Lead Research

• With Sales Navigator: Access full profile info, see who viewed them, more activity history, notes/tags
• Without Sales Navigator: Standard profile view, may have limited visibility for non-connections

Offer to save: If user answers and no DECISIONS.md exists, offer to save this preference.

Step 1: Identify Target Lead

If linkedin_url provided:

• Use directly for research

If lead_name provided:

• Search in campaigns under .business_growth/sales/campaigns/
• Find matching lead and get LinkedIn URL

If campaign_id provided (without specific lead):

• Load LIST.md from .business_growth/sales/campaigns/<campaign_id>/
• Ask user which lead to research, or research all leads

If neither provided:

1. Check for existing campaigns in .business_growth/sales/campaigns/
2. If campaigns exist, ask user:
   • (A) Create new campaign → Generate a meaningful snake_case name
   • (B) Add to existing campaign → Select from available campaigns
3. Establish campaign_id before proceeding
4. Ask user for LinkedIn URL or lead name to research

Step 2: Navigate to LinkedIn Profile

1. Get browser context with tabs_context_mcp
2. Navigate to profile:
   • With Sales Navigator: Use linkedin.com/sales/lead/<id> format for richer data
   • Without Sales Navigator: Use standard linkedin.com/in/<username> URL
3. Take screenshot to verify profile loaded
4. Check for any access restrictions (may need to be connected for full profile)

Step 3: Extract Profile Information

Use read_page to extract:

Basic Info:

• Full name
• Current title and company
• Location
• Profile headline
• Connection degree

About Section:

• Summary/bio content
• Key themes and interests
• Communication style

Experience:

• Current role details
• Previous companies
• Career progression
• Time in current role

Education:

• Schools attended
• Degrees and fields
• Graduation years

Skills & Endorsements:

• Top skills
• Endorsement counts

Step 4: Analyze Recent Activity

Scroll to activity section and extract:

Recent Posts (last 30 days):

• Topics they post about
• Engagement levels
• Opinions and perspectives

Reactions & Comments:

• Content they engage with
• Thought leaders they follow
• Interests indicated

Articles Written:

• Published content
• Expertise areas

Step 5: Identify Personalization Opportunities

Analyze gathered data for:

Conversation Starters:

• Recent post to reference
• Shared connections
• Common background (school, company)
• Mutual interests

Pain Point Signals:

• Job changes (new role = open to tools)
• Company growth signals
• Hiring activity
• Complaints or challenges mentioned

Rapport Builders:

• Hobbies and interests
• Causes they support
• Content preferences

Timing Signals:

• Best time to reach out
• Upcoming events
• Recent achievements to congratulate

Step 6: Generate Ice Breakers

Create 3-5 personalized ice breakers based on research:

Types of Ice Breakers:

1. Content Reference: "Loved your post about X..."
2. Shared Connection: "I see we both know Y..."
3. Common Background: "Fellow [school/company] alum here..."
4. Industry Insight: "Given your focus on X, thought you'd find..."
5. Congratulations: "Congrats on the recent [achievement]..."

Step 7: Save Research

Save to the campaign folder: .business_growth/sales/campaigns/<campaign_id>/leads/<lead_name>/research.md

Lead Naming: Generate meaningful snake_case names for leads based on their name and company (e.g., john_smith_acme, sarah_jones_techcorp). Keep names concise, lowercase with underscores, no special characters.

Create research file:

# Lead Research: <Full Name>

## Research Metadata
- **Researched**: <ISO timestamp>
- **LinkedIn**: <url>
- **Campaign**: <campaign_id if applicable>

## Profile Overview

### Current Position
- **Title**: <title>
- **Company**: <company>
- **Duration**: <time in role>
- **Location**: <location>

### Background
- **Previous Role**: <most recent previous>
- **Education**: <school, degree>
- **Career Theme**: <progression summary>

### About
<summary of their bio/about section>

## Recent Activity (Last 30 Days)

### Posts
1. **<date>**: <topic/summary>
   - Engagement: <likes/comments>
   - Key quote: "<relevant excerpt>"

2. **<date>**: <topic/summary>
   - Engagement: <likes/comments>

### Engagement Patterns
- Topics they engage with: <list>
- Thought leaders followed: <names>
- Activity level: Low / Medium / High

## Personalization Opportunities

### Strong Signals
1. <signal with context>
2. <signal with context>

### Potential Pain Points
1. <inferred pain point>
2. <inferred pain point>

### Shared Context
- Connections: <mutual connections>
- Background: <shared experiences>
- Interests: <common interests>

## Recommended Ice Breakers

### Option 1: Content Reference
"<personalized message referencing their recent post>"

**Why this works**: <explanation>

### Option 2: Industry Insight
"<message offering relevant insight>"

**Why this works**: <explanation>

### Option 3: Mutual Connection
"<message leveraging shared connection or background>"

**Why this works**: <explanation>

## Outreach Recommendations

### Best Approach
<recommended outreach strategy>

### Timing
<best time/day to reach out>

### Tone
<recommended communication style based on their profile>

### Topics to Avoid
<anything that might not resonate>

## Raw Notes
<any additional observations>

Step 8: Report Summary

Provide user with:

• Key findings summary
• Top 2-3 personalization angles
• Recommended ice breaker
• Suggested next steps

Browser Tools Used

Research Quality Tips

Good Personalization

• Specific and recent (last 30 days)
• Shows genuine interest
• Relevant to outreach purpose
• Not creepy or over-researched

Avoid

• Mentioning personal/family info
• Referencing very old content
• Being too detailed (feels stalky)
• Generic compliments
• Assumptions about their problems

Integration with Campaign Execution

When research is saved to a campaign lead folder:

• {{custom}} token in templates can reference research findings
• Campaign execution skill will use research for personalization
• Higher quality outreach leads to better response rates