] PCI DSS"
"[company] banking license" OR "[company] money transmitter"
"[company] Series [A/B/C]" "fintech"

E-commerce

# Directories & tools
site:apps.shopify.com "[category]"
site:store.bigcommerce.com "[category]"
"ecommerce brands" "[niche]" site:2pm.com OR site:modernretail.co

# Revenue signals
"[company] GMV" OR "[company] ARR"
"[company] warehouse" OR "[company] fulfillment center"
"[brand] DTC" OR "[brand] direct to consumer"

Manufacturing

# Directories
site:thomasnet.com "[product category]"
"manufacturing companies" "[city/state]" site:mfg.com
"industrial [category]" site:dnb.com

# Modernization signals
"[company] Industry 4.0" OR "[company] smart factory"
"[company] ERP" OR "[company] digital transformation"
"[company] ISO 9001" OR "[company] ISO 14001"

Industry Source Quick Reference

Vertical	Primary Directories	Key Signal Keywords
SaaS/Tech	G2, Capterra, ProductHunt, Crunchbase	"API launch", "SOC 2", "Series X"
Healthcare	HealthcareIT, ClinicalTrials.gov	"HIPAA", "FDA", "clinical trial"
Financial Services	CBInsights, Crunchbase	"PCI DSS", "banking license", "Series X"
E-commerce	Shopify App Store, ModernRetail	"GMV", "DTC", "fulfillment"
Manufacturing	ThomasNet, MFG.com	"Industry 4.0", "ISO 9001", "ERP"

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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

Enrichment Depth Escalation Strategy

Not all leads deserve the same enrichment investment. Use a two-pass approach:

1. First pass (Standard depth): Enrich all discovered leads at Standard depth. This is cost-effective and provides enough data for initial scoring.
2. Score checkpoint: After the first pass, score all leads. Any lead scoring 70+ at Standard depth is a strong candidate.
3. Second pass (Deep depth): Re-enrich only leads scoring 70+ at Deep depth. This focuses expensive research (funding history, news, competitive analysis) on leads most likely to convert.
4. Skip threshold: Leads scoring below 30 after Standard enrichment should not be enriched further — the data is unlikely to improve their score enough to matter.

This approach typically reduces total enrichment cost by 40-60% while maintaining the same output quality for top-tier leads.

Email Pattern Discovery

Common corporate email formats (try in order):

1. [e-mail] (most common for small companies)
2. [e-mail] (most common for larger companies)
3. [e-mail] (e.g., jsmith@)
4. [e-mail] (e.g., johns@)

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

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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

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Lead Qualification Frameworks

BANT Framework

Use BANT to quickly qualify leads during or after enrichment. Each dimension maps to data you can discover through web research.

Dimension	Question	Research Signals
Budget	Can they afford the solution?	Funding rounds, revenue estimates, job postings for related roles, pricing tier of current tools
Authority	Is this person a decision-maker?	Title seniority (VP+, C-level, Director), reports to CEO/CTO, listed on "Leadership" page
Need	Do they have the problem you solve?	Job postings mentioning the pain point, tech stack gaps, competitor tool usage, complaints on forums
Timeline	Is there urgency to buy?	Contract renewals, compliance deadlines, product launches, recent leadership changes

BANT Scoring Overlay

Apply these modifiers on top of the base lead score:

Budget confirmed (funding, revenue signal):   +5
Authority confirmed (VP+ or C-level):         +5
Need confirmed (pain point evidence):         +5
Timeline confirmed (urgency signal):          +5
                                     Max bonus: +20

MEDDIC Framework

Use MEDDIC for complex / enterprise sales qualification where longer deal cycles demand deeper research.

Dimension	Definition	What to Look For
Metrics	Quantifiable outcomes the buyer cares about	Case studies they publish, KPIs in job postings, analyst reports, earnings calls
Economic Buyer	Person with budget authority to sign	CFO, CEO, VP Finance, or "Head of Procurement" listed on team pages
Decision Criteria	Factors they use to evaluate vendors	RFP documents, vendor comparison blog posts, compliance requirements, review site feedback
Decision Process	Steps from evaluation to purchase	Procurement team presence, legal/compliance review cycles, pilot program mentions
Identify Pain	Specific problems driving the purchase	Support forums, Glassdoor reviews, social media complaints, analyst reports on industry challenges
Champion	Internal advocate for your solution	Conference speakers, blog authors, open-source contributors, people who engage with your content

MEDDIC Research Checklist

For each enterprise lead, attempt to discover:
[ ] At least one quantifiable metric they care about
[ ] The economic buyer's name and title
[ ] 2+ decision criteria (compliance, performance, price, integration)
[ ] Whether they run formal procurement (RFP, committee)
[ ] 1+ specific pain point with evidence
[ ] A potential internal champion (engaged user, tech advocate)

Choosing Between BANT and MEDDIC

The qualification_framework setting controls which framework is applied. When set to "auto", use this decision table:

Scenario	Recommended Framework
SMB / startup targets, short sales cycle	BANT
Enterprise targets, $100K+ deal size	MEDDIC
Mixed list with varied company sizes	BANT first pass, MEDDIC for A-grade enterprise leads
Time-constrained research	BANT (faster to assess)

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Deduplication Strategies

Matching Algorithm

1. Exact match: Normalize company name (lowercase, strip Inc/LLC/Ltd) + person name
2. Fuzzy match: Levenshtein distance < 2 on company name + same person
3. Domain match: Same company website domain = same company
4. 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)

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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

| # | Name | Title | Company | Score | Grade | Qualification | Key Signal |
|---|------|-------|---------|-------|-------|---------------|------------|
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | A | BANT 4/4 | Series B funded, hiring |
| 2 | John Doe | CTO | Beta Inc | 72 | B | BANT 3/4 | Product launch Q1 2025 |

CRM Export Field Mappings

When crm_export_format is configured, produce an additional file with CRM-native field names:

HubSpot (JSON):

Lead Field	HubSpot Property
first_name	firstname
last_name	lastname
title	jobtitle
company	company
company_url	website
industry	industry
score	hs_lead_status (mapped: 80+ = "New", 60-79 = "Open", <60 = "In Progress")

Salesforce (CSV):

Lead Field	Salesforce Field
first_name	FirstName
last_name	LastName
title	Title
company	Company
company_url	Website
industry	Industry
score	Rating (mapped: 80+ = "Hot", 60-79 = "Warm", <60 = "Cold")
lead_source	LeadSource

Pipedrive (JSON):

Lead Field	Pipedrive Field
full_name	name
title	job_title
company	org_name
company_url	org_address
notes	note

---

Worked Examples

Example 1: Fintech SaaS Series A/B Companies (50-200 Employees)

Objective: Find 10 SaaS companies in the fintech space with 50-200 employees that recently raised Series A or B.

Step 1 — Define ICP

Industry:       Fintech / Financial Technology
Company size:   50-200 employees (SMB)
Funding stage:  Series A or Series B (raised within last 18 months)
Geography:      United States (primary), UK/EU (secondary)
Decision-maker: VP Engineering, CTO, or Head of Product
Pain points:    Scaling infrastructure, compliance automation, developer tooling

Step 2 — Execute Search Queries

# Primary discovery queries
"fintech" "series A" OR "series B" site:crunchbase.com/organization
"fintech startup" "raised" "$" "2025" OR "2024" site:techcrunch.com
site:news.crunchbase.com "fintech" "series A" OR "series B"

# Employee count validation
"fintech" "50" OR "100" OR "150" "employees" site:linkedin.com/company
site:builtin.com/companies/fintech "51-200 employees"

# Growth signals
"fintech" hiring "senior engineer" OR "staff engineer" site:linkedin.com/jobs
"fintech startup" "SOC 2" OR "PCI DSS" — compliance-ready = selling to banks

Step 3 — Enrich and Score Each Lead

For each discovered company, gather:
  1. Company website → About page → leadership team, employee count
  2. Crunchbase profile → funding amount, date, investors, total raised
  3. LinkedIn company page → exact employee count, recent hires
  4. Job boards → open roles (signals growth and tech stack)
  5. Press releases → product launches, partnerships, customer wins

Scoring example for "PayFlow Inc":
  ICP Match:         25/30 (fintech ✓, 130 employees ✓, US ✓, CTO found ✓, no geography bonus)
  Growth Signals:    18/20 (Series B $18M ✓, hiring 8 engineers ✓, product launch ✓)
  Enrichment:        15/20 (LinkedIn ✓, full company data ✓, tech stack ✓, no direct email)
  Recency:           15/15 (funding announced 3 weeks ago)
  Accessibility:     10/15 (company contact form, CTO LinkedIn)
  TOTAL:             83/100 → Grade A

Step 4 — Final Output (top 3 of 10)

#	Name	Title	Company	Employees	Funding	Score	Key Signal
1	Sarah Chen	CTO	PayFlow Inc	130	Series B, $18M	83	Funded 3 weeks ago, hiring 8 engineers
2	Marcus Rivera	VP Engineering	LendStack	85	Series A, $12M	78	Launched API platform Q4, SOC 2 certified
3	Priya Patel	Head of Product	ComplianceAI	62	Series A, $8M	75	Hiring product + eng, regulatory focus

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Example 2: Enterprise AI/ML Decision-Makers

Objective: Identify decision-makers at enterprise companies (500+ employees) that are actively adopting AI/ML tools.

Step 1 — Define ICP

Industry:       Any (cross-industry AI adoption)
Company size:   500+ employees (Enterprise)
Signals:        Active AI/ML adoption (hiring, projects, tool procurement)
Geography:      North America
Decision-maker: VP/Director of Data Science, Head of AI/ML, CTO, Chief Data Officer
Pain points:    ML model deployment, data pipeline scaling, AI governance

Step 2 — Execute Search Queries

# Identify companies investing in AI
"head of AI" OR "VP data science" OR "chief data officer" hiring site:linkedin.com
"[company] machine learning" "team" OR "department" site:linkedin.com/company
"AI adoption" OR "ML platform" "enterprise" site:venturebeat.com OR site:techcrunch.com

# Conference and community signals
"speaker" "machine learning" OR "AI" site:neurips.cc OR site:icml.cc
"[company] MLOps" OR "[company] AI infrastructure" site:github.com

# Budget and procurement signals
"AI budget" OR "ML tools" RFP site:gov OR site:rfpdb.com
"[company] partnership" "AI" OR "machine learning" press release

Step 3 — Multi-Source Enrichment

For enterprise targets, cross-reference at least 3 sources per lead:

  Source 1: LinkedIn
    → Title confirmation, tenure, reporting structure
    → Company employee count, growth rate
    → Recent posts about AI/ML topics (champion signal)

  Source 2: Company website + press
    → AI/ML team page, published case studies
    → Press releases about AI initiatives
    → Open positions on careers page

  Source 3: Community / conferences
    → Conference talks (NeurIPS, ICML, KDD, MLOps World)
    → GitHub contributions (open-source ML projects)
    → Blog posts or whitepapers on AI strategy

  MEDDIC qualification pass:
    Metrics:          "Reduced model deployment time by 60%" (from case study)
    Economic Buyer:   Chief Data Officer, reports to CEO
    Decision Criteria: SOC 2 compliance, on-prem option, Python SDK
    Decision Process:  Procurement committee, 90-day eval period
    Pain:             "Manual ML pipeline taking 3 weeks per model" (job posting)
    Champion:         Sr. ML Engineer who spoke at MLOps World about tooling gaps

Step 4 — Final Output (top 3)

#	Name	Title	Company	Employees	Score	Qualification
1	David Kim	Chief Data Officer	GlobalRetail Corp	3,200	91	MEDDIC 5/6: metrics, buyer, criteria, pain, champion
2	Lisa Zhang	VP Data Science	HealthFirst Systems	1,800	86	MEDDIC 4/6: buyer, criteria, pain, champion
3	James O'Brien	Director of AI	MegaBank Financial	12,000	80	MEDDIC 4/6: metrics, buyer, decision process, pain

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Example 3: Quick-Turn SMB List Build

Objective: Build a 20-lead list of SMB e-commerce brands using Shopify that might need an email marketing tool. Time budget: 30 minutes.

Abbreviated Flow

ICP (quick):
  Industry: E-commerce / DTC brands
  Size: 10-100 employees
  Platform: Shopify
  Signal: Active store, social media presence, no advanced email tool detected

Search queries (5 minutes):
  site:myshopify.com "[niche]"
  "[niche] brand" "shopify" site:linkedin.com/company
  site:apps.shopify.com/reviews "[competitor email tool]" — negative reviews = opportunity
  "DTC brands" "[niche]" "founded 2022" OR "founded 2023"

Enrichment (15 minutes, per lead):
  1. Shopify store URL → active? recent products?
  2. LinkedIn company page → employee count, founded year
  3. BuiltWith → check for existing email/marketing tools
  4. Instagram/TikTok → follower count (engagement proxy)

Scoring (5 minutes):
  Use simplified scoring: ICP match (40%) + Growth signals (30%) + Reachability (30%)
  Skip MEDDIC for SMB — use BANT quick-check instead

Output (5 minutes):
  Deliver as CSV with columns: Brand, URL, Employees, Platform, Current Email Tool, Score, Contact

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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

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Common Pitfalls

1. Outdated Data

Problem: Company details change fast — people change jobs, startups pivot, funding info ages. Mitigation:

• Verify every lead against at least 2 sources, and prefer sources updated within the last 90 days
• Flag any data point older than 6 months as "needs re-verification"
• Check LinkedIn tenure: if a contact joined their current role <3 months ago, they may not have budget authority yet

2. Over-Relying on a Single Source

Problem: Crunchbase has gaps in non-US companies. LinkedIn employee counts lag. News articles are biased toward funded companies. Mitigation:

• Always cross-reference: Crunchbase funding + LinkedIn headcount + company website team page
• Use at least 2 sources for employee count (the numbers often diverge by 20-30%)
• If a company has zero press coverage, check industry-specific directories rather than discarding it

3. Ignoring Enrichment Quality

Problem: A lead list with 50 names but only 10 have titles and 5 have company size data is not actionable. Mitigation:

• Set a minimum enrichment threshold before including a lead (e.g., must have: name + title + company + at least one signal)
• Track an "enrichment completeness" percentage per lead
• Return to partially-enriched leads in a second pass rather than shipping incomplete data

4. Vanity List Sizes

Problem: Delivering 100 leads when only 15 are qualified wastes the user's time and erodes trust. Mitigation:

• Better to deliver 10 A-grade leads than 50 C-grade leads
• Always sort by score descending and include a clear recommendation on where to draw the cut-off line
• If the target count cannot be met at acceptable quality, say so: "Found 7 leads meeting all criteria; 13 additional leads are partial matches"

5. Confusing Company Name Variants

Problem: "Stripe, Inc.", "Stripe", and "Stripe Payments Europe Ltd" can appear as three separate leads. Mitigation:

• Always normalize company names before deduplication (see Normalization Rules above)
• Match on website domain as the primary key — it is the most stable identifier
• Be especially careful with common words as company names ("Bolt", "Block", "Square")

6. Mistaking Hiring Activity for Purchase Intent

Problem: A company hiring engineers does not necessarily mean they are buying your product. Mitigation:

• Hiring is a growth signal, not a purchase signal — score it accordingly (contributor, not decisive)
• Look for more direct signals: RFPs, vendor comparison blog posts, demo requests, event attendance
• Combine hiring data with tech stack analysis: hiring a "Salesforce Admin" means Salesforce budget exists

7. Neglecting Negative Signals

Problem: Focusing only on positive signals and missing red flags. Mitigation:

• Check for layoffs, lawsuits, or executive departures — these reduce lead quality
• A company that just went through a 30% layoff is unlikely to approve new vendor spend
• Apply negative score modifiers:

Recent layoffs (>10% headcount):     -10
Lawsuit / regulatory action:         -5
Executive turnover (CEO/CTO left):   -5
Declining web traffic (per SimilarWeb): -3

8. Skipping the ICP Step

Problem: Jumping straight into search without a clear ICP produces scattered, low-quality results. Mitigation:

• Always define the ICP before the first search query, even if it takes 5 extra minutes
• Write the ICP down explicitly (industry, size, geography, role, pain point, budget signal)
• Revisit and tighten the ICP after the first 10 leads if results are too broad

Pitfall Severity Quick Reference

Pitfall	Severity	Frequency	Fix Effort
Outdated data	High	Very common	Medium (multi-source verification)
Single source reliance	High	Common	Low (add 1-2 extra sources)
Poor enrichment quality	Medium	Common	Medium (set thresholds, second pass)
Vanity list sizes	Medium	Common	Low (enforce scoring cut-off)
Company name variants	Medium	Very common	Low (normalize + domain match)
Hiring != purchase intent	Low	Occasional	Low (adjust scoring weight)
Ignoring negative signals	High	Common	Medium (add negative modifiers)
Skipping ICP	High	Occasional	Low (5-minute discipline)