Using AI to Find Valid B2B Contact Emails

Here's how to find your next 3 ideal clients using just natural language in Apollo: Client #1: The Funded Startup Use this prompt: "Find Series A B2B SaaS companies that raised between $5M-$30M in the last 30 days" Apollo returns dozens of companies with: - Exact funding amounts - Investor names   - Company descriptions - Employee growth rates Next prompt: "Show me the VP of Sales and CMO at companies focused on martech or sales tech, exclude anyone who's been in role over 2 years" Result: A curated list of prospects with: - Verified work emails - Direct dial numbers - Time in current role - Previous company info Outreach prompt: "Write a 3-email sequence mentioning their Series A from [investor name], congrats on the funding, and how we help newly funded companies scale their [specific function]. Make it conversational, under 50 words per email" Apollo generates personalized sequences that actually sound human. Client #2: The Hypergrowth Company Use this prompt: "Companies between 50-200 employees that went from under 50 to over 100 employees in the past 12 months" Then: "Find RevOps, Sales Ops, or Growth leaders who joined in the last 6 months" Apollo even shows you which companies just implemented Salesforce or HubSpot (perfect timing for outreach). Client #3: The Tech Stack Match Use this prompt: "Find companies using Salesforce + HubSpot + Gong with 10-50 SDRs and over $20M in funding" This finds companies with the exact tools and team size you work best with. The game-changer: Apollo remembers your search criteria and suggests similar companies you missed. No more: - Hours of manual research - Checking multiple sources for funding news - Guessing email formats - Building lists in spreadsheets Just natural language requests and instant results. See it in action: https://bit.ly/3IkrRKz P.S. Apollo's AI Assistant is in beta. The sooner you get in, the bigger your head start. #ApolloAIAssistant #ApolloPartner

The way most people build B2B lead lists is painfully slow.   You open LinkedIn, manually search for companies, copy names into a CSV, cross-reference with Crunchbase for funding data, check their careers page for hiring signals, then try to find the right person to contact.   That's easily 2 to 3 hours for a list of 50 leads. And half the data is already outdated by the time you're done.   That's why I've been using Coresignal's AI Data Search to build lead lists in seconds instead of hours.   Coresignal pulls company and employee data from 15+ public web sources. Their AI Data Search feature lets you query all of it in plain English instead of writing complex filters or reading API docs.   You literally describe your ideal leads like you're chatting with an assistant.   1. Let me walk you through it:     2. Type what you want in plain English. Something like "SaaS companies in the US that raised funding last year and are actively hiring."     3. Get a preview of 100 matching records instantly. Company names, locations, employee counts, all the key data points.     4. Refine by adding conditions in the same chat. Want only companies with 50+ employees? Type it and the list updates.     5. Enrich with 500+ data fields per company. Funding history, tech stack, headcount trends, website traffic. All the signals that help you actually qualify whether a lead is worth reaching out to.     6. Export up to 10,000 fully enriched records as CSV or JSONL.   Some prompts I used:   → Show all companies in LA that are working with Oracle. → Discover companies using HubSpot but not Salesforce. → List AI companies with 50k+ monthly visitors that have marketing positions open.   Each one came back with structured results in seconds. And you can see the actual query the AI generated underneath, so if you want to plug into their API later at a bigger scale, the query is already there.   Coresignal has been recognized by Datarade as a top public web data provider three years in a row. Their data is multi-source, cleaned, and AI-enriched.   Try AI Data Search for free at https://lnkd.in/efBWVTcb   Over to you: What's your current lead list building process? Still manual or have you found something that actually saves time?

Clay is one of the most powerful tools in B2B right now. But most people don't know where to start. Most people open a Clay table, stare at it for 10 minutes, and close it because they don't know what to build. We've built 500+ Clay tables for clients. Here are the 5 workflows you should start with: Workflow 1: Waterfall Email Finder The problem: If you use one email finder you are getting something like a 40% finding rate. That means 60% of your list is useless. The fix: Stack multiple providers in sequence. Start with the first one (ie Prospeo.io). If the first provider doesn't find the email, try another one like LeadMagic. You can run more than 10 different providers to maximise your chance to find the right email. Result: 85-90% email findign rate vs 40% with a single provider. Workflow 2: Signal-Based List Builder The problem: You're blasting the same message to everyone. The fix: Pull companies from Ocean.io or Apollo. Then start to enrich to find relevant signals. For example: Use Claygent to scrape funding news. Track hiring spikes with LinkedIn enrichment. Check tech stack changes with BuiltWith. Score them: 3 signals = immediate outreach, 2 = nurture, 1 = long-term. Workflow 3: Inbound-led Outbound The problem: People are visiting your website but you are not capturing the intent. The fix: RB2B identifies website visitors. Push to Clay for enrichment. Find decision-makers at those companies companies. Then push the data to Instantly.ai for outreach. Workflow 4: LinkedIn Engagement -> Outreach The problem: People engage with your posts and you never follow up. The fix: Trigify.io tracks who engages with your posts. Push people engaging with your content to Clay. Enrich to find more relevant data. Do some ICP scoring and reach out to leads fitting your ICP. Workflow 5: Champion Reactivation The problem: Your past champions changed jobs and you're not tracking them. The fix: Export past customers from your CRM. Use UserGems 💎 or Clay tracks job changes. Enrich to rind the right data then reach out offering to help again. Stop staring at empty Clay tables. Build these 5 workflows first and you'll already have a good outbound engine going. Which one are you starting with?

We’ve been generating 20 outbound leads every single week for a B2B SaaS company. Here’s exactly how: 1️⃣ Target Account List Created an ICP Model including all personas & segments: → Used DiscoLike to find lookalike companies → Scraped competitor’s followers on LI → Curated a list of VC-backed companies with GetLatka 2️⃣ AI Qualification We imported CSVs to Clay for AI Qualification based on: → Industry → Location → Funding stage → CRM they’re using 3️⃣ Data Enrichment Gathered all important data points and eliminated manual research: → Created a prompt with Claygent + GPT 4o mini → Found ICP companies of each account on the target list → Created personalized snippets for our messaging 4️⃣ Contact Sourcing Found relevant contacts with Clay from each account: → Filtered based on department and job title → Cleaned first names with GPT and formulas → Found & validated their emails using Findymail → Found phone numbers using BetterContact 5️⃣ Contact Tiering Segmentation was arguably the most important thing here: → Tier 1: Champions/Users (Managers: Ops & Growth) → Tier 2: Decision Makers (C-Suite: Sales & Growth) → Split into groups based on whether they’re following the main competitor 6️⃣ Multi-channel Outreach We used a combination of email & LinkedIn outreach: → Created different copy for each of the segments → Pushed all data to Instantly.ai for email outreach → HeyReach.io as the LinkedIn sequencer Result: ↳ Email: 8-10 leads per week ↳ LinkedIn: 9-12 leads per week TL;DR: Outbound still works. My biggest takeaway: Segmentation is much more important than AI personalization. PS: Anything you’d add to make it better?