Restaurant Email Scraper

Restaurant Email Scraper 🎯

Manually visiting hundreds of restaurant websites to collect emails wastes hours you don’t have. Restaurant Email Scraper extracts restaurant contact information—emails, phone numbers, and social links—from publicly available sources at scale. This restaurant email scraper helps marketers, recruiters, and growth teams build a restaurant marketing email list in minutes by finding targeted contacts for each location. You can collect up to thousands of restaurant prospects in a single run, then export instantly for outreach.

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What You Get: Sample Output

Here’s a sample record from a single run:

{
  "name": "Saffron Street Kitchen",
  "website": "https://www.saffronstreetkitchen.com",
  "phone": "+1 (305) 555-0142",
  "full_address": "221B Ocean Ave Miami FL 33101 US",
  "city": "Miami",
  "state": "FL",
  "zip": "33101",
  "country_code": "US",
  "scraped_emails": ["info@saffronstreetkitchen.com", "sales@saffronstreetkitchen.com"],
  "scraped_phones": ["+1 (305) 555-0142"],
  "scraped_social_media": ["https://www.instagram.com/saffronstreetkitchen/"],
  "emails_found": 2,
  "pages_scraped": 6,
  "avg_rating": 4.5,
  "total_reviews": 318,
  "lat": 25.7617,
  "long": -80.1918,
  "place_id": "ChIJd8BlQ2BZwokRAFUEcm_qrcA",
  "scrape_status": "success",
  "email_found": "info@saffronstreetkitchen.com"
}

Output Fields

Field	Type	What It Tells You
name	string	The restaurant’s business name you can use for personalization
website	string	The website the actor used as the primary source for contact scraping
phone	string	A phone value captured at the business level
full_address	string	A formatted address string useful for CRM fields and verification
city	string	City for segmentation (local restaurant email database building)
state	string	State/region for filtering and routing outreach
zip	string	ZIP/postal code for geography-based campaigns
country_code	string	Country code to standardize your dataset
scraped_emails	array	All emails found on the restaurant’s website
scraped_phones	array	Phone numbers found while scraping website pages
scraped_social_media	array	Social profile links found during website scraping
emails_found	number	Quick count of how many emails were detected for that business
pages_scraped	number	How many pages were processed while searching for contact info
avg_rating	number	Helps prioritize leads based on reputation signals
total_reviews	number	Review volume you can use as a confidence heuristic
place_id	string	A unique place identifier to support deduplication
scrape_status	string	Indicates the result state (e.g., success, failed, no_website)
email_found	string	When an email exists, this row is emitted for a specific extracted email

Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard.

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Why Restaurant Email Scraper?

There are a lot of ways to pull data from public web listings—here’s what sets Restaurant Email Scraper apart.

Focused restaurant contact extraction

Restaurant Email Scraper searches for restaurants using your googleMapsSearchTerm and googleMapsLocation, then enriches each business by scraping their website for contact details. It’s designed as a practical email scraper for restaurants when you want outreach-ready leads, not just raw listings.

Email-forward output that’s easy to use

The actor pushes results to the Apify dataset in a flattened way: when emails are found, it emits one dataset row per extracted email via email_found. This makes it straightforward to build a restaurant prospecting email scraper workflow and import directly into outreach tools.

Enrichment includes phones and social links too

For each business, the output includes scraped_phones and scraped_social_media alongside scraped_emails. That means your restaurant contact email extractor doesn’t stop at email collection—it helps you capture extra ways to engage.

Resilient scraping for real-world websites

The actor includes resilience controls for scraping websites (including configuration for retries, timeouts, and concurrency at a high level). That helps reduce failures when sites behave differently in the wild—especially for building large restaurant marketing email lists.

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Configuring Your Run

Drop this into your input.json to get started:

{
  "googleMapsSearchTerm": "Restaurant",
  "googleMapsLocation": ["New York"],
  "maxBusinesses": 5,
  "scrapeMaxBusinessesPerLocation": false,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Parameter	Required	What It Does
googleMapsSearchTerm	✅	The business type or niche for the restaurant email scraper (for example, “coffee shops”).
googleMapsLocation	✅	One or more target locations (for example, ["Miami, Florida"] style inputs).
maxBusinesses	⬜	The target number of businesses to find (1–1000). The scraper stops once the target is reached.
scrapeMaxBusinessesPerLocation	⬜	If enabled, it collects up to maxBusinesses results per location. If disabled, it combines all locations up to a single total limit.
proxyConfiguration	⬜	Proxy settings for scraping. Recommended for large-scale scraping.
↳ proxy support	⬜	Use Apify Proxy for better reliability.

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Core Capabilities

Restaurant discovery by search term + location

Restaurant Email Scraper starts from your googleMapsSearchTerm and googleMapsLocation inputs, collecting candidate businesses that match your restaurant niche and target geography. This is the backbone of a restaurant email mining tool workflow.

Website enrichment for emails, phones, and social links

After discovery, it scrapes the business website and extracts scraped_emails, scraped_phones, and scraped_social_media. This gives you a richer dataset than a basic find restaurant emails approach.

Deduplication and status tracking

Results are deduplicated using place_id, and each record includes scrape_status so you can filter successes vs failures. When a business has no website, the actor marks scrape_status as no_website.

Tunable scale controls for batch lead building

You can cap the number of businesses with maxBusinesses and choose whether limits apply per location using scrapeMaxBusinessesPerLocation. This makes it suitable for everything from small local restaurant email database builds to larger outreach lists.

Proxy support for more reliable runs

Proxy settings are available via proxyConfiguration, with proxy support enabled by default in the provided UI configuration. This helps keep scraping stable when scaling your restaurant industry email scraping efforts.

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Who Gets the Most Out of This

Here's how different teams put Restaurant Email Scraper to work:

Sales Development Representatives — Use it to turn a restaurant niche and city list into an outreach-ready dataset. You get emails_found and a row per email_found, so your follow-up and sequencing is simpler.

Restaurant Marketing Teams — Build a restaurant marketing email list without manual lead hunting. Pair full_address, city, and state with the extracted emails to segment campaigns by market.

Lead Generation Agencies — Deliver restaurant leads email scraper results per client request by controlling maxBusinesses and location inputs. Exports from the Apify dataset tab make it easy to hand off to CRMs.

Freelance Researchers & Data Analysts — Analyze scraped contact coverage using pages_scraped, avg_rating, and total_reviews. The scrape_status field supports quality filtering for your final dataset.

Automation & Integration Specialists (technical users) — Integrate the actor output into pipelines that enrich, validate, and distribute leads. The consistent dataset fields like website, place_id, and email_found make downstream processing predictable.

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Step-by-Step: How to Use It

No coding needed. Here's how to run Restaurant Email Scraper from start to finish:

1. Open the actor on Apify — go to console.apify.com and open the actor page for Restaurant Email Scraper.
2. Enter your inputs — set googleMapsSearchTerm and googleMapsLocation first, then adjust maxBusinesses and scrapeMaxBusinessesPerLocation if you need tighter control.
3. Configure proxy settings — enable or adjust proxyConfiguration if you’re planning larger runs and want improved scraping reliability.
4. Hit Run and watch the live log — review progress as the actor discovers businesses and scrapes their websites for contact info.
5. View results in the dataset tab — each row includes restaurant details plus extracted fields like scraped_emails, scraped_phones, scraped_social_media, and scrape_status.
6. Export as JSON, CSV, or Excel — download in your preferred format directly from Apify.

The whole process takes under 5 minutes to set up.

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Integrations & Export Options

Once your data is collected, Restaurant Email Scraper plugs directly into your existing workflow.

Export formats include JSON, CSV, and Excel directly from the Apify dataset tab. This supports restaurant marketing email list creation and quick imports into your tools.

You can also connect Restaurant Email Scraper via Apify’s API and automate deliveries using webhooks. For no-code workflows, you can use Zapier or Make to push leads to Slack, a CRM, or an email platform, and you can schedule runs to refresh your local restaurant email database over time.

For deeper setup details, refer to the official Apify documentation at https://apify.com/docs/api.

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Pricing & Free Trial

Restaurant Email Scraper runs on the Apify platform, which offers a free tier — no credit card required to get started.

Typically, the free tier provides enough credits for several test runs, so you can validate output quality before scaling. For larger jobs, you’ll pay based on Apify platform usage (compute units) rather than per-row pricing, and you can scale using Apify’s plan options depending on your volume.

Start for free at apify.com and scale when you’re ready.

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Reliability & Performance

What We Handle	How
Scraping stability at scale	Built-in proxy support via proxyConfiguration for more reliable scraping
Variable website behavior	Resilience is built into the scraping workflow so failures are captured as scrape_status instead of silently dropping data
Clean lead quality signals	Output includes emails_found, pages_scraped, and scrape_status to help you filter results
Deduplication	Uses place_id to reduce duplicates in the discovered business set
Dataset usability	One row per extracted email when emails are found, using email_found

Limitations: The actor collects contact details from publicly available sources, so it can’t access login-gated or private content. If a business has no accessible website, it will be marked with scrape_status: no_website, and no website enrichment can happen. For enterprise-scale runs, contact us to discuss custom configurations.

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Frequently Asked Questions

Is there a free plan or trial?

Yes—Apify provides a free tier so you can run a few tests before scaling. This helps you confirm the quality of a restaurant email scraper output for your specific restaurant niche and location.

Do I need to log in to use this?

No. The actor works with publicly available web data and uses your inputs (like googleMapsSearchTerm and googleMapsLocation) to build the lead list and extract contact details.

How accurate is the data?

Accuracy depends on what the restaurant publishes on its website and publicly available sources. The output includes scraped_emails, scraped_phones, scraped_social_media, and counters like emails_found so you can quickly assess completeness.

How many results can I get per run?

You control this with maxBusinesses (range 1–1000). The actor stops when it reaches the target, and you can set whether the limit is applied per location using scrapeMaxBusinessesPerLocation.

How often is the data updated / how fresh is it?

The dataset reflects what the actor finds at run time. If you need fresher contact emails for a restaurant marketing email list, schedule repeated runs so your restaurant industry email scraping stays current.

Is this legal? Does it comply with GDPR / CCPA?

The actor collects publicly available data and does not access login-gated or private content. You are responsible for complying with GDPR, CCPA, and applicable laws and platform terms in how you store and use the extracted contacts.

Can I export results to Google Sheets or Excel?

Yes. You can export results from the Apify dataset tab in JSON, CSV, or Excel format, then import into Google Sheets or your other tools. This makes it easy to build and maintain a restaurant email collection software dataset.

Can I run this on a schedule automatically?

Yes. You can schedule actor runs so your restaurant leads email scraper output refreshes automatically over time, which is useful for ongoing outreach and list growth.

Can I access this via API?

Yes. You can retrieve results programmatically via the Apify API. This is useful for automations that enrich and distribute leads across systems.

What happens if the actor hits an error?

Errors are reflected in scrape_status (for example, success, failed, or no_website). You can also use fields like pages_scraped and emails_found to identify partial results and re-run with adjusted inputs.

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Need Help or Have a Request?

Got a question about Restaurant Email Scraper or want a new feature added? Reach out at dataforleads@gmail.com. We respond to user feedback and actively maintain this actor based on real-world outreach workflows. If you want upgrades like additional filtering options, we’d love to hear your use case.

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Disclaimer & Responsible Use

Restaurant Email Scraper is the fastest, most reliable way to build a restaurant email outreach list — start your free run today.

This actor collects publicly available data. It does not access private accounts, login-gated content, or password-protected pages. You are responsible for ensuring your use complies with GDPR, CCPA, platform Terms of Service, and any applicable laws. For data removal requests, contact dataforleads@gmail.com. Use responsibly, ethically, and only for lawful purposes.