B2b Lead Qualifier

Score and rank B2B leads by analyzing company websites for business quality signals. Give it a list of domains and get back a score (0-100), letter grade (A-F), and a breakdown of exactly which signals contributed to the score.

No AI API keys needed. Pure rule-based scoring from publicly visible HTML — fast, transparent, and affordable.

How to score B2B leads

1. Go to the B2B Lead Qualifier on Apify
2. Enter company domains (e.g., stripe.com, zapier.com)
3. Click Start — get back scored, ranked leads with signal breakdowns
4. Sort by score to focus on the highest-quality leads first

What does it analyze?

The Actor crawls each company website and checks for 30+ business quality signals across 5 categories:

Category	Max Points	What It Measures
Contact Reachability	30	Can you actually reach someone? Email, phone, LinkedIn, decision-makers
Business Legitimacy	25	Is this a real, active business? Privacy policy, SSL, content depth
Online Presence	20	Marketing sophistication: blog, analytics, chat widget, testimonials
Website Quality	15	Technical professionalism: meta tags, structured data, mobile-ready
Team Transparency	10	Can you see who works there? Team page, named contacts, job titles

Grading scale

Score	Grade	Meaning
90-100	A	Excellent — highly reachable, established business
75-89	B	Good — solid web presence, likely reachable
60-74	C	Average — some signals present, may need more research
40-59	D	Below average — limited online presence
0-39	F	Poor — minimal signals, likely hard to reach

Output

Each domain produces one scored result. Here's a real result from analyzing apify.com:

{
    "domain": "apify.com",
    "url": "https://apify.com",
    "score": 62,
    "grade": "C",
    "scoreBreakdown": {
        "contactReachability": 10,
        "businessLegitimacy": 22,
        "onlinePresence": 15,
        "websiteQuality": 12,
        "teamTransparency": 3
    },
    "signals": [
        { "signal": "hasLinkedInCompany", "category": "contactReachability", "points": 4, "detail": "http://linkedin.com/company/apify/" },
        { "signal": "hasContactPage", "category": "contactReachability", "points": 3, "detail": "Contact page link found" },
        { "signal": "hasGenericEmail", "category": "contactReachability", "points": 3, "detail": "Found: hello@apify.com" },
        { "signal": "hasCopyrightCurrent", "category": "businessLegitimacy", "points": 3, "detail": "Copyright 2026" },
        { "signal": "hasPrivacyPolicy", "category": "businessLegitimacy", "points": 3, "detail": "Privacy policy link found" },
        { "signal": "hasMarketingTools", "category": "onlinePresence", "points": 3, "detail": "hubspot" },
        { "signal": "hasAnalytics", "category": "onlinePresence", "points": 2, "detail": "google-analytics, segment" }
    ],
    "extractedData": {
        "emails": ["hello@apify.com"],
        "phones": [],
        "contactNames": [],
        "socialLinks": { "linkedin": "http://linkedin.com/company/apify/", "twitter": "https://x.com/apify" },
        "address": null,
        "cmsDetected": null,
        "techSignals": ["google-analytics", "segment", "hubspot"]
    },
    "pagesScraped": 5,
    "qualifiedAt": "2026-02-07T01:57:12.078Z"
}

Signals array shortened for brevity — the full output includes all 25 signals that fired.

Output fields

Field	Type	Description
domain	String	Company domain analyzed
score	Integer	Lead quality score (0-100)
grade	String	Letter grade: A, B, C, D, F
scoreBreakdown	Object	Points per category (contactReachability, businessLegitimacy, onlinePresence, websiteQuality, teamTransparency)
signals	Array	Every signal that contributed to the score, with signal name, category, points, and human-readable detail
extractedData	Object	All extracted contact data: emails, phones, contact names, social links, address, CMS, tech stack
pagesScraped	Integer	Number of pages crawled for this domain
qualifiedAt	String	ISO timestamp

Use cases

Prioritize outreach from a lead list

You have 500 company domains. Instead of contacting them all, run them through the qualifier to rank them by quality. Focus your outreach on grade A and B leads first — they have the best web presence and are most likely to respond.

Filter low-quality leads before enrichment

Running email enrichment on 1,000 domains is expensive. Pre-qualify them first: filter out grade D and F leads (set minScore to 60) and only enrich the promising ones.

Complete the lead generation pipeline

Google Maps Scraper          Website Contact Scraper        Email Pattern Finder        B2B Lead Qualifier
┌──────────────────┐        ┌──────────────────────┐       ┌────────────────────┐      ┌───────────────────┐
│ "plumbers in LA" │ ────>  │ 500 website URLs      │ ───>  │ Detect email       │ ──>  │ Score & rank      │
│                  │ sites  │                        │ data  │ patterns           │      │ all 500 leads     │
│ 500 businesses   │        │ emails, names, titles  │       │ Generate addresses │      │ Focus on grade A  │
└──────────────────┘        └──────────────────────┘       └────────────────────┘      └───────────────────┘

Want this entire pipeline in one click? Use B2B Lead Generation Suite — it chains all 3 actors automatically with a single input.

Input

Field	Type	Description	Default
domains	Array of strings	Company domains to score (required)	—
pipelineData	Array of objects	Pre-scraped data from Website Contact Scraper / Email Pattern Finder (optional)	[]
maxPagesPerDomain	Integer (1-15)	Max pages to crawl per website	5
minScore	Integer (0-100)	Only output leads at or above this score	0
proxyConfiguration	Object	Proxy settings for website scraping	Apify Proxy

Example input

{
    "domains": ["stripe.com", "zapier.com", "basecamp.com"],
    "maxPagesPerDomain": 5,
    "minScore": 50
}

Pipeline integration input

Feed output from Website Contact Scraper directly as pipelineData to skip re-scraping contact info:

{
    "domains": ["buffer.com"],
    "pipelineData": [
        {
            "domain": "buffer.com",
            "emails": ["hello@buffer.com"],
            "contacts": [
                { "name": "Joel Gascoigne", "title": "Founder CEO" },
                { "name": "Jenny Terry", "title": "VP of Finance & Operations" }
            ],
            "socialLinks": {
                "linkedin": "https://www.linkedin.com/company/bufferapp",
                "twitter": "https://x.com/buffer"
            }
        }
    ]
}

How to use the API

Python

from apify_client import ApifyClient

client = ApifyClient(token="YOUR_API_TOKEN")

run = client.actor("ryanclinton/b2b-lead-qualifier").call(
    run_input={
        "domains": ["stripe.com", "zapier.com", "basecamp.com"],
        "minScore": 50,
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['domain']}: {item['score']}/100 ({item['grade']})")
    for signal in item["signals"]:
        print(f"  +{signal['points']} {signal['signal']}: {signal['detail']}")

JavaScript / Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('ryanclinton/b2b-lead-qualifier').call({
    domains: ['stripe.com', 'zapier.com', 'basecamp.com'],
    minScore: 50,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => {
    console.log(`${item.domain}: ${item.score}/100 (${item.grade})`);
    item.signals.forEach(s => console.log(`  +${s.points} ${s.signal}: ${s.detail}`));
});

Python — full pipeline example

from apify_client import ApifyClient

client = ApifyClient(token="YOUR_API_TOKEN")

# Step 1: Get businesses from Google Maps
maps_run = client.actor("compass/crawler-google-places").call(
    run_input={"searchStringsArray": ["dentists in Austin TX"]}
)

websites = []
for biz in client.dataset(maps_run["defaultDatasetId"]).iterate_items():
    if biz.get("website"):
        websites.append(biz["website"])

# Step 2: Scrape contact info
scraper_run = client.actor("ryanclinton/website-contact-scraper").call(
    run_input={"urls": websites, "maxPagesPerDomain": 5}
)

pipeline_data = []
domains = []
for item in client.dataset(scraper_run["defaultDatasetId"]).iterate_items():
    domains.append(item["domain"])
    pipeline_data.append(item)

# Step 3: Qualify and rank leads
qualifier_run = client.actor("ryanclinton/b2b-lead-qualifier").call(
    run_input={
        "domains": domains,
        "pipelineData": pipeline_data,
        "minScore": 50,
    }
)

# Step 4: Get ranked results
for item in client.dataset(qualifier_run["defaultDatasetId"]).iterate_items():
    print(f"{item['domain']}: {item['score']}/100 ({item['grade']}) - {len(item['signals'])} signals")

Signals reference

Contact Reachability (30 points max)

Signal	Points	What triggers it
Personal email found	8	Non-generic email discovered (not info@, sales@, etc.)
Generic email found	3	At least a role-based email like info@ exists
Phone number	5	Phone number found via tel: link or in contact sections
Physical address	4	Street address detected in footer, contact page, or <address> tag
Contact page	3	Link to a contact/get-in-touch page found in navigation
LinkedIn company	4	LinkedIn company page linked from the website
Decision-maker	3	C-level, founder, or president found in team contacts

Business Legitimacy (25 points max)

Signal	Points	What triggers it
Privacy policy	3	Link to a privacy policy page
Terms of service	2	Link to terms of service
Current copyright	3	Copyright year is recent (2024+)
About page	3	Link to an about/company page
Structured data	3	JSON-LD or microdata markup present
Multiple pages	3	Navigation has 3+ internal links
Content depth	3	Homepage has 200+ words
Favicon	2	Custom favicon set
SSL active	3	HTTPS connection successful

Online Presence (20 points max)

Signal	Points	What triggers it
Social media	1-5	1 point per platform linked (LinkedIn, Twitter, Facebook, Instagram, YouTube)
Blog/news	3	Link to blog, news, or press section
Testimonials	3	Customer testimonials, case studies, or "trusted by" section
Analytics	2	Google Analytics, Segment, Mixpanel, Hotjar, or Plausible detected
Marketing tools	3	HubSpot, Marketo, Pardot, Mailchimp, or Klaviyo detected
Chat widget	2	Intercom, Drift, Crisp, Tawk, Zendesk, or Freshdesk detected
Pricing page	2	Link to a pricing or plans page

Website Quality (15 points max)

Signal	Points	What triggers it
Meta description	2	Meaningful meta description tag present
Open Graph	2	OpenGraph meta tags for social sharing
Mobile viewport	2	Responsive viewport meta tag configured
Proper title	2	Page title exists, is longer than 10 characters
H1 heading	1	H1 element present on the page
Schema.org Org	3	Organization or LocalBusiness structured data
Images	1	At least 3 images on the page
Navigation	2	Navigation element with 3+ links

Team Transparency (10 points max)

Signal	Points	What triggers it
Team page	3	Link to team, people, or leadership page
Named contacts	1-4	1 point per person found, up to 4
Job titles	3	At least one contact has a job title

Performance and cost

This Actor uses CheerioCrawler (HTTP only, no browser) for minimal cost:

Domains	Estimated time	Estimated platform cost
10	~15 seconds	< $0.01
100	~3 minutes	~$0.05
1,000	~20 minutes	~$1.00

Estimates based on 5 pages per domain with datacenter proxies.

FAQ

How is the score calculated?

The score is a simple sum of points from detected signals. Each signal has a fixed point value, grouped into 5 categories with maximum caps. The scoring is fully transparent — every signal that contributed is listed in the output with its point value and what triggered it.

Does this use AI or LLMs?

No. The scoring is entirely rule-based — it checks for specific HTML elements, meta tags, links, and script patterns. No AI API keys needed, which keeps costs predictable and results reproducible.

Why did my domain get a low score?

Common reasons: the website uses JavaScript rendering (this Actor uses HTTP requests, no browser — JS-rendered content won't be detected), the site has minimal public contact info, or it's a simple single-page website. Check the signals array to see exactly which signals fired and which didn't.

Can I customize the scoring weights?

Not currently — the weights are designed based on B2B outreach best practices. If you need custom scoring, export the signals array and apply your own weights in post-processing.

Does this verify emails or phone numbers?

No — it detects whether contact information is publicly visible on the website. For email verification, use a dedicated verification service after qualifying leads.

What's the difference between standalone and pipeline mode?

Standalone: Just provide domains — the Actor crawls each website and extracts everything from scratch. Pipeline: Provide pre-scraped data from Website Contact Scraper via pipelineData — the Actor uses that contact data instead of re-scraping, but still crawls for website quality and tech signals.

Responsible use

This Actor analyzes publicly visible website content. By using it, you agree to:

• Comply with all applicable laws, including GDPR, CAN-SPAM, and CCPA
• Respect each website's Terms of Service
• Use the scores and extracted data only for legitimate business purposes
• Not use this tool to facilitate spam or unsolicited bulk outreach

Limitations

• JavaScript-rendered content — Uses HTTP requests only (CheerioCrawler). SPAs that render everything client-side will score lower because body content isn't visible.
• No email verification — Detects emails on the page but doesn't verify deliverability.
• English-centric — Name extraction and signal patterns work best with English-language websites.
• Snapshot scoring — Scores reflect the website at the time of crawling. Websites change over time.

Pricing

• $5 per 1,000 domains scored
• First 100 domains free — try it risk-free
• Only pay for successful results

Domains	Price
100	Free
1,000	$5
5,000	$25
10,000	$50

Changelog

v1.0 (2026-02-07)

• Initial release
• 30+ business quality signals across 5 categories
• Rule-based scoring (0-100) with letter grades
• Pipeline integration with Website Contact Scraper and Email Pattern Finder
• Tech stack detection: CMS, analytics, marketing, chat, e-commerce
• Contact extraction: emails, phones, social links, physical addresses, team members