Complete Restaurant Directory Data Scraping: iens.nl & Eet.nu

About the Client

Location: Amsterdam, Netherlands

Industry: Market Research / Food & Beverage Analytics

Objective: To build a comprehensive dataset of all restaurants listed on two major Dutch directory sites—iens.nl and eet.nu—capturing:

Restaurant name
Business e-mail address
Total number of reviews
City where located

The client required no business to be missed, with results delivered in Excel (.xlsx/.csv) format as quickly as possible.

Actowiz Solutions was engaged because of its experience in directory scraping, data enrichment, and full-scale extraction pipelines.

The Challenges

Scraping large directories like iens.nl and Eet.nu involves several complexities:

Large Volume & Pagination: Each site lists thousands of restaurants, across multiple pages, filters, and regions. You must crawl all pages without missing any entry.
Dynamic Loading & JavaScript: Some content is loaded dynamically (AJAX), requiring headless browsers or script execution to uncover all entries.
Email Harvesting Difficulty: Many listings do not directly show email addresses. Some require visiting the restaurant's official site or contact page to extract the address.
Data Inconsistencies: Restaurant names, cities, and review counts may be formatted differently across listings, making normalization necessary.
Rate Limits & Blocking Risk: Aggressive crawling may trigger defenses or temporary bans. The scraper must include delays, proxy rotation, and error recovery.
Completeness Assurance: Ensuring that no restaurant is left out demands robust link discovery and sanity checks against directory totals.

Project Goals

Actowiz Solutions committed to:

Extracting every restaurant listing from iens.nl and Eet.nu
Capturing the four key data points per restaurant
Enriching with email addresses wherever possible
Delivering a clean Excel (XLSX/CSV) with distinct columns
Ensuring high data completeness and accuracy
Providing data quickly, with progress milestones

Technology & Tools

Function	Tools / Libraries
Core Crawling	Python (Scrapy + Requests + Playwright)
Dynamic Page Handling	Playwright / Selenium
Parsing	BeautifulSoup, lxml
Email Extraction	Regex, site crawling, contact-page discovery
Data Storage	Pandas (DataFrame), SQLite / CSV
Logging & Monitoring	Logging module, retry middleware
Proxy & Throttling	Rotating proxies, crawl delays, back-off logic
Output	Excel (XLSX) + CSV

Architecture & Workflow

[ iens.nl & Eet.nu Directory Pages ]

↓ [ URL Discovery & Pagination Crawler ]

↓ [ Listing Page Scraper (extract name, city, review count, email link if available) ]

↓ [ Restaurant Site Visit (if needed) to find email ]

↓ [ Data Cleaning & Normalization ]

↓ [ Deduplication & Completeness Checks ]

↓ [ Export to Excel / CSV ]

Key Steps:

Pagination & Region Discovery

Crawl all region and city-level pages to find all restaurant listing URLs.

Listing Extraction

On each listing page, extract:

Restaurant name
City
Total reviews
Email (if present)

Fallback Email Discovery

If no email on listing, follow the restaurant's website link (if available) and visit "Contact" or "About" pages to find email via regex patterns.

Cleaning & Normalization

Standardize city names
Convert review counts to integer
Remove whitespace and encoding errors

Deduplication & Completeness Assurance

Use restaurant name + city combination to detect duplicates
Cross-check listing counts per region with totals reported on directory (if available)
Flag missing entries for audit

Export Formats & Delivery

Output Excel file with columns: Business Name, Email, Total Reviews, City

Also deliver CSV for compatibility

Sample Dataset (Illustrative Example)

Business Name	Email	Total Reviews	City
De Hasselhoff Restaurant	info@hasselhoff.nl	215	Amsterdam
Bistro Van Gogh	contact@bistrovangogh.nl	142	Utrecht
De Pannenkoekenbakker	info@pannenkoekenbakker.nl	318	Rotterdam
Sushi House NL	info@sushihousenl.nl	54	The Hague
Café de Vrienden	cafedevrienden@gmail.com	78	Eindhoven

Performance & Milestones

Total Listings Extracted (Example): 9,500+

Email Addresses Found: ~6,800 (72%)

Cities Covered: ~220

Project Duration: ~6 days

Completeness Estimate: >98% listing coverage

Data Accuracy: ~99% on name/ city / review counts

Chart Idea – Email Coverage vs Listings by City

Create a bar chart per city (top 10 cities) showing:

Total restaurant listings
Number of those with email extracted

This highlights which cities had higher email coverage and where fallback discovery was needed.

Infographic Layout – "How We Built a Full Directory Scraper"

Implementation Highlights

Playwright or headless browser used for portions of listings or dynamic loading.
Retry and fallback logic for pages failing due to timeouts or changes.
Proxy rotation and crawl delays to minimize blocking risk.
Parallel crawling (within safe concurrency limits) to speed up extraction.
Logging module captures each failed URL, reason, retry count, and timestamp.

Example log entry:

Results & Insights

High Email Capture Rate: ~72% of listings had an email directly or via fallback.
City Distributions: Amsterdam had ~1,200 listings, Rotterdam ~800, Utrecht ~550, etc.
Top Cities by Email Coverage: Smaller towns had higher rates (fewer "no email" cases).
Review Counts Variability: Some listings had zero reviews; others had 500+ in major cities.

These insights help downstream analytics—for instance, targeting restaurants with reviews >100 and valid email.

Data Quality & Limitations

Known Gaps:

Some restaurants may use contact forms only (no email in text), which cannot be reliably extracted.
A few listings had email obfuscation (e.g. 'contact [at] domain'), requiring manual decoding.
Some dynamic listings only fully load via infinite scroll; small number of edge-case listings may get missed.

Mitigation Approach:

Flag entries with missing email for manual review.
Use heuristics and pattern matching to guess obfuscated emails (e.g. replace [at] tokens).
Cross-validate counts of listings per region with directory metrics (if available).

Impact for the Client

Delivered a complete, email-rich directory of restaurants across both platforms.
Replaced weeks of manual data collection with automated pipelines.
Enabled targeted outreach campaigns (email marketing, partnerships).
Provided dataset in ready-to-use Excel format—integrated with the client's CRM.

Why Actowiz Solutions

Proven expertise in large-scale directory scraping and email enrichment.
Experience with dynamic site rendering, fallback logic, and robust logging.
Ability to deliver clean, verified output on tight timelines.
Focus on compliance, error handling, and completeness.

Next Steps / Enhancements

Expand to other directories (TripAdvisor NL, Yelp NL, etc.).
Add additional fields like phone numbers, website URLs, opening hours, cuisine type.
Automate periodic updates (weekly/monthly) to keep data fresh.
Build a dashboard to visualize email coverage by city, review distribution, etc.

Conclusion

This case study shows how Actowiz Solutions tackled full-scale directory scraping across two large restaurant listing platforms in the Netherlands. The project delivered:

Nearly complete listing coverage
High email extraction rate
Clean, normalized records
Rapid turnaround

With robust retry logic, fallback paths, and classification, the client now has a marketing-ready dataset for outreach and analytics.

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Complete Restaurant Directory Data Scraping: iens.nl & Eet.nu

Download Case Studies

About the Client

The Challenges

Project Goals

Technology & Tools

Architecture & Workflow

Key Steps:

Pagination & Region Discovery

Listing Extraction

Fallback Email Discovery

Cleaning & Normalization

Deduplication & Completeness Assurance

Export Formats & Delivery

Sample Dataset (Illustrative Example)

Performance & Milestones

Chart Idea – Email Coverage vs Listings by City

Infographic Layout – "How We Built a Full Directory Scraper"

Implementation Highlights

Example log entry:

Results & Insights

Data Quality & Limitations

Known Gaps:

Mitigation Approach:

Impact for the Client

Why Actowiz Solutions

Next Steps / Enhancements

Conclusion

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