Your sales team just spent 40 hours manually researching 200 prospects. They found company names, guessed at email addresses, copy-pasted LinkedIn profiles into spreadsheets.
Total cost: $2,000 in labor. Result: 200 half-complete records with 60% bad email addresses.
There's a better way.
Professional data APIs can enrich those same 200 prospects in under 30 seconds. With accurate data. For less than $10.
But here's the problem: there are hundreds of data APIs out there. LinkedIn APIs, company intelligence platforms, contact enrichment services, B2B databases—the options are overwhelming.
Which ones actually work? Which are worth the money? Which will give you accurate, up-to-date data instead of stale garbage from 2019?
This guide covers everything you need to know about professional data APIs in 2025. We'll break down the different types, compare the top providers, show you real code examples, and help you choose the right solution for your use case.
By the end, you'll know exactly which APIs to use for LinkedIn data, company intelligence, and B2B enrichment—and how to implement them in under 30 minutes.
What Are Professional Data APIs?
Professional data APIs are services that let you programmatically access business information about people, companies, and organizations.
Instead of manually researching prospects or scraping websites, you make an API call and get structured data back instantly.
The Basics
A typical workflow looks like this:
1# Traditional manual research
2# 1. Google the person
3# 2. Find their LinkedIn
4# 3. Copy-paste their info
5# 4. Search for their email
6# 5. Verify the email works
7# Time: 10-15 minutes per person
8
9# With a professional data API
10import requests
11
12response = requests.post('https://api.example.com/enrich',
13 json={'linkedin_url': 'linkedin.com/in/username'}
14)
15
16data = response.json()
17# You now have: name, title, company, email, phone, location
18# Time: 0.5 secondsThe difference: 10-15 minutes → 0.5 seconds per record.
What Data Can You Get?
Professional data APIs provide:
Personal Information:
- Full name and current title
- LinkedIn profile URL and details
- Work history and education
- Skills and endorsements
- Professional bio and headline
- Location and timezone
Company Information:
- Company name and website
- Industry and size (employee count)
- Revenue and funding
- Technologies used
- Office locations
- Leadership team
Contact Information:
- Business email addresses
- Phone numbers (direct lines)
- Social media profiles
- Alternative contact methods
Intent & Signals:
- Job changes (hiring signals)
- Company growth indicators
- Technology adoption patterns
- Funding announcements
- Recent news and mentions
Why APIs Instead of Databases?
Traditional approach (CSV databases):
- Data goes stale within weeks
- No real-time updates
- Pay for data you don't use
- Limited to what's in the database
API approach:
- Fresh data on-demand
- Real-time enrichment
- Pay per API call (only what you use)
- Access to the latest information
Example: A database might tell you someone works at "Google" based on data from 2023. An API will tell you they moved to "Meta" last month.
Why Businesses Need Data APIs
Let's be honest about what manual research actually costs:
The Real Cost of Manual Research
Sales team scenario:
- Average sales rep salary: $60,000/year ($30/hour)
- Time per prospect research: 15 minutes
- Prospects researched per day: 20
- Time spent researching: 5 hours/day
Annual cost: $30/hour × 5 hours/day × 250 days = $37,500 per rep
And that's just the labor. Add:
- Incomplete data → missed opportunities
- Outdated emails → high bounce rates
- Manual errors → bad targeting
- Slow process → competitors move faster
Total business impact: $50,000-100,000 per rep in lost productivity and missed deals.
What APIs Solve
1. Speed: 1,000 enrichments in minutes vs. weeks
1# Manual: 1,000 prospects × 15 minutes = 250 hours (6+ weeks)
2# API: 1,000 prospects × 0.5 seconds = 8 minutes
3
4# Time savings: 249 hours, 52 minutes
5# Cost savings: $7,470 in labor (at $30/hour)2. Accuracy: 80-95% data accuracy vs. 40-60% manual research
- Humans make typos
- Humans miss LinkedIn updates
- Humans guess at email formats
- APIs pull fresh, verified data
3. Scale: Handle 10K prospects as easily as 10
1# Manual research doesn't scale
2# 10 prospects: manageable
3# 100 prospects: painful
4# 1,000 prospects: impossible
5# 10,000 prospects: hire more people
6
7# APIs scale infinitely
8# 10 or 10,000 = same effort (just more API calls)4. Consistency: Same data format every time
1Manual research output:
2- "Software Engineer at Google"
3- "SWE @ Google Inc."
4- "Google - Software Developer"
5(Three different formats for the same thing)
6
7API output:
8{
9 "title": "Software Engineer",
10 "company": "Google",
11 "company_id": "google"
12}
13(Consistent, structured, parseable)ROI Calculation
Typical business case:
1Manual approach:
2- 1 sales rep researching full-time
3- Salary + benefits: $75,000/year
4- Enriches ~4,000 prospects/year
5- Cost per enrichment: $18.75
6
7API approach:
8- API cost: $0.02-0.10 per enrichment
9- 4,000 enrichments: $80-400/year
10- Time saved: 4,000 hours (reallocate to selling)
11
12Annual savings: $74,600-74,920
13ROI: 18,650-93,650%And your rep can now focus on selling instead of researching.
Types of Professional Data APIs
Not all data APIs are the same. Here are the main categories:
1. LinkedIn Data APIs
What they do: Access LinkedIn profile and company data
Use cases:
- Prospect enrichment
- Lead qualification
- Talent sourcing
- Competitive intelligence
Data you get:
- Profile information (name, title, location)
- Work history and education
- Skills and endorsements
- Company data (size, industry, employees)
- Job postings
- Company updates and news
Best for: B2B sales, recruiting, market research
Examples: LinkdAPI, Proxycurl (shut down), RapidAPI LinkedIn scrapers
Pricing: $0.015-0.10 per profile
2. Company Intelligence APIs
What they do: Provide company firmographic and technographic data
Use cases:
- Account-based marketing (ABM)
- Market segmentation
- Lead scoring
- Competitive analysis
Data you get:
- Company basics (name, industry, size, revenue)
- Technologies used (tech stack)
- Funding and investors
- News and events
- Growth signals
- Office locations
Best for: Enterprise sales, marketing operations, investors
Examples: Clearbit, ZoomInfo, Crunchbase
Pricing: $0.05-0.50 per company lookup
3. Contact Enrichment APIs
What they do: Find and verify email addresses and phone numbers
Use cases:
- Email outreach campaigns
- Contact data cleaning
- Lead generation
- CRM enrichment
Data you get:
- Business email addresses
- Personal email addresses (with permission)
- Direct phone numbers
- Email verification status
- Confidence scores
Best for: Sales development, email marketing, recruiting
Examples: Hunter.io, Snov.io, Clearbit
Pricing: $0.01-0.10 per email lookup
4. Technographic APIs
What they do: Identify technologies companies are using
Use cases:
- Competitor tracking
- Market analysis
- Sales targeting (tech-based segmentation)
- Product positioning
Data you get:
- Tech stack (CMS, analytics, marketing tools)
- Cloud infrastructure
- E-commerce platforms
- Programming languages and frameworks
- Install dates and usage patterns
Best for: SaaS sales, product marketing, competitive intelligence
Examples: BuiltWith, Datanyze, HG Insights
Pricing: $0.10-1.00 per company tech scan
5. Intent Data APIs
What they do: Track buying signals and research behavior
Use cases:
- Sales prioritization
- Lead scoring
- Marketing campaign targeting
- Account-based sales
Data you get:
- Website visitor tracking
- Content consumption patterns
- Search behavior
- Engagement signals
- Topic interests
- Purchase intent scores
Best for: B2B marketing, sales operations, account managers
Examples: Bombora, 6sense, Cognism
Pricing: Subscription-based, $1,000-10,000+/month
6. Database APIs (Traditional B2B Data)
What they do: Access large pre-built databases of contacts and companies
Use cases:
- List building
- Market research
- Prospecting at scale
- Territory planning
Data you get:
- Contact directories (millions of records)
- Searchable by criteria (title, industry, location)
- Historical data
- Static snapshots
Best for: High-volume prospecting, market research firms
Examples: ZoomInfo, Apollo.io, LeadIQ
Pricing: Subscription-based, $5,000-50,000+/year
7. Social Media APIs
What they do: Access public social media data beyond LinkedIn
Use cases:
- Brand monitoring
- Influencer research
- Audience analysis
- Social selling
Data you get:
- Twitter/X profiles and posts
- GitHub activity (for developers)
- Medium articles (for thought leaders)
- Social engagement metrics
Best for: Marketing, PR, developer relations
Examples: Twitter API, GitHub API, various unofficial scrapers
Pricing: Free to $100+/month depending on volume
LinkedIn Data APIs: Complete Overview
LinkedIn is the gold standard for professional data. Here's everything you need to know about LinkedIn APIs in 2025.
Official vs Unofficial APIs
Official LinkedIn API (LinkedIn Marketing Developer Platform):
What you CAN'T do:
- ❌ Search for profiles
- ❌ Scrape profile data
- ❌ Access public profiles without consent
- ❌ Get email addresses
- ❌ View connection lists
What you CAN do:
- ✅ Post to your own company page
- ✅ Access your own profile data
- ✅ Manage ad campaigns
- ✅ Get analytics for your pages
Reality: The official API is extremely limited. It's designed for posting content and managing ads, not for data extraction.
Unofficial LinkedIn APIs:
These services access LinkedIn's public data programmatically. They don't use the official API, but they're how most businesses actually get LinkedIn data.
What they provide:
- ✅ Profile data (name, title, experience, education)
- ✅ Company information
- ✅ Job postings
- ✅ Search functionality
- ✅ Employee lists
- ✅ Posts and engagement data
Legal status: The hiQ Labs vs. LinkedIn case (2022) established that scraping publicly available data is legal under the CFAA. Unofficial APIs operate in this legal framework.
Top LinkedIn APIs in 2025
Here are the major players:
1. LinkdAPI
What it is: Comprehensive unofficial LinkedIn API focused on B2B data enrichment
Strengths:
- 30+ endpoints covering profiles, companies, jobs, posts
- Fast (200ms average response time)
- Async support for bulk operations
- Account-less architecture (no ban risk)
- Real-time data (not cached)
Data you get:
1{
2 "fullName": "Ryan Roslansky",
3 "headline": "CEO at LinkedIn",
4 "location": {
5 "country": "United States",
6 "city": "San Francisco Bay Area"
7 },
8 "CurrentPositions": [{
9 "name": "LinkedIn",
10 "title": "Chief Executive Officer",
11 "startDate": "June 2020"
12 }],
13 "followerCount": 500000,
14 "connectionCount": 500
15}Pricing: $49-399/month (5K-100K credits)
- $0.03/profile at entry tier
- $0.015/profile at scale
Best for: Sales teams, recruiters, B2B marketers needing reliable LinkedIn data
Free trial: 100 credits, no credit card required
Code example:
1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4profile = api.get_profile_overview("ryanroslansky")
5
6print(f"Name: {profile['data']['fullName']}")
7print(f"Title: {profile['data']['headline']}")2. Proxycurl
Status: ⚠️ SHUT DOWN (July 2025)
What happened: LinkedIn sued Proxycurl in January 2025 for creating "hundreds of thousands of fake accounts" to scrape data. Proxycurl shut down operations in July 2025.
Why it matters: This was the most popular LinkedIn API ($10M ARR). Its shutdown created a market gap that other providers are filling.
Lesson: Using fake accounts to scrape LinkedIn is not sustainable. Look for APIs that access public data without violating LinkedIn's ToS.
3. RapidAPI LinkedIn Scrapers
What it is: Marketplace with multiple LinkedIn scraping APIs
Strengths:
- Multiple providers to choose from
- Unified API interface
- Pay-as-you-go pricing
- Good for testing different providers
Weaknesses:
- Variable quality between providers
- Some have low rate limits
- Customer support depends on provider
- Reliability can be inconsistent
Pricing: $0.01-0.10 per request depending on provider
Best for: Developers who want to test multiple APIs easily
4. Bright Data (formerly Luminati)
What it is: Enterprise web scraping infrastructure with LinkedIn capabilities
Strengths:
- Massive proxy network (72M+ IPs)
- High success rates
- Can handle large-scale scraping
- Legal team fought LinkedIn and won
Weaknesses:
- Very expensive ($500-50,000+/month)
- Complex setup
- Requires technical expertise
- Overkill for most use cases
Pricing: Enterprise only, starting at $500/month minimum
Best for: Large enterprises with big budgets and technical teams
5. ScraperAPI
What it is: General web scraping API that works with LinkedIn
Strengths:
- Works for any website (not just LinkedIn)
- Good for multi-site scraping
- Handles proxies and CAPTCHAs
Weaknesses:
- Not LinkedIn-optimized
- Returns raw HTML (you parse it)
- Slower than specialized APIs
- Higher cost per LinkedIn profile
Pricing: $0.024-0.037 per LinkedIn profile
Best for: Teams scraping multiple websites, not just LinkedIn
LinkedIn API Comparison Table
| Feature | LinkdAPI | Proxycurl | RapidAPI | Bright Data | ScraperAPI |
|---|---|---|---|---|---|
| Status | ✅ Active | ❌ Shut down | ✅ Active | ✅ Active | ✅ Active |
| Profile data | ✅ Full | N/A | ✅ Varies | ✅ Full | ✅ HTML only |
| Company data | ✅ Yes |
What to Look for in a LinkedIn API
Must-haves:
- Real-time data (not 6-month-old cache)
- Fast response times (<1 second per profile)
- Structured JSON output (not HTML you have to parse)
- Reliable uptime (99%+ availability)
- Good documentation (clear examples)
- Reasonable pricing ($0.01-0.05 per profile)
Nice-to-haves:
- Async support for bulk operations
- Webhooks for real-time updates
- Data enrichment beyond LinkedIn
- Integration with CRMs
- Customer support that responds
Red flags:
- Requires you to provide LinkedIn account credentials
- No clear pricing information
- Poor documentation or no code examples
- History of downtime or legal issues
- Extremely cheap (usually means stale data or fake accounts)
Company Intelligence APIs
Beyond LinkedIn, you need company-level data for account-based strategies.
What Company Intelligence Provides
Firmographic data:
- Company name, website, industry
- Employee count and growth rate
- Revenue and funding history
- Office locations and subsidiaries
- Leadership team and key contacts
Technographic data:
- Technologies used (CRM, marketing tools, infrastructure)
- Tech stack changes over time
- IT budget estimates
- Technology decision-makers
Intent signals:
- Website visitor tracking
- Content consumption patterns
- Product research activity
- Buying committee engagement
Top Company Intelligence APIs
1. Clearbit
What it is: Real-time company and contact enrichment
Strengths:
- Clean, accurate data (85%+ accuracy)
- Real-time enrichment
- Good integration ecosystem
- Used by major SaaS companies
Data you get:
1{
2 "name": "Stripe",
3 "domain": "stripe.com",
4 "category": {
5 "industry": "Financial Services",
6 "subIndustry": "Payments"
7 },
8 "employees": 7000,
9 "estimatedAnnualRevenue": "7B",
10 "tech": ["AWS", "Kubernetes", "React"],
11 "location": "San Francisco, CA"
12}Pricing: $99-999+/month, $0.05-0.20 per lookup
Best for: SaaS companies, B2B marketing teams
2. ZoomInfo
What it is: Massive B2B contact and company database
Strengths:
- Huge database (100M+ contacts, 20M+ companies)
- Intent data included
- Direct phone numbers
- Sales Navigator integration
Weaknesses:
- Very expensive ($15,000-50,000+/year)
- Annual contracts only
- Data quality varies by region
- UI can be clunky
Pricing: Enterprise only, typically $15K-50K/year
Best for: Large sales organizations with big budgets
3. Crunchbase
What it is: Startup and funding database
Strengths:
- Comprehensive funding data
- Investor information
- Acquisition history
- News and announcements
Data focus: Startups and private companies
Pricing: $29-299/month, API access at higher tiers
Best for: VCs, startup sales teams, market researchers
4. BuiltWith
What it is: Technology tracking and market intelligence
Strengths:
- Identifies 80,000+ technologies
- Historical technology data
- Market share reports
- Lead generation by tech stack
Use case example: Find all companies using Salesforce + Marketo but not HubSpot (potential customers for your SaaS integration)
Pricing: $295-995/month
Best for: SaaS sales teams targeting by technology
5. Apollo.io
What it is: Sales intelligence and engagement platform
Strengths:
- 250M+ contacts
- Built-in email sequencing
- Good for outbound sales
- Chrome extension
Weaknesses:
- Data quality issues (60-70% accuracy)
- Aggressive sales tactics
- Some GDPR concerns
Pricing: Free tier available, $49-149+/month
Best for: SDR teams doing high-volume outbound
Company API Comparison
| Feature | Clearbit | ZoomInfo | Crunchbase | BuiltWith | Apollo |
|---|---|---|---|---|---|
| Company data | ✅ Excellent | ✅ Excellent | ✅ Good | ✅ Good | ✅ Good |
| Contact data | ✅ Yes | ✅ Yes | ⚠️ Limited | ❌ No | ✅ Yes |
| Tech stack | ✅ Yes |
Contact Enrichment & Email Finding APIs
You have a LinkedIn profile. Now you need their email address.
How Email Finding Works
Method 1: Pattern matching
1# LinkedIn shows: John Smith, works at Acme Corp (acme.com)
2# Guess email patterns:
3# - [email protected]
4# - [email protected]
5# - [email protected]
6# - [email protected]
7
8# Accuracy: 40-60% (lots of guessing)Method 2: Database lookup
1# Service has pre-crawled millions of emails
2# Look up in database by name + company
3# Return if found
4
5# Accuracy: 70-80% (but data gets stale)Method 3: Real-time verification
1# Generate pattern + verify with email server
2# SMTP verification without sending email
3# Confirm email exists and accepts mail
4
5# Accuracy: 85-95% (best method)Top Email Finding APIs
1. Hunter.io
What it is: Email finder and verifier
Strengths:
- Large database (100M+ emails)
- Email pattern detection
- Domain search (find all emails at a company)
- Real-time verification
- Chrome extension
Data you get:
1{
2 "email": "[email protected]",
3 "confidence": 92,
4 "sources": [
5 {"domain": "github.com", "uri": "https://github.com/jsmith"},
6 {"domain": "twitter.com", "uri": "https://twitter.com/jsmith"}
7 ],
8 "verification": {
9 "status": "valid",
10 "mx_records": true,
11 "smtp_check": true
12 }
13}Pricing: Free tier (25 searches/month), $49-399/month
Best for: Sales teams, recruiters, marketers
2. Snov.io
What it is: Email finder with drip campaigns
Strengths:
- Email finder + verifier
- LinkedIn email extraction
- Built-in email outreach
- Good for cold email campaigns
Pricing: $39-189/month
Best for: SDRs doing email outreach
3. ContactOut
What it is: Email and phone number finder focused on recruiters
Strengths:
- High accuracy (90%+ claimed)
- Phone numbers included
- Chrome extension for LinkedIn
- Popular with recruiters
Pricing: $99-399/month
Best for: Recruiters, talent acquisition
4. Lusha
What it is: B2B contact database with Chrome extension
Strengths:
- Large database
- Phone numbers included
- Salesforce integration
- Good UI/UX
Weaknesses:
- Expensive at scale
- Credits system can be confusing
Pricing: Free tier (5 credits), $39-99+/month
Best for: Sales teams with CRM integrations
5. RocketReach
What it is: Contact information database
Strengths:
- 700M+ profiles
- Phone numbers and emails
- Social media profiles
- Bulk lookup
Pricing: $49-249/month
Best for: Recruiters, sales teams
Email API Comparison
| Feature | Hunter | Snov.io | ContactOut | Lusha | RocketReach |
|---|---|---|---|---|---|
| Email finding | ✅ Excellent | ✅ Good | ✅ Excellent | ✅ Good | ✅ Good |
| Verification | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ⚠️ Basic |
| Phone numbers | ❌ No |
Combining LinkedIn + Email APIs
Best practice: Use multiple APIs together
1from linkdapi import LinkdAPI
2import requests
3
4# Step 1: Get LinkedIn data
5linkedin = LinkdAPI("your_api_key")
6profile = linkedin.get_profile_overview("username")
7
8name = profile['data']['fullName']
9company = profile['data']['CurrentPositions'][0]['name']
10company_domain = profile['data']['CurrentPositions'][0]['companyUrl']
11
12# Step 2: Find email with Hunter.io
13hunter_response = requests.get(
14 'https://api.hunter.io/v2/email-finder',
15 params={
16 'domain': company_domain,
17 'first_name': name.split()[0],
18 'last_name': name.split()[-1],
19 'api_key': 'your_hunter_key'
20 }
21)
22
23email = hunter_response.json()['data']['email']
24
25# Step 3: Enrich your CRM
26enriched_lead = {
27 'name': name,
28 'title': profile['data']['headline'],
29 'company': company,
30 'email': email,
31 'linkedin': f"linkedin.com/in/{username}",
32 'location': profile['data']['location']['fullLocation']
33}
34
35# Now you have a complete lead profileCost: $0.03 (LinkedIn) + $0.02 (email) = $0.05 per complete lead
Compare to: ZoomInfo charges $0.50-2.00 per lead
Top 15 Professional Data APIs Compared
Here's the complete comparison of all major APIs:
The Complete Comparison Table
| API | Category | Data Type | Pricing | Accuracy | Best For | Trial |
|---|---|---|---|---|---|---|
| LinkdAPI | Profiles, companies, jobs | $49-399/mo | 90-95% | B2B teams | 100 credits | |
| Hunter.io | Email finding + verify | $49-399/mo | 85-90% | Sales, recruiting |
Pricing Breakdown by Volume
1,000 profile enrichments/month:
- LinkdAPI: $49/month ($0.049 per profile)
- Hunter + LinkdAPI: $98/month (complete leads)
- Apollo.io: Free-$49/month
- ZoomInfo: ~$1,250/month ($15K annual / 12)
10,000 profile enrichments/month:
- LinkdAPI: $149/month ($0.015 per profile)
- Hunter + LinkdAPI: $247/month
- Apollo.io: $99-149/month
- ZoomInfo: ~$2,500/month ($30K annual / 12)
100,000 profile enrichments/month:
- LinkdAPI: $399/month ($0.004 per profile)
- Bright Data: $5,000-10,000/month
- ZoomInfo: $4,000+/month
- Apollo.io: Custom pricing
Use Cases by Industry
Different industries use professional data APIs differently:
B2B Sales & SDR Teams
Goal: Find and enrich leads, get contact info
Typical workflow:
1# 1. Get list of target companies from your ICP
2target_companies = ["stripe", "shopify", "square"]
3
4# 2. Find employees at target titles
5for company in target_companies:
6 # Search LinkedIn for decision-makers
7 results = linkedin_api.search_profiles(
8 company=company,
9 title="Head of Sales OR VP Sales"
10 )
11
12 # 3. Enrich with contact info
13 for profile in results:
14 email = email_api.find_email(profile['name'], company)
15 phone = phone_api.find_phone(profile['linkedin_url'])
16
17 # 4. Add to CRM
18 crm.create_lead({
19 'name': profile['name'],
20 'title': profile['title'],
21 'company': company,
22 'email': email,
23 'phone': phone,
24 'source': 'API enrichment'
25 })Best APIs:
- LinkdAPI (LinkedIn profiles)
- Hunter.io (emails)
- Clearbit (company data)
- ZoomInfo (all-in-one, if budget allows)
ROI: 20-50x (spend $100/month, generate $2,000-5,000 in pipeline)
Recruiting & Talent Acquisition
Goal: Find passive candidates, get contact info
Typical workflow:
1# 1. Search for candidates matching job requirements
2candidates = linkedin_api.search_profiles(
3 title="Senior Software Engineer",
4 location="San Francisco Bay Area",
5 current_company="NOT our-company"
6)
7
8# 2. Filter by specific skills
9for candidate in candidates:
10 profile = linkedin_api.get_profile(candidate['username'])
11
12 if "Python" in profile['skills'] and "AWS" in profile['skills']:
13 # 3. Get contact info
14 email = contactout.find_email(profile)
15
16 # 4. Add to ATS
17 ats.create_candidate(profile, email)
18
19 # 5. Send outreach
20 email_campaign.add_recipient(email)Best APIs:
- LinkdAPI (profiles and skills)
- ContactOut (emails + phone)
- RocketReach (high volume contact finding)
ROI: 5-10x (faster sourcing, higher response rates)
Marketing & ABM Teams
Goal: Target accounts, personalize campaigns
Typical workflow:
1# 1. Identify target accounts
2target_accounts = clearbit.discover(
3 industry="SaaS",
4 employees="100-500",
5 tech_stack=["Salesforce", "Marketo"],
6 not_using=["HubSpot"]
7)
8
9# 2. Build contact list at each account
10for account in target_accounts:
11 # Find decision-makers
12 contacts = linkedin_api.search_profiles(
13 company=account['name'],
14 title="CMO OR VP Marketing OR Director Marketing"
15 )
16
17 # 3. Enrich with intent data
18 intent_score = intent_api.get_buying_signals(account['domain'])
19
20 # 4. Prioritize by intent
21 if intent_score > 70:
22 # High intent - immediate outreach
23 for contact in contacts:
24 personalized_ad.show_to(contact)
25 email_campaign.add_to_priority(contact)Best APIs:
- Clearbit (company intel)
- LinkdAPI (contact discovery)
- BuiltWith (technographics)
- 6sense/Bombora (intent data)
ROI: 10-30x (better targeting, higher conversion)
Investors & VCs
Goal: Track startups, find founders, analyze markets
Typical workflow:
1# 1. Find recently funded startups
2startups = crunchbase.search(
3 funding_round="Series A",
4 date_from="2024-01-01",
5 industry="AI/ML"
6)
7
8# 2. Analyze team
9for startup in startups:
10 team = linkedin_api.get_company_employees(startup['name'])
11
12 # Key questions:
13 # - Who are the founders?
14 # - Where did they work before?
15 # - Who are the key hires?
16
17 founders = [p for p in team if 'Founder' in p['title']]
18
19 # 3. Check company growth
20 employee_growth = len(team) - startup['employees_6mo_ago']
21
22 # 4. Identify warm intros
23 for founder in founders:
24 mutual_connections = find_mutual_connections(founder)Start building with 100 free credits
Access profiles, companies, jobs, and more through our reliable, high-performance API. No credit card required.
Best APIs:
- Crunchbase (funding data)
- LinkdAPI (team analysis)
- Clearbit (company intelligence)
ROI: Hard to quantify, but saves 10-20 hours/week on research
Market Research Firms
Goal: Analyze industries, track trends, generate reports
Typical workflow:
1# Research question: "How is AI adoption in healthcare companies?"
2
3# 1. Identify healthcare companies
4healthcare_companies = clearbit.search(
5 industry="Healthcare",
6 employees="500+"
7)
8
9# 2. Check tech adoption
10ai_adopters = []
11for company in healthcare_companies:
12 tech_stack = builtwith.get_technologies(company['domain'])
13
14 if any(ai_tool in tech_stack for ai_tool in ['TensorFlow', 'AWS SageMaker', 'Azure ML']):
15 ai_adopters.append(company)
16
17# 3. Analyze hiring trends
18for company in ai_adopters:
19 jobs = linkedin_api.get_company_jobs(company['name'])
20 ai_jobs = [j for j in jobs if 'AI' in j['title'] or 'Machine Learning' in j['title']]
21
22 # Are they hiring AI talent?
23 company['ai_hiring'] = len(ai_jobs)
24
25# 4. Generate report
26report = {
27 'total_surveyed': len(healthcare_companies),
28 'ai_adoption_rate': len(ai_adopters) / len(healthcare_companies),
29 'top_ai_tools': most_common_tools(ai_adopters),
30 'hiring_trends': summarize_hiring(ai_adopters)
31}:
