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linkedin-sales-navigator-research-process

12 min read

LinkedIn Sales Navigator Research Process

Purpose: Step-by-step process to find ICP fits in your current network using LinkedIn Sales Navigator
Target: Identify qualified leads matching Brainforge ICP criteria
Last Updated: 2025-01-26

Overview

Goal: Systematically identify and qualify leads in your network who match Brainforge ICP criteria using LinkedIn Sales Navigator filters and research.

Key Qualification Signals:

  • Company stage: Post Series A minimum
  • Revenue: 10M–$100M)
  • Decision maker: Operator-level leader with authority
  • Pain point: Clear operational pain (scrappy data, conflicting numbers, bottlenecks)
  • Urgency: Active project, funding cycle, board deadline, or operational stress
  • Budget: 30K/month (startups) to 100K+/month (enterprise)

Time Investment: 15-20 minutes per qualified lead (research + qualification)

Phase 1: Build Search Filters

Search Strategy 1: Decision Makers by Role (Primary)

Use This When: You want to find decision makers first, then qualify companies

Sales Navigator Filters:

People Filters:

  • Connection: 1st connections OR 2nd connections (leverage network)
  • Title Keywords:
    • “Head of Data” OR “VP Data” OR “Director Data” OR “Head of Analytics” OR “VP Analytics” OR “Director Analytics”
    • OR “CTO” OR “Co-Founder” OR “Founder” OR “CEO”
    • OR “Head of BI” OR “VP Engineering” OR “Chief Data”
  • Seniority Level: Director, VP, C-Level
  • Function: Engineering, Information Technology, Operations, Product Management

Company Filters:

  • Company Size: 25-500 employees (ideal: 50-500)
  • Industry:
    • Technology, Information and Internet
    • Computer Software
    • Consumer Goods
    • Retail
    • Logistics and Supply Chain
    • Manufacturing
  • Headquarters: North America (or your target geography)
  • Growth: Growing (if available)

Save This Search: “ICP - Data Leaders 1st/2nd Connections”

Search Strategy 2: Companies by Stage (Secondary)

Use This When: You want to find companies first, then identify decision makers

Sales Navigator Filters:

Company Filters:

  • Company Size: 25-500 employees
  • Industry: Technology, Consumer Goods, Retail, Logistics, Manufacturing
  • Headquarters: North America
  • Growth: Growing
  • Keywords in Company Description:
    • “Series A” OR “Series B” OR “Series C”
    • OR “growth-stage” OR “scaling” OR “30M” OR “$100M”

People Filters:

  • Connection: 1st connections OR 2nd connections
  • Title Keywords: (Same as Strategy 1)
  • Seniority Level: Director, VP, C-Level

Save This Search: “ICP - Growth-Stage Companies”

Search Strategy 3: Hiring Signals (High Priority)

Use This When: You want to find companies actively hiring data roles (indicates urgency)

Sales Navigator Filters:

Company Filters:

  • Company Size: 25-500 employees
  • Industry: Technology, Consumer Goods, Retail, Logistics, Manufacturing
  • Headquarters: North America
  • Keywords in Company Description: (Same as Strategy 2)

People Filters:

  • Connection: 1st connections OR 2nd connections
  • Title Keywords:
    • “Head of Data” OR “VP Data” OR “CTO” OR “CEO” OR “Founder”
  • Seniority Level: Director, VP, C-Level
  • Recently Posted: (Check for recent job postings on company page)

Additional Research:

  • Check company LinkedIn page for job postings
  • Look for “Hiring” badges on profiles
  • Search company careers page for data/AI roles

Save This Search: “ICP - Hiring Data Roles”

Search Strategy 4: Funding Signals (Urgency Indicator)

Use This When: You want to find companies with recent funding (indicates growth/urgency)

Sales Navigator Filters:

Company Filters:

  • Company Size: 25-500 employees
  • Industry: Technology, Consumer Goods, Retail, Logistics, Manufacturing
  • Headquarters: North America
  • Keywords in Company Description:
    • “raised” OR “funding” OR “Series A” OR “Series B” OR “investment”

People Filters:

  • Connection: 1st connections OR 2nd connections
  • Title Keywords: (Same as Strategy 1)
  • Seniority Level: Director, VP, C-Level
  • Recent Activity: Posted in last 30 days (check for funding announcements)

Additional Research:

  • Check Crunchbase for recent funding rounds
  • Search Google News for “[Company Name] funding”
  • Look for LinkedIn posts about funding

Save This Search: “ICP - Recent Funding”

Phase 2: Research Workflow (Per Lead)

Step 1: Initial Qualification (2-3 minutes)

LinkedIn Profile Review:

  • Role: Does title indicate decision-making authority?
    • ✅ Head of Data, VP Data, Director Data, CTO, CEO, Founder
    • ⚠️ Manager, Senior Analyst (may be influencer, not decision maker)
    • ❌ Individual Contributor, Analyst (likely not decision maker)
  • Company: Check company size and industry
    • ✅ 25-500 employees, target industries
    • ⚠️ 500-1000 employees (may be too large, slow procurement)
    • ❌ <25 employees (too early) or >5000 (too slow)
  • Connection Degree: 1st or 2nd connection?
    • ✅ 1st connection (direct outreach)
    • ✅ 2nd connection (mutual intro playbook)
    • ⚠️ 3rd+ connection (harder to reach, lower priority)

Quick ICP Check:

  • Stage: Post Series A? (Check company description, LinkedIn, or Google)
  • Revenue: $10M+ ARR/GMV? (May need to research)
  • Decision Maker: Operator-level leader? (From role/title)

If 2+ red flags → Disqualify and move on
If 1 red flag → Mark as “Needs Research”
If all green → Proceed to Step 2

Step 2: Company Research (5-7 minutes)

Company Website:

  • About Page: Company size, stage, industry
  • Careers Page:
    • Are they hiring data engineers/analysts? (Urgency signal)
    • What data tools do they mention? (Tech stack signal)
  • News/Blog: Recent funding, growth announcements, challenges

LinkedIn Company Page:

  • Company Size: Confirm employee count
  • Recent Posts: Funding announcements, growth milestones, hiring
  • Job Postings: Data/AI roles? (Urgency signal)
  • Tech Stack: Tools mentioned in job postings or posts

Crunchbase/Google Research:

  • Funding History: Recent Series A/B/C? (Stage signal)
  • Revenue Estimate: $10M+ ARR/GMV? (May be estimated)
  • Growth Signals: Recent hires, expansion, new products

Qualification Signals to Look For:

  • ✅ Recent funding (Series A/B/C in last 12 months)
  • ✅ Hiring data engineers/analysts
  • ✅ Job postings mention data challenges
  • ✅ Growth announcements (new markets, products, revenue milestones)
  • ✅ Tech stack mentions: Snowflake, dbt, Amplitude, Mixpanel, Looker, Tableau
  • ⚠️ No clear growth signals (may still qualify if role/company fit)
  • ❌ Pre-revenue or <$1M revenue
  • ❌ No data infrastructure signals

Step 3: Individual Research (3-5 minutes)

LinkedIn Profile Deep Dive:

  • Experience:
    • How long in current role? (New role = potential urgency)
    • Previous roles indicate data/AI focus?
    • Background at similar companies?
  • Activity:
    • Recent posts about data challenges?
    • Engaging with data/AI content?
    • Sharing growth/scale challenges?
  • Skills/Endorsements: Data tools, analytics, AI skills
  • Recommendations: What do others say about their data/AI work?

Mutual Connections:

  • 1st Connections: Who do you know at their company?
  • 2nd Connections: Who can introduce you?
  • Shared Background: Same school, previous company, industry?

Personalization Angles:

  • Company-Level: Recent funding, hiring, growth challenges
  • Role-Level: Data infrastructure needs, analytics bottlenecks
  • Individual: Background, recent activity, mutual connections

Step 4: Pain Point Identification (2-3 minutes)

Look for Pain Point Signals:

Startup Stage (30M ARR):

  • ✅ Job postings mention “scrappy data” or “building data foundation”
  • ✅ Recent Series A/B funding (need to prove data value)
  • ✅ Hiring first data hire (building from scratch)
  • ✅ Company description mentions “data-driven” but no data team

Scale-Up Stage (100M ARR):

  • ✅ Job postings mention “conflicting numbers” or “single source of truth”
  • ✅ Hiring multiple data roles (scaling team)
  • ✅ Recent growth announcements (need to operationalize analytics)
  • ✅ Tech stack mentions multiple tools (potential integration challenges)

Enterprise Stage ($500M+ ARR):

  • ✅ Job postings mention “complexity” or “automation”
  • ✅ Recent AI/automation initiatives
  • ✅ Multiple data tools mentioned (potential tool sprawl)
  • ✅ Focus on “scaling decision-making” or “operationalizing AI”

Urgency Signals:

  • ✅ Recent funding (board pressure)
  • ✅ Hiring data roles (active project)
  • ✅ Recent posts about data challenges
  • ✅ New role (new leader, new priorities)
  • ⚠️ No clear urgency signals (may still qualify)

Step 5: Qualification Scorecard (2 minutes)

Score Each Lead (1-10):

Company Fit (ICP):

  • 10: Perfect match (100M ARR, Post Series A, target industry)
  • 7-9: Good match (close to ideal, minor gaps)
  • 4-6: Fair match (some ICP criteria met)
  • 1-3: Poor match (major gaps)

Decision Maker:

  • 10: C-level decision maker (CEO, CTO, Founder)
  • 7-9: VP/Director with clear authority (Head of Data, VP Data)
  • 4-6: Manager/influencer (may need to go up)
  • 1-3: Individual contributor only

Pain Point:

  • 10: Clear, urgent pain point (hiring, funding, active project)
  • 7-9: Identified pain point (growth challenges, data needs)
  • 4-6: Potential pain point (inferred from company stage)
  • 1-3: No clear pain point

Budget:

  • 10: Budget confirmed (hiring signals, growth-stage)
  • 7-9: Budget likely (Post Series A, scaling)
  • 4-6: Budget unclear (need to qualify)
  • 1-3: No budget signals

Timeline:

  • 10: Active project, ready to start (hiring, urgent need)
  • 7-9: Project planned in 3-6 months (growth signals)
  • 4-6: Project planned in 6-12 months (early stage)
  • 1-3: No timeline

Total Score:

  • 40-50: Highly qualified → Pursue aggressively
  • 30-39: Qualified → Pursue with standard cadence
  • 20-29: Marginally qualified → Low priority
  • 10-19: Not qualified → Disqualify

Phase 3: Prioritization Framework

Priority Tiers

Tier 1: High Priority (Score 40-50)

  • Perfect ICP fit
  • Clear decision maker
  • Urgent pain point
  • Active project/hiring
  • Action: Reach out within 24-48 hours

Tier 2: Medium Priority (Score 30-39)

  • Good ICP fit
  • Decision maker identified
  • Pain point identified
  • Some urgency signals
  • Action: Reach out within 1 week

Tier 3: Low Priority (Score 20-29)

  • Fair ICP fit
  • Decision maker unclear
  • Pain point inferred
  • No clear urgency
  • Action: Nurture, add to long-term list

Tier 4: Disqualify (Score 10-19)

  • Poor ICP fit
  • No decision maker
  • No clear pain point
  • Action: Remove from list

Phase 4: Tracking & Organization

GSheets Tracking Template

Columns:

  • A: Lead Name
  • B: Company
  • C: Connection Degree (1st/2nd)
  • D: Role/Title
  • E: Company Stage (Startup/Scale-Up/Enterprise)
  • F: Revenue Estimate (30M / 100M / $500M+)
  • G: ICP Score (Total)
  • H: Company Fit (1-10)
  • I: Decision Maker (1-10)
  • J: Pain Point (1-10)
  • K: Budget (1-10)
  • L: Timeline (1-10)
  • M: Priority Tier (1-4)
  • N: Pain Point Signals
  • O: Urgency Signals
  • P: Personalization Angles
  • Q: Mutual Connections
  • R: Research Date
  • S: Status (Researching/Qualified/Outreach/Responded/Closed)
  • T: Next Action

Sales Navigator Lists

Create Lists in Sales Navigator:

  1. “ICP - Tier 1 (High Priority)”

    • Score 40-50
    • Reach out within 24-48 hours

  2. “ICP - Tier 2 (Medium Priority)”

    • Score 30-39
    • Reach out within 1 week

  3. “ICP - Tier 3 (Low Priority)”

    • Score 20-29
    • Nurture, long-term

  4. “ICP - Needs Research”

    • Incomplete research
    • Follow up when time allows

  5. “ICP - Disqualified”

    • Score 10-19
    • Not a fit, archive

Phase 5: Weekly Research Routine

Daily Routine (30-45 minutes)

Morning (15 minutes):

  • Review saved searches for new leads
  • Quick scan of new profiles (initial qualification)
  • Add qualified leads to “Needs Research” list

Afternoon (15-30 minutes):

  • Deep research on 2-3 leads from “Needs Research”
  • Complete qualification scorecard
  • Add to appropriate priority tier list
  • Update GSheets tracking

Weekly Routine (2-3 hours)

Monday:

  • Review all saved searches
  • Identify 10-15 new leads to research
  • Prioritize based on urgency signals

Tuesday-Thursday:

  • Deep research on 3-5 leads per day
  • Complete qualification scorecards
  • Add to priority tier lists

Friday:

  • Review Tier 1 leads (prepare for outreach)
  • Review Tier 2 leads (plan next week outreach)
  • Update GSheets with all research
  • Plan next week’s research focus

Phase 6: Research Efficiency Tips

Time-Saving Strategies

  1. Batch Research:

    • Research 5-10 leads at once
    • Use multiple tabs (LinkedIn, Crunchbase, company website)
    • Fill out scorecards in batch

  2. Use Saved Searches:

    • Save your filter combinations
    • Check daily for new results
    • Adjust filters based on results

  3. Quick Disqualification:

    • If 2+ red flags in Step 1, disqualify immediately
    • Don’t spend time on poor fits
    • Focus on high-probability leads

  4. Leverage Alerts:

    • Set up Google Alerts for target companies
    • Monitor LinkedIn for funding announcements
    • Track job postings on company pages

  5. Template Research:

    • Create research checklist template
    • Use same process for all leads
    • Speed up with practice

Phase 7: Red Flags & Disqualification

Immediate Disqualification Signals

Company Stage:

  • ❌ Pre-revenue or <$1M revenue
  • ❌ <25 employees (too early)
  • ❌ >5000 employees (too slow, traditional enterprise)
  • ❌ No funding history (may not have budget)

Decision Maker:

  • ❌ Individual contributor only
  • ❌ No clear authority (junior role)
  • ❌ Delegated “AI” downward with no executive ownership

Pain Point:

  • ❌ Still debating whether data infrastructure matters
  • ❌ Research-only with no business outcomes
  • ❌ No operational pressure or urgency

Budget:

  • ❌ No formal data/AI budget
  • ❌ Seeking cheap execution (not business leverage)
  • ❌ Staff augmentation request (not strategic)

If 2+ red flags → Disqualify immediately

Phase 8: Research Quality Checklist

Before Adding to Outreach List

  • ICP Match: Company stage, revenue, industry fit
  • Decision Maker: Clear authority, operator-level leader
  • Pain Point: Identified operational pain
  • Urgency: Active project, funding, or growth signals
  • Budget: Likely budget based on stage/signals
  • Personalization: At least 2-3 personalization angles identified
  • Connection: 1st or 2nd connection (or path to connect)
  • Score: 30+ on qualification scorecard

If all checked → Ready for outreach
If missing items → Continue research or disqualify

Example Research Workflow

Example Lead: “Sarah Chen, Head of Data at Acme Corp”

Step 1: Initial Qualification (2 min)

  • ✅ Role: Head of Data (decision maker)
  • ✅ Company: Acme Corp, 150 employees
  • ✅ Connection: 2nd connection
  • ✅ Quick check: Post Series A, SaaS company
  • Result: Proceed to Step 2

Step 2: Company Research (6 min)

  • ✅ Company website: $25M ARR, Series B, SaaS
  • ✅ Careers page: Hiring 2 data engineers (urgency signal)
  • ✅ LinkedIn: Recent growth announcement, using Snowflake
  • ✅ Crunchbase: Series B 6 months ago ($15M)
  • Result: Strong ICP fit, urgency signals

Step 3: Individual Research (4 min)

  • ✅ Experience: 2 years as Head of Data, previous VP Analytics at similar company
  • ✅ Activity: Recent post about “scaling data infrastructure”
  • ✅ Mutual connections: 3 shared connections (intro path)
  • Result: Strong personalization angles

Step 4: Pain Point Identification (2 min)

  • ✅ Hiring data engineers (active project)
  • ✅ Recent Series B (board pressure)
  • ✅ Post about scaling challenges (operational pain)
  • ✅ Using Snowflake (data infrastructure investment)
  • Result: Clear pain point, high urgency

Step 5: Qualification Scorecard (2 min)

  • Company Fit: 9/10 (Scale-Up, $25M ARR, SaaS)
  • Decision Maker: 10/10 (Head of Data, clear authority)
  • Pain Point: 10/10 (Hiring, funding, scaling challenges)
  • Budget: 9/10 (Series B, hiring signals)
  • Timeline: 10/10 (Active hiring, urgent need)
  • Total: 48/50 → Tier 1, High Priority

Result: Ready for immediate outreach via mutual intro

Success Metrics

Track Weekly:

  • Leads researched: Target 15-20 per week
  • Qualified leads (30+ score): Target 5-8 per week
  • Tier 1 leads (40+ score): Target 2-3 per week
  • Research time per lead: Target 15-20 minutes
  • Qualification rate: Target 30-40% (qualified/researched)

Track Monthly:

  • Total leads researched
  • Qualified leads added to outreach
  • Response rate from researched leads
  • Conversion rate (research → meeting → qualified opportunity)

Next Steps After Research

  1. Add to Outreach List: Qualified leads (30+ score)
  2. Prepare Outreach: Use personalization angles from research
  3. Leverage Connections: Request mutual intros for 2nd connections
  4. Track Results: Update GSheets with outreach status
  5. Iterate: Adjust research process based on what works

This research process enables systematic identification and qualification of ICP fits in your network using LinkedIn Sales Navigator.

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