I've been building web scraping actors on Apify for a while now. Over 170 public actors, 93 MCP intelligence servers, the whole thing. But one actor keeps outperforming everything else in my portfolio: Website Contact Scraper.
It's hit 11,516 runs, 236 users, and a 99.8% success rate. Not the most exciting numbers you'll ever hear, but for a single-purpose scraping tool on Apify's marketplace? That's solid traction. And the reason it works is dead simple: it does one thing well.
You give it a list of business website URLs. It gives you back emails, phone numbers, team member names with job titles, and social media links. One structured JSON record per domain. No login required, no API keys to configure, no code to write.
This post is about how it works under the hood, what I learned building it, and why it keeps getting picked over alternatives that cost 10x more.
What does Website Contact Scraper actually extract?
Website Contact Scraper crawls business websites and returns structured contact data including email addresses, phone numbers, team member names with job titles, LinkedIn profiles, and social links for Twitter/X, Facebook, Instagram, and YouTube. It outputs one deduplicated record per domain.
Here's the full breakdown of what comes back in each result:
- Email addresses from mailto: links, body text (with script/style nodes stripped), and anchor hrefs
- Phone numbers from tel: links and formatted numbers in contact areas (header, footer, nav, address blocks)
- Team member names via Schema.org Person markup, 11 CSS team-card selectors, and heading-paragraph pair analysis
- Job titles matched against 35+ title keywords (CEO through Finance) from adjacent elements
- Social links for LinkedIn, Twitter/X, Facebook, Instagram, YouTube
- Metadata: domain, pages scraped count, ISO timestamp
The important bit: everything is deduplicated across all pages crawled. Emails by exact lowercase string. Phones by digit-only key, so +1 (415) 555-0192 and 4155550192 collapse to one entry. Contacts by case-insensitive name. Social links first-match-per-platform.
How does the contact extraction work technically?
The actor uses Apify's CheerioCrawler to parse static HTML without a browser. It runs up to 10 concurrent connections with a 120 requests/minute rate limit and automatic retries. For each domain, it crawls the homepage first, then discovers and follows same-domain links matching 19 contact-page path keywords like /contact, /about, /team, /leadership, and /people.
Three extraction strategies run in parallel on every page:
Email extraction runs a regex (/\b[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,12}\b/g) against three sources: mailto link hrefs, cleaned body text (script and style nodes stripped to avoid tracking pixel emails), and all anchor href attributes. Thirteen junk patterns filter out noreply@, test@, admin@, webmaster@, and addresses from placeholder domains like example.com, sentry.io, and wixpress.io.
Phone extraction prioritizes tel: links as the most reliable source. For text-based numbers, it only searches contact-specific page areas — header, footer, nav, address elements, and anything with contact/phone/info CSS classes. Three regex patterns cover international (+1 (555) 123-4567), parentheses ((555) 123-4567), and separator formats (555-123-4567). Numbers must be 7-15 digits, can't be all-same-digit sequences, and can't contain 1234567.
Name extraction uses three strategies: Schema.org Person structured data, 11 team-card CSS selectors (.team-member, .team-card, .staff-member, etc.), and heading-paragraph pairs where an h3/h4 matches a strict proper-name regex and the next sibling contains job title keywords. A 40-word junk-name blocklist catches false positives like "Free Plan" and "Our Services."
This strict approach is why the success rate sits at 99.8%. I'd rather miss a phone number buried in body copy than return a random 10-digit sequence from a ZIP code or product SKU.
Why build another contact scraper?
Honestly, I built it because the existing options annoyed me.
Hunter.io charges $49-$149/month for access to a pre-scraped database. Clay runs $149-$720/month. Both are querying stale indexes — you're paying to search what they scraped weeks or months ago, not what's actually on the website right now.
Website Contact Scraper crawls the live site every time you run it. The data is as fresh as the website itself. And the pricing model is fundamentally different: $0.15 per website scanned. No subscription, no monthly minimum, no seat licenses.
According to Apify's 2025 web scraping report, pay-per-result pricing has grown 340% year-over-year on their platform. Users don't want subscriptions for tools they use irregularly. They want to pay when they actually get value. That's exactly what PPE (pay-per-event) pricing does — you're charged per website successfully scanned, not per API call or monthly seat.
Quick math: scanning 200 websites costs $30. At Hunter.io's Professional plan ($149/month), you'd blow through that budget in one month whether you use it or not. Most ApifyForge users I've talked to spend $5-$30/month on contact scraping and cancel nothing because there's nothing to cancel.
Who's actually using this thing?
The 236 users break down into a few clear patterns based on what I see in the run logs:
SDRs building prospect lists. They paste 50-200 company URLs from LinkedIn Sales Navigator exports or CRM lists. The actor returns emails, direct phone numbers, and LinkedIn profiles they feed into outreach sequences. A 2024 Gartner report found that SDR teams spend 21% of their time on manual data entry and research. This cuts that to near zero for the contact-finding portion.
Marketing agencies doing lead gen for clients. They scrape industry directories and trade association member pages to build prospect databases. The CSV export maps directly to email marketing tools and CRM imports. ApifyForge has a full lead generation comparison page if you want to see how the contact scraper stacks up against other tools in the category.
Recruiters pulling team pages. They want to know who works at target companies, what their titles are, and how to reach them — before making first contact. The contacts array with names and titles is built for this.
RevOps teams enriching CRM data. They run batches of existing company records through the scraper to fill in missing emails, phones, and social profiles. Then they verify addresses with the Bulk Email Verifier before importing.
How much does website contact scraping cost?
Website Contact Scraper costs $0.15 per website scanned using Apify's pay-per-event pricing model. A batch of 100 websites costs $15, and 500 websites costs $75. There is no monthly subscription or minimum commitment. Apify's free tier includes $5 of monthly platform credits, which covers about 33 sites at no cost.
Here's how that compares to alternatives:
| Tool | Pricing model | Cost for 200 sites/month | Annual cost |
|---|---|---|---|
| Website Contact Scraper | $0.15/site | $30 | $360 |
| Hunter.io | $49-$149/mo subscription | $49-$149 | $588-$1,788 |
| Clay | $149-$720/mo subscription | $149-$720 | $1,788-$8,640 |
| Apollo.io | $49-$119/mo subscription | $49-$119 | $588-$1,428 |
| Manual research (15 sites/hr at $25/hr) | Labor | $333 | $4,000 |
The manual research line is real. Forrester's 2024 B2B data quality study found that companies spend an average of $15,000/year on manual prospect research across their sales teams. Even small teams burn hours on this.
You can set a spending cap per run, and the actor stops gracefully when you hit it. It logs exactly how many domains were processed versus how many were skipped. No surprise charges.
What makes the extraction actually reliable?
Two things I obsessed over: minimizing false positives and maximizing useful signal.
Most contact scrapers just run a regex across the entire page body. That gets you emails from tracking pixels, phone numbers from postal codes, and "names" that are actually navigation headings. The noise-to-signal ratio is terrible.
Here's what I did differently:
Phone numbers only come from structured sources. Tel: links first (most reliable), then formatted numbers in contact-specific page areas only. I intentionally skip the main body copy. A Stanford NLP study on web data extraction showed that restricting extraction to structurally relevant page regions reduces false positives by 60-80% compared to full-page regex.
Email filtering is aggressive. Thirteen junk patterns remove noreply@, admin@, webmaster@, and addresses from known placeholder domains. The body text extraction strips script, style, and noscript nodes before running the regex — this catches tracking pixel emails that other scrapers happily return.
Name validation uses a strict regex plus a blocklist. The name must be 2-4 capitalized words, under 40 characters, and not contain any of 40+ common false-positive words. It catches "Free Trial", "Our Services", "Read More" — all the headings that look like names if you're not careful.
The result: 99.8% success rate across 11,516 runs. That number isn't just "the actor didn't crash." It means the output was structured, clean, and usable.
How to use Website Contact Scraper step by step
- Go to Website Contact Scraper on Apify
- Paste your website URLs into the input field (root domains like
https://acmecorp.com, not deep URLs) - Keep
maxPagesPerDomainat 5 — this covers homepage + contact + about + team pages for most sites - Click Start and wait. 50 websites typically finish in 3-5 minutes, 500 in under an hour.
- Download results as JSON, CSV, or Excel from the Dataset tab
Or call it programmatically:
from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("ryanclinton/website-contact-scraper").call(run_input={
"urls": [
"https://pinnacleventures.com",
"https://meridiantech.io",
],
"maxPagesPerDomain": 5,
"includeNames": True,
"includeSocials": True,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(f"{item['domain']}: {len(item['emails'])} emails")
for contact in item.get("contacts", []):
print(f" {contact['name']} - {contact.get('title', 'no title')}")
The JavaScript and cURL equivalents work the same way — check the full API docs on ApifyForge for examples in every language.
What are the limitations of HTML-based contact scraping?
The biggest one: no JavaScript rendering. Website Contact Scraper uses CheerioCrawler, which parses static HTML. React, Angular, and Vue apps that load contacts via client-side JS won't have that dynamic content captured.
I built Website Contact Scraper Pro for exactly this case — it uses a real browser to render SPAs. But for the roughly 70-80% of business websites that serve contact info in static HTML (according to W3Techs' 2025 web technology survey, only about 20% of business sites are fully client-rendered), the Cheerio version is faster, cheaper, and more reliable.
Other limitations worth knowing:
- Same-domain links only — external team directories or hosted about pages won't be discovered
- Name extraction depends on HTML patterns — custom layouts may not trigger any of the three strategies
- First social link per platform — if a page has multiple LinkedIn profiles, only the first is captured
- No authentication — login-gated employee directories aren't supported
- Static data — reflects what's on the page at run time, not historical
I list these in the README because I'd rather you know the boundaries upfront than find out mid-project.
How do you combine contact scraping with email verification?
The best workflow chains Website Contact Scraper with Email Pattern Finder and email verification. First, scrape contacts to get names and whatever emails are publicly listed. Then use Email Pattern Finder to predict missing personal emails from the company's naming convention. Finally, verify everything before import.
ApifyForge has a contact scraper comparison page that breaks down how different tools in this pipeline work together. The short version:
| Step | Tool | Cost |
|---|---|---|
| Scrape contacts | Website Contact Scraper | $0.15/site |
| Predict missing emails | Email Pattern Finder | $0.10/domain |
| Verify addresses | Bulk Email Verifier | $0.005/email |
| Score leads | B2B Lead Qualifier | $0.15/lead |
For a batch of 100 companies, that entire pipeline costs about $25-$30 total. No subscriptions. A McKinsey 2024 analysis of B2B sales efficiency found that companies using automated contact enrichment pipelines close deals 23% faster than those relying on manual research.
What I'd build differently today
If I were starting from scratch, I'd add a few things:
Schema.org Organization extraction for company metadata like founding year, employee count, and address. About 18% of business websites include Organization structured data according to a 2025 Schema.org adoption study, and it's free information sitting in the HTML.
Better phone parsing for international formats. The current regex handles US/UK/EU formats well, but some Asian and African phone number formats slip through the cracks. It's on the roadmap.
A confidence score per contact. Schema.org Person data with itemprop=name is high confidence. A heading-paragraph pair match is medium. That signal would help users prioritize which contacts to verify first.
But tbh, the current version works. The 99.8% success rate and 11,516 runs tell me that perfect is the enemy of shipped. I'd rather keep improving incrementally than rewrite from scratch.
Should you use this or something else?
If you need fresh contact data scraped directly from live websites, and the sites you're targeting serve contact info in static HTML — this is probably the cheapest, simplest option available. $0.15 per site, no subscription, structured output, 99.8% success rate.
If your targets are JavaScript-heavy SPAs, use the Pro version instead.
If you want a pre-built database you can query without waiting for a crawl, Hunter.io or Apollo might be better — but you'll pay $50-$150/month for the privilege and the data may be weeks old.
If you need the full pipeline — scrape, enrich, verify, score, push to CRM — check the Waterfall Contact Enrichment actor on ApifyForge, which chains multiple data sources into a single run.
The ApifyForge cost calculator can estimate your monthly spend based on volume. And if you're comparing options, the lead generation comparison page lays out the full category.
The actor is live at apify.com/ryanclinton/website-contact-scraper. Run it on one site for $0.15 and see if the output is what you need. That's the whole pitch.
Last updated: March 2026
Built and maintained by ApifyForge — 300+ Apify actors and 93 MCP intelligence servers for web scraping, lead generation, and compliance screening.
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