Creating a B2B prospecting list is not just a tooling question. It is an arbitrage between time, technical skill, data quality and budget. This guide compares eight concrete methods so you can choose the right one.
1. Why a structured prospecting list matters
A good file makes prospecting repeatable. You can filter by segment, enrich later, import into a CRM, and follow up without rebuilding the list every time.
- Company name
- City or region
- Professional email
- Phone or website
- Source or segment
For a concrete structure, read the Excel prospecting list guide.
2. The 8 methods compared
1. Manual research on Google and company websites
Accessible and free, but slow. It works for a few dozen highly targeted accounts, not for volume.
2. Open data and public CSV exports
Useful for mapping a market, but usually weak on direct emails and outreach-ready fields.
3. LinkedIn Sales Navigator
Strong for targeting people by role, seniority and industry. It still needs enrichment to obtain emails.
4. Lobstr.io
Useful for no-code scraping from structured sources. The output still needs review and cleaning.
5. Apify
Powerful for technical users. For emails, expect a two-step process: scrape companies, then enrich websites.
6. Outscraper
Good for Google Maps extraction, but emails usually require a separate enrichment step.
7. Kompass
Useful for market mapping and rich company data, but heavy for fast outreach testing.
8. SphereScout
The fastest option when you need verified contacts, filters and direct CSV or Excel export without maintaining a scraping pipeline.
3. Comparison table
| Method | Cost | Time | Volume | Technical effort |
|---|---|---|---|---|
| Manual research | Free | Very long | Low | Low |
| Open data | Free | Long | Medium | Low |
| LinkedIn Sales Navigator | Paid per seat | Long | Medium | Medium |
| Lobstr.io | $20-$100/mo | Medium | High | Medium |
| Apify / Outscraper | Variable | Medium to long | High | High |
| Kompass | Quote-based | Medium | High | Medium |
| SphereScout | Free sample, then paid | Fast | High | Low |
4. Which method should you choose?
If you are starting alone and targeting very few companies, manual research is enough.
If you need to understand a market before buying data, start with open data and compare a small ready-made export.
If you have technical skills and a precise source, scraping tools can work, but include setup and cleanup time.
If you need usable volume quickly, a ready-to-use export is usually cheaper than building and maintaining a pipeline.
5. SphereScout: the fastest method to test
SphereScout is not the only method. It is the fastest to put into production for teams without an existing scraping setup.
You search by sector and location, preview results, and export verified contacts. The free account lets you test the format before paying.
How to make the choice in practice
Choose the method according to the constraint that is most expensive for your team.
If time is scarce, avoid manual research. If data precision matters more than volume, start with a small hand-built list and compare it against a purchased export. If engineering time is available, scraping can work, but include maintenance and enrichment in the real cost.
For most B2B teams, the best first test is a narrow segment with measurable output: 200 to 1,000 companies, one geography, one offer, and one campaign owner.
Metrics to watch after the first export
- Valid email rate after verification.
- Bounce rate after the first send.
- Reply rate by segment or category.
- CRM duplicates created during import.
- Time spent cleaning before launch.
The cheapest method on paper is not always the cheapest method in production. A file that saves five hours of cleaning can be more valuable than a free export that blocks the campaign.