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Why Most List Building Fails?

Most people build lists using basic filters: company size, location, job title, industry. This approach gets you lists of people who might be interested, not people who need what you're selling right now.

The difference: Instead of targeting people who could buy, target people who are actively looking for solutions like yours.

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Here are some tactics that just worked for us:

LinkedIn Sales Nav + Boolean Search

Podcast Guest Scraping

Job Posting Intelligence

Google Maps Scraping (Local Businesses)

List Quality Over Quantity Framework

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The 90/10 Rule

• 90% targeting accuracy using custom data sources (podcasts, job postings, Google Maps)
• 10% targeting accuracy using standard tools (Apollo, LinkedIn basic filters)

Creative Data Sources:

• [ ] Industry podcast guest lists
• [ ] Company job postings (especially recent ones)
• [ ] Google Maps for local businesses
• [ ] Industry event speaker lists
• [ ] Company blog contributor lists
• [ ] LinkedIn group member lists
• [ ] Recent company news and announcements </aside>

List Segmentation (Very Important)

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Segment 1: Hot Prospects

• Recently changed jobs OR actively hiring for your solution
• High personalization, direct outreach

Segment 2: Warm Prospects

• Posted on LinkedIn recently OR viewed your profile
• Medium personalization, value-first approach

Segment 3: Cold Prospects

• Basic demographic fit but no recent activity
• Light personalization, volume approach </aside>

Success Metrics to Track

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List Quality Metrics:

• Acceptance rate by list source (aim for 15%+ from quality lists)
• Positive response rate (aim for 5%+)
• Meeting booking rate (aim for 2%+)
• Deal close rate by list source

List Building Efficiency:

• Time spent building list vs. results generated
• Cost per qualified lead by data source
• List accuracy percentage (how many bounce/wrong person) </aside>

Common List Building Mistakes

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❌ Using only standard database tools - Everyone has the same lists ❌ Focusing on company size over need indicators - Big companies don't always need you ❌ Building lists too far in advance - Data gets stale, opportunities get missed

❌ Not segmenting by intent level - Treating hot and cold prospects the same ❌ Ignoring list hygiene - Bad data kills response rates

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Research beats volume: 50 highly researched prospects beat 500 generic ones

Combine data sources: Use multiple filters and sources for maximum accuracy