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Why Most List Building Fails?

Most people build lists using basic filters: company size, location, job title, industry, and pull out hugeee 2.5k+ lists at once.

This approach gets you lists of people who might be interested, not people who need what you're selling right now. And it’s basically a waste of effort because it takes too long to exhaust, and by that time you’re done with LinkedIn, claiming “it doesn’t work”.

The difference: Instead of targeting people who could buy, target people who are actively looking for solutions like yours and get smaller lists so you can tweak and test as much as possible.

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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