🏫 K12 Schools Email Scraper – School Contact Data, Verified Emails & Phone Numbers

The K12 Schools Email Scraper finds K-12 schools in the locations you specify, visits each school's website, and extracts contact data — email addresses, phone numbers and social media profiles — alongside the school's name, full address, coordinates, rating and place identifier. Every row it writes combines a location record with the contact details actually found on that school's own site.

Education-sector outreach lives or dies on contact quality. District lists go stale, generic directories sell the same recycled CSV to everyone, and manually opening a few hundred school websites to copy the office email is not a plan. This K-12 schools email scraper automates that loop: search once, crawl the sites, optionally validate every address against DNS and MX records, and get back a contact list you can hand straight to your CRM or email platform.

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📊 What Data Can You Extract with This K12 Schools Email Scraper?

The scraper writes one flat record per contact discovered. The fields group naturally into six categories.

Category	Fields	What it gives you
🏫 School identity	name, place_id, platform, url, website	The school name, its place identifier, the source platform, and the site that was crawled
📍 Location data	street_address, city, state, zip, country_code, full_address, lat, long	A complete postal address plus latitude and longitude for mapping and territory assignment
📧 Email contacts	email, email_found, emails, scraped_emails, _detailed_emails	The email addresses discovered on the school's website, both flattened and in list form
✅ Email validation	syntax_valid, dns_valid, mx_valid, smtp_valid, is_disposable, is_role_based, confidence_score, validation_errors	Layered deliverability signals so you can filter before you send
☎️ Other contact channels	phone, scraped_phones, scraped_social_media	Phone numbers and social profile links found on the same pages
⭐ Reputation and crawl telemetry	avg_rating, total_reviews, domain, job_id, status, emails_found, pages_scraped, scrape_status, error, scrape_error	Public rating signals plus per-domain crawl statistics and failure reasons

The most valuable field in practice is confidence_score. Combined with is_role_based and mx_valid, it lets you split a raw scrape into a high-confidence direct-contact list and a lower-confidence generic-inbox list — which is exactly the distinction that determines whether an education outreach campaign gets replies or bounces.

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🌟 Key Features of the K12 Schools Email Scraper

Feature	Description
🗺️ Location-driven discovery	Search by term and location list, so you can sweep a city, a county or a set of states in one run
🌐 Deep website crawling	Each school's site is crawled up to maxPagesPerSite pages, prioritising contact, about, staff and directory pages where school emails actually live
🔍 Obfuscated email detection	Recognises addresses written as name [at] domain [dot] org and similar anti-scraping patterns, not just plain mailto: links
✅ Multi-layer validation	Optional syntax, DNS, MX and SMTP checks feed a confidence_score alongside disposable and role-based flags
☎️ Phone and social extraction	Phone numbers and social media profile links are pulled from the same crawl at no extra configuration cost
📍 Geocoded records	lat and long on every row let you map coverage, assign territories or filter by radius
🎯 Per-location or global quotas	scrapeMaxBusinessesPerLocation decides whether maxBusinesses is a per-location target or a total across the whole run
📧 Email-first output	The scraper keeps crawling until it has met your target of schools that actually have an email, rather than filling the quota with blanks
🔄 Automatic proxy rotation	Requests are routed through rotating proxies automatically, so no proxy setup is required from you

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🚀 Why Choose This K12 Schools Email Scraper?

Contact data from the source, not a resold list. Every email in the output was found on the school's own website during the run. That means it reflects the site as it is today, including addresses that were updated after the last commercial database refresh.

Validation is built in, not a separate pipeline. Turn on validateEmails and each address comes back with syntax_valid, dns_valid, mx_valid, smtp_valid, is_disposable, is_role_based and a numeric confidence_score. You can filter to deliverable addresses before an address ever reaches your sending platform.

Full location context on every row. Because each record carries full_address, city, state, zip, lat and long alongside the email, the output is immediately usable for territory planning, regional segmentation and CRM import without a separate geocoding step.

Crawl telemetry makes gaps explainable. pages_scraped, emails_found, scrape_status and scrape_error tell you why a particular school produced nothing — a dead site, a blocked crawl or genuinely no published contact address. That is the difference between a list you trust and one you second-guess.

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📥 Input

{
  "googleMapsSearchTerm": "elementary school",
  "googleMapsLocation": ["Austin, TX", "Round Rock, TX"],
  "maxBusinesses": 100,
  "maxPagesPerSite": 20,
  "scrapeMaxBusinessesPerLocation": false,
  "validateEmails": true
}

🔧 K12 Schools Email Scraper Input Fields

Field	Type	Required	Default	Description
googleMapsSearchTerm	string	✅ Yes	—	The search term used to find schools, e.g. elementary school, middle school, private high school
googleMapsLocation	array	✅ Yes	[]	One or more locations to search in. Each entry is processed in turn
maxBusinesses	integer	No	20	How many schools with emails to collect. Interpreted per location or globally depending on scrapeMaxBusinessesPerLocation
maxPagesPerSite	integer	No	20	Maximum number of pages to crawl on each school's website while hunting for contact details
scrapeMaxBusinessesPerLocation	boolean	No	false	When true, maxBusinesses applies to each location separately; when false it is a global limit across the run
validateEmails	boolean	No	false	Run syntax, DNS, MX and SMTP validation on each discovered address and populate the confidence fields
proxyConfiguration	object	No	{"useApifyProxy": true}	Proxy settings for the run. Recommended for large-scale scraping to avoid IP blocks and rate limits

The run stops early and logs an error if googleMapsSearchTerm or googleMapsLocation is missing — both are required for the scraper to know what to look for and where.

💡 Input Examples

Small validation run before committing to a big scrape

{
  "googleMapsSearchTerm": "public elementary school",
  "googleMapsLocation": ["Portland, OR"],
  "maxBusinesses": 10,
  "maxPagesPerSite": 10,
  "validateEmails": true
}

Multi-city sweep with an equal quota per city

{
  "googleMapsSearchTerm": "high school",
  "googleMapsLocation": ["Denver, CO", "Boulder, CO", "Fort Collins, CO"],
  "maxBusinesses": 50,
  "scrapeMaxBusinessesPerLocation": true,
  "validateEmails": true
}

Fast, wide harvest without validation

{
  "googleMapsSearchTerm": "charter school",
  "googleMapsLocation": ["Phoenix, AZ"],
  "maxBusinesses": 300,
  "maxPagesPerSite": 8,
  "validateEmails": false
}

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📤 Output

A representative record combining school location data with a validated contact:

{
  "name": "Riverside Elementary School",
  "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4",
  "platform": "google_maps",
  "website": "https://riverside-elementary.example.org",
  "url": "https://riverside-elementary.example.org/contact",
  "domain": "riverside-elementary.example.org",
  "street_address": "1420 Riverside Drive",
  "city": "Austin",
  "state": "TX",
  "zip": "78704",
  "country_code": "US",
  "full_address": "1420 Riverside Drive, Austin, TX 78704, USA",
  "lat": 30.2451,
  "long": -97.7425,
  "phone": "+1 512-555-0142",
  "avg_rating": 4.3,
  "total_reviews": 87,
  "email_found": "office@riverside-elementary.example.org",
  "email": "office@riverside-elementary.example.org",
  "syntax_valid": true,
  "dns_valid": true,
  "mx_valid": true,
  "smtp_valid": false,
  "is_disposable": false,
  "is_role_based": true,
  "confidence_score": 78.5,
  "validation_errors": [],
  "scraped_phones": ["+1 512-555-0142"],
  "scraped_social_media": ["https://www.facebook.com/riversideelementary"],
  "emails_found": 3,
  "pages_scraped": 12,
  "scrape_status": "completed"
}

🧾 K12 Schools Email Output Fields — School and Contact Data

Field	Type	Description
name	string	School name
place_id	string	Identifier of the place
platform	string	Platform the record came from
website	string	The school's website address
url	string	Canonical URL of the scraped item
street_address	string	Street address
city	string	City
state	string	State or region
zip	string	ZIP code
country_code	string	ISO country code
full_address	string	Complete formatted address
lat	number	Latitude coordinate
long	number	Longitude coordinate
phone	string	Phone number found for the school
avg_rating	number	Average public rating
total_reviews	integer	Total number of public reviews
email	string	Email address found
email_found	string	The email address this row represents
emails	array	Email addresses found for the school
scraped_emails	array	Emails collected during the website crawl
_detailed_emails	array	Per-email detail collected during the crawl
scraped_phones	array	Phone numbers collected during the website crawl
scraped_social_media	array	Social media profile links collected during the crawl

🧾 K12 Schools Email Output Fields — Validation and Crawl Telemetry

Field	Type	Description
syntax_valid	boolean	Whether the address passes syntax validation
dns_valid	boolean	Whether the domain resolves in DNS
mx_valid	boolean	Whether the domain publishes MX records
smtp_valid	boolean	Whether the SMTP-level check passed
is_disposable	boolean	Whether the address belongs to a disposable email provider
is_role_based	boolean	Whether the address is a role account such as info@ or office@
confidence_score	number	Composite confidence score for the address
validation_errors	array	Validation problems recorded for the address
job_id	string	Identifier of the crawl job for that domain
status	string	Status of the crawl job
domain	string	The domain that was crawled
emails_found	integer	Number of emails found on that domain
pages_scraped	integer	Number of pages crawled on that domain
scrape_status	string	Outcome of the site crawl, e.g. completed or no_website
error	string	Error message, if the record failed to process
scrape_error	string	Error raised during the website crawl

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💻 How to Use the K12 Schools Email Scraper (Step by Step)

Step 1: Decide what kind of school you are targeting

googleMapsSearchTerm is a free-text search term, and the phrasing matters more than people expect. "Elementary school" and "public elementary school" return overlapping but different sets. If you are building a full K-12 list for an area, it is usually better to run several targeted scrapes — elementary, middle, high, charter, private — than one broad "school" query that pulls in driving schools, dance schools and tutoring centres.

Step 2: Build your location list

googleMapsLocation accepts an array, so a single run can cover many places. Use whatever granularity matches your outreach plan: city names for metro-level campaigns, county or region names for broader sweeps. Each location is processed in sequence and the log announces which one it is working on, so you can watch progress in real time.

Step 3: Choose how the quota is applied

This is the setting people most often get wrong. With scrapeMaxBusinessesPerLocation left at false, maxBusinesses is a total for the whole run — five cities with a limit of 100 gives you 100 schools, weighted toward whichever city was processed first. Set it to true and each of those five cities gets its own target of 100. Use true whenever you need balanced geographic coverage.

Step 4: Set the crawl depth per school website

maxPagesPerSite defaults to 20. School websites bury contact details in staff directories, department pages and PDF-heavy "about us" sections, so a deeper crawl generally finds more addresses — but it also takes longer per school. Ten pages is a reasonable compromise for a quick harvest; twenty or more when contact completeness matters more than run time.

Step 5: Turn on email validation

validateEmails defaults to false because validation adds time. For any list you intend to actually send to, turn it on. You get syntax_valid, dns_valid, mx_valid and smtp_valid as independent booleans, plus is_disposable, is_role_based, a numeric confidence_score and any validation_errors. Filtering on mx_valid alone removes a large share of addresses that would otherwise hard-bounce.

Step 6: Run and monitor the log

Start the run. The log reports the location being processed, the target count, and a per-school line showing the name and website as each one is crawled. Because the scraper keeps going until it has met your target of schools with emails, it will crawl more listings than your maxBusinesses number — that is expected behaviour, not a bug.

Step 7: Filter and export the results

Open the Dataset tab when the run finishes. For an outreach-ready list, filter to rows where email_found is non-empty and, if you validated, where mx_valid is true and is_disposable is false. Split is_role_based into its own segment — role accounts like office@ and info@ behave differently in campaigns to named staff addresses. Export as CSV for your email platform or JSON for programmatic use.

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🔌 API Access & Integrations

Trigger the K12 schools email scraper over HTTP and get the dataset items back in one call:

curl -X POST "https://api.apify.com/v2/acts/scrapers-hub~k12-schools-email-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "googleMapsSearchTerm": "elementary school",
    "googleMapsLocation": ["Austin, TX"],
    "maxBusinesses": 50,
    "maxPagesPerSite": 15,
    "validateEmails": true
  }'

With the Python client:

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

run = client.actor("scrapers-hub/k12-schools-email-scraper").call(run_input={
    "googleMapsSearchTerm": "high school",
    "googleMapsLocation": ["Denver, CO", "Boulder, CO"],
    "maxBusinesses": 100,
    "scrapeMaxBusinessesPerLocation": True,
    "validateEmails": True,
})

deliverable = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("email_found") and item.get("mx_valid") and not item.get("is_disposable"):
        deliverable.append({
            "school": item.get("name"),
            "email": item["email_found"],
            "city": item.get("city"),
            "state": item.get("state"),
            "score": item.get("confidence_score"),
        })

print(f"{len(deliverable)} deliverable school contacts")

Results can be pushed straight into Zapier, Make, Google Sheets or Slack, or delivered to your own CRM endpoint with an Apify webhook on run completion.

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💡 Best Use Cases for K12 Schools Contact Data

📚 EdTech Sales Prospecting

EdTech vendors need the right school, the right region and a working inbox. Filter on state and city to match your sales territories, use confidence_score and mx_valid to prioritise deliverable addresses, and treat avg_rating and total_reviews as a rough proxy for school engagement. place_id gives you a stable key for deduplicating against an existing CRM.

🏢 School Supplies and Services Outreach

Suppliers of furniture, catering, uniforms, transport and maintenance services can build regional target lists in a single run. full_address, lat and long support radius-based territory assignment, while phone and scraped_phones give a second channel for accounts where email does not land.

📊 Education Market Research

Researchers and policy analysts can map school density, contactability and public reputation across regions. Aggregating avg_rating and total_reviews by city or zip gives a rating landscape; aggregating emails_found and scrape_status shows how digitally reachable schools are in different areas, which is itself a meaningful finding.

🤝 Non-Profit and Programme Partnerships

Organisations running reading programmes, STEM initiatives, sports schemes or grant offers need direct contact with school offices. The combination of email_found, is_role_based and scraped_social_media gives you the office inbox for formal approaches and the social channel for warm-up contact.

🗺️ Territory Planning and Field Sales Routing

With lat and long on every row, contact data becomes mappable. Cluster schools geographically to plan field visits, calculate drive-time territories, or find gaps where you have no coverage. street_address and zip support the same analysis at postcode level for reporting.

📢 District-Level Campaign Building

Grouping records by domain reveals which schools sit on shared district infrastructure. That is often the fastest route to identifying district-wide decision makers rather than pitching each school individually, and emails_found per domain hints at how much contact surface each district exposes.

🧹 CRM Hygiene and List Enrichment

Already have a school list? Run the scraper over the same locations and use place_id, website and full_address to match records, then overwrite stale emails with freshly scraped, validated ones. The validation_errors array tells you exactly why an old address failed, which is useful evidence when justifying a list cleanup.

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⚙️ Tips for Better K12 Schools Email Scraping Results

• Run one school type per scrape. Separate runs for elementary, middle, high, charter and private schools produce cleaner, more complete lists than a single generic "school" query, which drags in unrelated businesses.
• Enable scrapeMaxBusinessesPerLocation for multi-city runs. Without it, the first location in your list can consume the entire quota and later cities return nothing.
• Increase maxPagesPerSite when contact rates look low. School sites often hide addresses in staff directory subpages. Going from 10 to 20 pages typically lifts emails_found noticeably.
• Validate before you send, not after. Turning on validateEmails costs run time but saves your sending reputation. A hard bounce rate driven by dead district addresses is much more expensive than a longer scrape.
• Segment role-based addresses separately. is_role_based flags office@, info@ and admin@ style inboxes. They are legitimate school contacts but respond to different messaging than a named staff address.
• Start with a 10-school test run. Confirm the search term returns actual K-12 schools in your target area before spending a large run on a query that is pulling in tutoring centres.

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🛠️ Troubleshooting

The run ended immediately with a missing-input error. Both googleMapsSearchTerm and googleMapsLocation are required. If either is empty the scraper logs an error and exits without scraping. Provide a search term string and at least one location in the array.

I got fewer schools than maxBusinesses. The scraper only saves schools where an email was actually found, and it stops when it runs out of listings for the location. Widen the location, use a broader search term, or raise maxPagesPerSite so more sites yield a contact.

Many rows have scrape_status set to no_website. Those schools have no website listed on their public profile, so there is nothing to crawl for emails. They are still written to the dataset with their location and phone data, which is often useful even without an email.

Emails come back with mx_valid false. The domain does not publish mail exchange records, so it cannot receive email at that address. These are almost always dead or misconfigured domains — filter them out before importing into any sending platform.

Only one of my locations produced results. Check scrapeMaxBusinessesPerLocation. When it is false, maxBusinesses is a global cap and the first location can absorb it entirely. Set it to true for even coverage across every location in the list.

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❓ Frequently Asked Questions About K12 Schools Email Scraping

What does the K12 Schools Email Scraper actually collect? It finds schools matching your search term in your chosen locations, crawls each school's website, and returns email addresses, phone numbers and social links together with the school's name, full address, coordinates, rating and place identifier.

Where do the email addresses come from? They are extracted from the school's own public website during the crawl — contact pages, about pages, staff directories and similar. Nothing is taken from a purchased database or a private source.

Can it find emails written to avoid scrapers? Yes. The extractor recognises common obfuscation patterns such as name [at] domain [dot] org in addition to plain text addresses and mailto: links.

How does email validation work in this scraper? With validateEmails enabled, each address is checked for syntax, DNS resolution, MX records and SMTP acceptance. Those results feed confidence_score, and separate flags mark disposable providers and role-based inboxes.

What is the difference between email, email_found and scraped_emails? scraped_emails is the list of everything found on that school's site. email_found and email identify the specific address a given output row represents, since a school with several published addresses produces several rows.

Can I control how deep the scraper goes into each school website? Yes, with maxPagesPerSite. It defaults to 20 pages per domain. Lower it for speed, raise it when you need maximum contact coverage.

Does maxBusinesses apply per location or overall? It depends on scrapeMaxBusinessesPerLocation. When false (the default) it is a global limit for the whole run. When true, each location in googleMapsLocation gets its own target.

Do I need to configure a proxy? No. Proxy rotation is handled automatically by the actor, so there is nothing for you to set up.

Are phone numbers and social profiles included? Yes. Phone extraction and social media extraction run as part of every crawl, populating phone, scraped_phones and scraped_social_media without any extra configuration.

Can I use this K12 schools email scraper outside the United States? The location field is free text, so you can search other countries. country_code is returned on each record. Results depend on how well schools in that region are represented on the underlying source and how their websites publish contact details.

How do I get only the deliverable contacts? Run with validateEmails on, then filter to rows where mx_valid is true, is_disposable is false, and confidence_score clears whatever threshold your campaign requires.

Why do some schools appear more than once in the output? Each row represents one email address. A school publishing an office address, a principal's address and an admissions address will appear as three rows sharing the same name, place_id and full_address.

Can I schedule this to keep a school contact list current? Yes. Apify's Scheduler can re-run the same input monthly or quarterly. Compare new results against your stored list using place_id to spot new schools and changed addresses.

How do I export the school contact data? From the Apify Console you can export the dataset as CSV, JSON, Excel or XML. Via the API, run-sync-get-dataset-items returns the items directly in the response body.

Is scraping school contact information legal? The scraper collects only publicly published information from school websites. How you use it is your responsibility — commercial outreach to school addresses is subject to marketing and privacy law in your jurisdiction, and you should confirm your legal basis before sending.

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🆘 Support & Feedback

Run into a bug, an unexpected result, or a location that returns nothing? Open a ticket in the Issues tab on the actor page and include the run ID and your input so the case can be reproduced.

Need a custom build — additional output fields, a different discovery source, tighter validation rules, or direct delivery into your CRM? Email scraperhubapi@gmail.com and describe the requirement.

If the K12 schools email scraper saves you time, please leave a rating and a short review on the actor page. Feedback directly shapes what gets improved.

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⚖️ Disclaimer

This K12 schools email scraper collects only publicly available information — school listings and the contact details schools themselves publish on their public websites. It does not access private systems, authenticated portals, student records or any non-public source.

You are responsible for how you use this actor and the data it produces. That includes complying with the terms of service of the sites visited, respecting any published contact policies, and following the marketing and anti-spam rules that apply where you and your recipients are located.

Email addresses, phone numbers and staff names are personal data under GDPR, CCPA and comparable privacy regimes. If you store or process this output you are acting as the data controller: you need a lawful basis for processing, you must honour access and deletion requests, and you should not retain contact data beyond the purpose you collected it for. Take particular care with any data relating to schools, since the education context can attract additional protections.

If you believe data collected through this actor should be removed, contact scraperhubapi@gmail.com with the details and the request will be handled.