Try OnsaLinkedIn has 1 billion profiles. Your next 50 customers are in there somewhere.
The problem isn’t access — it’s filtering. Every B2B founder I talk to has the same experience: they search LinkedIn, get 10,000 results, scroll through the first 3 pages, message 20 people who seem like a fit, and hear back from maybe 2.
That’s a 10% response rate on a hand-picked list. Which means the hand-picking isn’t working.
After watching how dozens of founders actually search for leads on our platform — and where they get stuck — I’ve mapped out what works at each budget level. Free LinkedIn, Sales Navigator, and AI-powered enrichment. The right approach depends on how many leads you need and how niche your ICP is.
Most founders jump straight to Sales Navigator without realizing how much free LinkedIn can do. Here’s what’s available without paying anything:
You can filter by: - Keywords (in title, company, posts) - Location (country, city, metro area) - Current company - Industry (broad categories) - Connections (1st, 2nd, 3rd degree) - “People who talk about” topics
You can’t filter by: - Company headcount (the single most useful filter) - Seniority level - Years in current role - Company revenue or funding - Technology stack - Job changes in last 90 days
Free search works if your ICP is defined by industry + geography + job title and you’re targeting fewer than 100 people. A SaaS founder looking for “VP of Marketing at fintech companies in London” can find 30-40 relevant profiles manually.
It breaks down when you need volume or precision. “Head of Sales at Series B SaaS companies with 50-200 employees in the US” requires filters that free LinkedIn doesn’t have.
The free LinkedIn playbook: 1. Search your target job title + industry keyword (e.g., “VP Sales” + “SaaS”) 2. Filter to 2nd-degree connections (warm intros possible) 3. Check each profile’s company size manually (look at the company page) 4. Look at their recent posts — are they talking about problems you solve? 5. Save promising profiles to a spreadsheet
This takes about 2 hours to build a list of 30-40 qualified leads. Not scalable, but good enough for your first outreach campaign.
Sales Navigator’s core value is three filters that free LinkedIn doesn’t have: company headcount, seniority level, and job changes.
These three filters alone transform your search from “everyone who might fit” to “people who probably fit.”
Here’s how each ICP criterion maps to Sales Navigator:
ICP Criteria → Sales Nav Filter → How to use it
Industry → Industry filter → Use LinkedIn’s taxonomy, not your own labels. “Computer Software” covers most SaaS.
Company Size → Company headcount → The ranges are coarse (11-50, 51-200, 201-500) but functional. This is the #1 filter you’re paying for.
Geography → HQ location → Filter by country, state, or metro. Be specific — “San Francisco Bay Area” not just “United States.”
Buyer Role → Job title + Seniority → Combine title keywords with seniority level (VP, Director, CXO). “VP” + “Sales” captures VP of Sales, SVP of Sales, VP Sales & Marketing.
Company Type → Company type → Filter out staffing agencies, educational institutions, nonprofits that clutter results.
Job Changes → Changed jobs in past 90 days → People who just started a new role are 3x more likely to buy new tools. They have budget, mandate, and motivation to make changes.
Where Sales Navigator filters exist but underperform:
Funding Stage — Sales Navigator does have a “Funding Type” filter (Seed, Series A, B, C, etc.), but the data is sparse and disconnected from sources like Crunchbase. You’ll get far fewer results than actually exist. Use it as a rough filter, but don’t rely on it for completeness — Crunchbase is still the gold standard for funding data.
Department Headcount Growth — Sales Navigator has this filter and it works reasonably well for spotting companies with growing teams. The limitation is granularity — you can filter by growth ranges but can’t combine it with specific department growth (e.g., “sales team growing 30%+ while engineering is flat”).
What Sales Navigator genuinely can’t do:
Tech Stack — You can’t find “companies using Salesforce + HubSpot.” BuiltWith or enrichment tools know this, LinkedIn doesn’t.
Intent Signals — You can’t find “companies actively evaluating CRM alternatives.” Bombora, G2 buyer intent, or web scraping can surface this.
Revenue — LinkedIn doesn’t share revenue data. ZoomInfo, Apollo, and similar databases estimate it.
These gaps are exactly where the 3-column ICP framework matters most. Your Column 1 criteria (must-haves) mostly map to Sales Navigator. Your Column 2 criteria (amplifiers like tech stack, intent signals, revenue) often require enrichment — data from outside LinkedIn layered on top of your search results.
Here’s the real workflow that experienced sales teams use. It has three steps.
Use Sales Navigator (or free search) to find everyone who matches your Column 1 criteria. At this stage, you’ll have 500-2,000 companies. That’s too many to prospect manually, and not all of them are actually good fits.
Export or save these results. You now have a raw list.
This is where you add the signals LinkedIn doesn’t have. There are three ways to do this, depending on your budget and technical comfort.
Manual enrichment (free, slow — good for <50 leads): For each company on your list, spend 2-3 minutes checking: - Crunchbase — When did they last raise? How much? Who invested? - Their careers page — Are they hiring for the role you sell to? (Hiring = budget exists) - BuiltWith or Wappalyzer — What tech stack are they running? - Their blog/news — Any recent product launches, expansions, or leadership changes?
Save findings in a spreadsheet. After checking 50 companies, you’ll have maybe 15-20 that tick all boxes. This doesn’t scale, but it’s how you validate your ICP before investing in tools.
Spreadsheet enrichment (cheap, some setup — good for 50-500 leads): - Export Sales Navigator results using tools like Evaboot or PhantomBuster - Use APIs to enrich each row: Crunchbase for funding, Apollo for contact data, BuiltWith for tech stack - Build simple filters in the spreadsheet: “Show me only companies with Series A-C funding AND growing headcount AND using Salesforce” - This takes a few hours to set up but is repeatable
Automated enrichment (paid tools — good for 500+ leads): Tools like Clay automate this with waterfall enrichment — your LinkedIn search feeds through 150+ data providers, and each lead gets funding data, tech stack, hiring signals, and contact information added automatically. Apollo and ZoomInfo offer built-in databases with some of these signals.
The alternative approach is AI-powered prospecting where you describe your ICP in natural language — “Series A fintech companies in Europe with a Head of Payments” — and the AI cross-references LinkedIn, Crunchbase, job boards, and technographic data in a single query. This is the approach we built Onsa around.
Your enriched list of 150-300 companies still needs ranking. Not every match is equally likely to buy right now.
Score based on timing signals: - Hot (reach out this week): Recent funding, hiring for your buyer role, competitor mentioned on their site - Warm (reach out this month): Growing headcount, using complementary tools, active on LinkedIn discussing relevant problems - Nurture (add to sequence): Fits ICP but no timing signals yet
This scoring is what separates “I messaged 200 people and got 5 replies” from “I messaged 50 people and got 15 replies.” The response rate triples when you reach the right person at the right time.
For automated scoring, see our guide on lead qualification.
We analyzed 74 conversations where B2B founders searched for leads. Here’s what we found:
Pattern 1: Too broad, then refine. The most common first message is some version of “find leads for me” or “find prospects for [URL].” This always needs 2-3 rounds of refinement. The founder learns what they actually need by seeing what comes back wrong.
One user started with “find Creative Strategists for DTC brands that work with Meta Ads on LinkedIn — list 50 prospects.” The first results included freelancers, agency employees, and people who mentioned Meta Ads once in a post 2 years ago. After refining to “in-house Creative Strategists at DTC brands with $5M+ revenue, currently running Meta Ads campaigns,” the list became actionable.
Pattern 2: LinkedIn URL as starting point. 8.6% of conversations start with users pasting a LinkedIn profile URL and saying “find more people like this.” This is actually a smart approach — instead of defining your ICP from scratch, you start with one perfect customer and reverse-engineer the criteria. What industry are they in? What size company? What’s their title? What tools do they use?
Pattern 3: Geography matters more than people think. Users who don’t specify geography upfront always come back to add it. “Find dental clinic owners focused on dental tourism” became “…in Mexico, Turkey, and Hungary” after the first results included clinics everywhere. Geography isn’t just a filter — it changes the competitive landscape, price sensitivity, and buying behavior.
Free LinkedIn search: Good for your first 30-50 leads. Enough to test whether your ICP converts before spending money. Zero cost, high manual effort.
Sales Navigator ($99/month): Worth it the moment you need to filter by company size or seniority — which is basically the moment you have a real ICP. The headcount filter alone saves you hours of manual checking. ROI positive if you close 1 extra deal per year from better targeting.
Enrichment tools ($100-500/month): Worth it when your Column 2 criteria (funding, tech stack, hiring) are important for targeting and you’re prospecting more than 100 companies per month. The cost is trivial compared to the time saved checking Crunchbase manually for every prospect.
AI prospecting ($50-200/month): Worth it when you want to describe what you need in plain English and get a qualified list back. Best for founders and small teams who don’t have a dedicated sales ops person to configure waterfall enrichment.
The mistake most founders make: paying for Sales Navigator and then still manually checking Crunchbase for every lead. Either commit to free LinkedIn + manual enrichment, or commit to Sales Navigator + automated enrichment. The middle ground — paying for tools but still doing everything manually — is the worst of both worlds.
If you have no leads yet: 1. Build your ICP using the 3-column framework (15 minutes) 2. Run a free LinkedIn search with your Column 1 criteria 3. Manually enrich your top 30 results (2 hours) 4. Reach out to the top 10 with personalized messages 5. Track responses. Refine your ICP based on who responds.
If you have leads but low response rates: 1. Review your current list against Column 2 amplifiers (funding, hiring, tech stack) 2. Remove everyone without at least one timing signal 3. Re-prioritize based on recency of trigger events 4. A/B test your outreach messaging on the refined list
If you’re ready to scale: 1. Set up automated enrichment (Clay, Apollo, or Onsa) 2. Build a repeatable weekly workflow: search → enrich → score → outreach 3. Track conversion by ICP segment to continuously refine targeting
The founders who build pipeline fastest aren’t the ones with the biggest lead lists. They’re the ones who find 150 right companies instead of messaging 2,000 wrong ones. As we saw in 36 interviews with salespeople using AI, the top performers won by iterating faster on targeting — not by working more hours.
Is Sales Navigator worth it for a solo founder?
Yes, if you have a defined ICP with company size as a criteria (most do). The headcount filter alone justifies $99/month. Cancel it once you’ve built your initial pipeline and shift to nurturing.
How many leads should I reach out to per week?
Quality over quantity. 20-30 highly targeted, personalized outreach messages per week beats 200 generic ones. If your response rate is below 10%, your targeting is wrong — go back to your ICP, don’t increase volume.
Can I scrape LinkedIn for leads?
LinkedIn’s terms of service prohibit scraping. Tools that “export” Sales Navigator results operate in a gray area. The safer approach is using LinkedIn’s official APIs or tools that access data through authorized partnerships with data providers.
How do I know if my targeting is working?
Track three metrics: response rate (should be 15-25% for well-targeted outreach), positive response rate (should be 5-10%), and meeting booked rate (should be 2-5%). If response rate is high but meeting rate is low, your messaging is good but targeting is off. If response rate is low, your targeting needs work.
What’s better — finding more leads or better qualifying existing ones?
Almost always better qualifying. Synthetic market research shows that narrower targeting with stronger signals outperforms broad outreach in every B2B context. The economics are simple: 50 leads at 20% response = 10 conversations. 500 leads at 2% response = 10 conversations. Same output, 10x less effort.
Based on patterns from 74 real lead-finding conversations. Want to skip the manual work? Onsa.ai finds qualified leads from a single ICP description — no spreadsheets, no manual enrichment.