lead-intelligence

Lead Intelligence

Full-cycle lead research: from raw prospect to qualified, personalized outreach-ready intelligence.

When to Use

• Researching a specific prospect or company
• Building a prospecting list for a segment
• Qualifying inbound leads
• Preparing personalized outreach campaigns

Process

Step 1: Prospect Research

For each prospect, gather:

• Role & seniority — Title, responsibilities, decision-making power
• Company — Size, industry, growth stage, tech stack, recent news
• Activity — LinkedIn posts, articles, podcast appearances, conference talks
• Network — Mutual connections, shared groups, common interests
• Pain signals — Hiring for roles that suggest our problem area, complaints in posts, industry-specific challenges

Use WebSearch extensively. Check LinkedIn, company website, press releases, Glassdoor.

Step 2: Signal Scoring

Score each prospect (0-100):

Signal	Weight	Scoring
Role fit	30%	Decision maker: 100. Influencer: 70. User: 40. Unrelated: 0.
Industry match	25%	ICP industry: 100. Adjacent: 60. Unrelated: 0.
Activity signals	20%	Active on LinkedIn/content: 100. Moderate: 50. Ghost: 10.
Company size fit	15%	Sweet spot: 100. Too big/small but viable: 50. Wrong: 0.
Timing signals	10%	Hiring, funding, expansion: 100. Stable: 50. Downsizing: 10.

Threshold: Score ≥65 = qualified. 40-64 = nurture. <40 = skip.

Step 3: Warm Path Discovery

For qualified prospects, identify:

• Mutual connections who could introduce
• Content they've engaged with (comment on it first)
• Events they're attending
• Communities they're active in
• Any existing relationship touchpoints

Step 4: Pain Mapping

Map prospect's likely pains to our value:

Pain: [specific problem they likely have]
Evidence: [what suggests this — hiring posts, complaints, industry trend]
Our angle: [how we address this specifically]
Proof: [case study, metric, or example from similar company]