B2B Prospect Research (Exa AI)

Purpose

Automatically discover, score, and research ideal B2B prospects using Exa AI's semantic search capabilities. This skill takes your Ideal Customer Profile (ICP) criteria, searches the web for matching companies, extracts key decision-maker information, scores each prospect based on fit, and generates an outreach-ready spreadsheet with company details, contact info, and personalized talking points. Perfect for sales teams, business developers, and marketers who need to build high-quality prospect lists without spending hours on manual research.

When to Use

Use this Skill when you need to:

• Build a list of qualified B2B prospects matching your ICP
• Research target accounts for sales campaigns or ABM programs
• Find companies in specific industries, sizes, or technology stacks
• Discover decision-makers and key contacts at target companies
• Score and prioritize prospects by ICP fit
• Generate outreach-ready reports with company insights
• Validate market size or TAM for specific segments
• Find competitors' customers or users of specific technologies
• Build cold outreach lists with personalized talking points
• Research accounts before sales calls or demos

Step-by-Step Process

Step 1: Gather ICP Criteria

Define the Ideal Customer Profile to guide the prospect search.

Help the user specify their target prospect criteria:

Ask guiding questions:

• "What type of companies are you looking for?" (industry, vertical, niche)
• "What size companies?" (employees, revenue, stage)
• "What geography?" (regions, countries, cities)
• "What technology stack or tools do they use?" (if relevant)
• "Any specific characteristics?" (funding stage, growth rate, business model)
• "Do you have an existing ICP document I can reference?"

Criteria to gather:

• Industry/Vertical: Specific industries or markets
• Company Size: Employee count or revenue range
• Geography: Target locations or regions
• Technology: Tools, platforms, or tech stack they use
• Stage: Startup, growth, enterprise, public
• Other Signals: Funding events, job postings, recent news, growth indicators

Example ICP criteria:

• "Series A-B SaaS companies, 20-100 employees, using Salesforce and HubSpot, based in North America"
• "Ecommerce brands doing $5M-50M revenue, Shopify-based, direct-to-consumer model"

Tools Used: basile_search_documents (check for existing ICP), basile_get_document (read ICP if exists) Output Variable: icp_criteria

Step 2: Search for Prospect Companies

Use Exa AI to find companies matching the ICP criteria.

Execute semantic search to discover target companies:

Search strategy:

• Craft multiple Exa queries to cover different angles
• Use semantic search (neural mode) for concept matching
• Filter by domains if targeting specific industry sites
• Set appropriate date ranges for recency
• Request 15-30 results per query for good coverage

Example Exa queries:

• "Series A B2B SaaS companies that raised funding in 2024 2025"
• "Fast-growing ecommerce brands using Shopify Plus"
• "Marketing technology companies with 50-200 employees using Salesforce"
• "Fintech startups that recently posted engineering jobs"

Search parameters:

• num_results: 20-30 per query (balance coverage vs quality)
• search_type: "neural" (semantic understanding)
• include_domains: Relevant sites (Crunchbase, company blogs, news sites)
• start_published_date: Recent content (last 6-24 months)

Parse results:

• Extract company names and websites
• Identify company descriptions and key details
• Note signals of ICP fit (funding, size, tech stack mentions)
• Deduplicate if companies appear in multiple searches

Tools Used: exa_search Output Variable: discovered_companies Context Used: icp_criteria

Step 3: Enrich Prospect Data

Research each company to gather detailed information.

For each discovered company, gather comprehensive data:

Company information to collect:

• Company Name: Full legal or brand name
• Website: Primary domain
• Industry: Specific vertical or sector
• Size: Employee count (from LinkedIn, company pages)
• Location: Headquarters and office locations
• Funding: Stage, total raised, recent rounds
• Technology Stack: Tools and platforms they use (from job posts, tech blogs)
• Recent News: Product launches, funding, expansions, partnerships
• Growth Signals: Job postings, company blog activity, news mentions

Decision-maker information:

• Key Roles: CEO, VP Sales, Head of Marketing, CTO (depending on your buyer)
• Names: If publicly available on LinkedIn, team pages, about pages
• LinkedIn Profiles: Professional background and contact info
• Recent Activity: Posts, articles, interviews, conference talks

Use additional Exa searches per company:

• "[Company name] leadership team"
• "[Company name] technology stack tools"
• "[Company name] recent funding news"

Tools Used: exa_search (for each company) Output Variable: enriched_prospects Context Used: discovered_companies, icp_criteria

Step 4: Score and Prioritize Prospects

Rank prospects by ICP fit and readiness to buy.

Score each prospect across multiple dimensions:

ICP Fit Score (0-100):

• Size Match (20 points): Company size matches ICP range
• Industry Match (20 points): In target vertical/industry
• Geography Match (15 points): Located in target regions
• Technology Match (15 points): Uses relevant tech stack
• Stage Match (10 points): At right company stage (startup, growth, etc.)
• Other Criteria (20 points): Custom ICP criteria met

Buying Intent Signals (0-100):

• Recent Funding (25 points): Raised money in last 12 months
• Job Postings (25 points): Hiring in relevant departments
• Growth Indicators (20 points): Expanding team, new offices, news mentions
• Technology Changes (15 points): Adopting new tools, switching platforms
• Leadership Changes (15 points): New decision-makers in key roles

Combined Priority Score:

• A-Tier (80-100): Excellent fit + high intent
• B-Tier (60-79): Good fit or medium intent
• C-Tier (40-59): Moderate fit, worth nurturing
• D-Tier (<40): Poor fit, deprioritize

Prioritize top 20-30 prospects for outreach:

• Rank by combined score
• Consider effort required (is decision-maker info available?)
• Flag any red flags or disqualifiers

Output Variable: scored_prospects Context Used: enriched_prospects, icp_criteria

Step 5: Generate Prospect Research Report

Create a comprehensive spreadsheet document with outreach-ready data.

Generate a structured sheet document titled "B2B Prospect Research - [Date]":

Sheet Structure (Table Format):

| Priority | Company Name | Website | Industry | Size | Location | ICP Score | Intent Score | Decision Maker | Role | LinkedIn | Key Talking Points | Tech Stack | Recent News | Next Steps | |----------|--------------|---------|----------|------|----------|-----------|--------------|----------------|------|----------|-------------------|------------|-------------|------------|

For each prospect, include:

Company Overview:

• Company name and website
• Industry/vertical
• Company size (employees)
• Location (HQ and offices)
• Funding stage and total raised

Scoring:

• ICP Fit Score (0-100)
• Buying Intent Score (0-100)
• Priority Tier (A/B/C/D)

Decision Maker Info:

• Name (if available)
• Role/title
• LinkedIn profile URL
• Contact info (if publicly available)
• Brief background

Outreach Intelligence:

• Key Talking Points: 3-5 personalized hooks based on company research
• Recent funding/growth
• Technology they use that relates to your solution
• Pain points or challenges they likely face
• Mutual connections or shared interests
• Tech Stack: Tools they currently use
• Recent News: Latest announcements, launches, or milestones
• Next Steps: Recommended outreach approach (email, LinkedIn, warm intro)

Document Sections:

1. Executive Summary:

• Total prospects researched
• A-Tier count (immediate outreach)
• B-Tier count (warm nurture)
• Key insights or patterns

1. A-Tier Prospects (Top Priority):

• Full prospect table sorted by score
• Detailed talking points for each
• Recommended outreach sequence

1. B-Tier Prospects (Secondary):

• Prospect table with key info
• Nurture recommendations

1. C-Tier Prospects (Long-term):

• Company list for future reference

1. Research Methodology:

• ICP criteria used
• Exa search queries executed
• Scoring methodology
• Data sources

Format as:

• Markdown table for easy copy/paste to spreadsheet
• Actionable talking points for each prospect
• Clear priority indicators
• Ready for immediate outreach

Tools Used: basile_create_document Context Used: scored_prospects, enriched_prospects, icp_criteria

Example Usage

User: > I need to find 20 B2B SaaS companies that would be a good fit for our sales intelligence tool. They should be Series A/B, 30-150 employees, using Salesforce, and based in the US.

Claude (using this Skill): > I'll use the B2B Prospect Research Skill to discover and research ideal prospects for you. > > Step 1: Gather ICP Criteria > Got it! Your ICP: > - Industry: B2B SaaS > - Stage: Series A/B > - Size: 30-150 employees > - Tech Stack: Using Salesforce > - Geography: United States > - Buyer Persona: VP Sales, CRO, or Head of Revenue Operations > > Step 2: Search for Prospect Companies > Using Exa AI to search for matching companies... > [Executed 3 semantic searches: > 1. "Series A B2B SaaS companies 50-150 employees Salesforce 2024" > 2. "Fast-growing sales teams SaaS startups using CRM United States" > 3. "B2B software companies raised Series A funding sales operations"] > > Found 32 companies matching your criteria. > > Step 3: Enrich Prospect Data > Researching each company for detailed information... > [For each company: Gathered size, funding, decision makers, tech stack, recent news] > Enriched 32 prospects with company details and decision-maker info. > > Step 4: Score and Prioritize Prospects > Scoring prospects by ICP fit and buying intent... > - A-Tier (Immediate outreach): 12 companies (ICP 85-95, Intent 70-90) > - B-Tier (Warm nurture): 14 companies (ICP 70-84, Intent 50-75) > - C-Tier (Long-term): 6 companies (ICP 60-69, Intent 30-50) > > Top 3 prospects: > 1. Acme SaaS (ICP: 92, Intent: 85) - Series A, 85 employees, raised $12M, VP Sales on LinkedIn > 2. TechFlow (ICP: 90, Intent: 78) - Series B, 120 employees, hiring sales ops roles, CRO active on LinkedIn > 3. DataCorp (ICP: 88, Intent: 82) - Series A, 65 employees, recent product launch, Head of Revenue Ops findable > > Step 5: Generate Prospect Research Report > Creating your outreach-ready prospect sheet... > > Done! Your B2B Prospect Research Report is ready with 32 prospects. > > Quick Stats: > - 12 A-Tier prospects ready for immediate outreach > - VP Sales / CRO contact info for 10 of them > - Personalized talking points for each (recent funding, tech stack, growth signals) > - Recommended outreach sequence included > > Top Talking Point Example (Acme SaaS): > - "Congrats on the $12M Series A from Sequoia last month" > - "Noticed you're using Salesforce but hiring 3 sales ops roles - sign of scaling pains?" > - "Other fast-growing SaaS sales teams like yours use our tool to reduce CRM data entry by 60%" > > Your prospect sheet includes company details, decision makers, LinkedIn profiles, talking points, and next steps. Ready to start outreach!

Tips for Best Results

• Start with clear ICP criteria - the more specific, the better the matches
• Reference your ICP document if you have one (I can search for it)
• Request 20-30 prospects for a good balance of quality and quantity
• Specify your buyer persona (VP Sales, CMO, CTO) so I find the right decision makers
• Include technology stack in ICP if relevant - great signal for search
• Mention any specific signals (recent funding, job postings, tech changes)
• Review A-Tier prospects first - they're highest priority for outreach
• Use talking points as inspiration, personalize further for best results
• Update and re-run monthly to keep your pipeline fresh
• Combine with your CRM data to avoid duplicates
• Export the markdown table to Google Sheets or Excel for team sharing
• Validate contact info before outreach (email verification tools)
• Consider warm intros over cold outreach when possible

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Created with Basile.ai