How to Scrape Google Maps for an Entire Country: Complete Guide 2025

Google Maps contains 200 million establishments. Each listing = business data: address, phone, email, reviews, hours, social media, technologies used.

For a sales manager or an agency, it's a goldmine. But scraping a few hundred leads is different from scraping 50,000 or 140,000 businesses on a country scale.

This article shows you two approaches: the technical method (complex, limited) and the simple solution (comprehensive, fast). You will see real results, pitfalls to avoid, and how to choose based on your case.

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Why Scrape Google Maps at Country Scale?

Before diving into the technique, understanding the "why" changes everything.

Real use cases:

• B2B Prospecting: An SEO agency seeks all plumbers in a region to offer its services. Instead of searching manually (100+ hours), it exports 5,000 contacts in 5 minutes.
• Market Research: Analyze local competition. How many restaurants in Paris? What’s the average rating? How many reviews? The raw data answers.
• Lead Generation: Create a list of qualified prospects. Filter by rating (>4 stars), by presence of a website, by technologies used.
• Business Intelligence: Identify poorly rated companies (opportunity for reputation service), or well-rated ones (clients to study).
• Data Enrichment: You have a list of 10,000 companies. You seek their emails, phones, social media, Google reviews. Google Maps has all that.

The main challenge: Google Maps displays a maximum of 120 results per search. To scrape an entire country, you need to bypass this limit.

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Method 1: Technical Scraping with Python and Octoparse

Let's start with the "makeshift method." It works, but it requires time, technical skills, and produces limited results.

Step 1: Create a List of Communes/Cities

The trick: instead of searching "restaurant in France" (impossible directly), you search "restaurant in Paris," "restaurant in Lyon," "restaurant in Marseille"... 36,000 times.

For that, you first need a comprehensive list of French communes. Websites like communes.com list them by department.

Here’s the process:

1. Go to a site listing communes (by department)
2. Use a scraping tool (Octoparse, Apify, Selenium) to extract all names + postal codes
3. You get a CSV file with 36,000+ rows

Estimated time: 30-45 minutes to set up the scraper, 48 minutes in the cloud for execution.

Result: A file with commune names and postal codes.

Step 2: Configuring the Google Maps Scraper

Once the list of communes is ready, you create a scraping template for Google Maps.

With Octoparse:

1. Create a "task" that takes each commune as a variable
2. Configure the scraper to search "restaurant in [commune]"
3. Extract the fields: name, address, phone, rating, number of reviews, hours
4. Add delays between requests (2-5 seconds) to avoid blocks

Important limitation: Octoparse is limited to 1,000 results per task. If you have 36,000 communes, you need to create 36 tasks.

Estimated time: 2-3 days of continuous execution.

Step 3: Cleaning Data with Python

Once scraping is complete, you have raw data. It needs to be cleaned.

Common issues:

• Duplicates (the same restaurant appears for multiple communes)
• Inconsistent formatting (spaces, uppercase, special characters)
• Missing data (some restaurants don’t have a phone)
• Incomplete hours (only the current day is displayed)

With Python Pandas:

import pandas as pd

# Load the files
df1 = pd.read_csv('restaurants_1.csv')
df2 = pd.read_csv('restaurants_2.csv')

# Merge
df = pd.concat([df1, df2])

# Remove duplicates on name + address
df = df.drop_duplicates(subset=['nom', 'adresse'])

# Export
df.to_csv('restaurants_final.csv', index=False)

Estimated time: 1-2 hours (depending on volume).

Results of the Technical Method

After all this work, here’s what you get:

• 52,000 restaurants (after removing duplicates)
• 7 columns: category, name, rating, reviews, address, hours, phone
• Total time: 3-4 days
• Required skills: Python, scraping, data cleaning
• Maintenance: To be redone every month (Google Maps changes constantly)

Clear limitations:

• No emails (Google Maps does not display them directly)
• No social media
• No technologies used
• Incomplete hours (only the current day)
• No data enrichment
• Risk of blocking (Google may blacklist you)

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Method 2: Simple Solution with a Pre-Indexed Database

Now, the real question: do you really need to scrape?

There’s an alternative: use a pre-indexed Google Maps database. Instead of scraping in real-time (risky, slow, limited), you access a database that is already indexed, updated monthly, with 50M+ establishments.

IBLead is exactly that.

How It Works

1. You log into the app
2. You search by country, region, city, category
3. You apply filters (rating, reviews, email, website, technologies)
4. You export to CSV

No scraping, no code, no risk.

Real Results: Restaurants in France

With the same search (restaurants France), here are the results:

• 139,170 restaurants (vs 52,000 with the technical method)
• 44 data columns (vs 7)
• Time: 2 clicks, less than 5 minutes
• Required skills: None

The 44 columns include:

• Basic contact details: name, full address, postal code, city, region, phone
• Google Maps data: rating, number of reviews, detailed reviews (text, author, date), claimed listing or not
• Contact: emails (enriched from the website), contact forms
• Social media: Facebook, Instagram, YouTube, Twitter, LinkedIn
• Website: title, meta description, detected technologies (WordPress, Shopify, etc.), ad pixels
• Hours: complete grid (Monday-Sunday), not just the current day
• Photos: number and URLs
• Price range
• Reviews by rating (how many 5 stars, 4 stars, etc.)

Direct Comparison

Aspect	Technical Method	Pre-Indexed Database
Results	52,000	139,170
Columns	7	44
Time	3-4 days	5 minutes
Skills	Python, scraping	None
Emails	No	Yes (enriched)
Google Reviews	Number only	Full text + author + date
Technologies	No	Yes (160+ detected)
Maintenance	Monthly	Automatic
Blocking Risk	High	Zero
Price	0€ (personal time)	35-179€/month depending on volume

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How to Use a Pre-Indexed Database: Practical Steps

Suppose you are a digital marketing agency and you are looking for all restaurants in Paris with a website to offer your SEO services.

Step 1: Define Your Search

• Country: France
• City: Paris (or Île-de-France region)
• Category: Restaurants

Step 2: Apply Filters

• Must have: Website (to be able to do SEO)
• Minimum rating: 3.5 stars (serious clients)
• Minimum number of reviews: 20 (established establishments)
• Must have: Email or contact form

Step 3: Check Results

Before exporting, you see how many restaurants match. Example: 2,340 restaurants in Paris with a website and rating >3.5.

Step 4: Export to CSV

You click "Export" and you get a file with: - Name, address, phone - Email (enriched from the website) - Website - Social media - Rating and Google reviews - Site technologies (WordPress, WooCommerce, etc.)

Step 5: Use the Data

You import the CSV into your CRM or email tool and launch your prospecting campaign.

Total time: 15 minutes (instead of 3-4 days).

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Advanced Filters: Specific Use Cases

A pre-indexed database allows for filters that technical scraping cannot offer.

Case 1: Find Prospects with Bad Reputation

You offer an online reputation management service.

Filters: - Category: Hotels, restaurants, services - Google rating: <3 stars - Number of reviews: >50 (to be meaningful)

Result: 5,000 poorly rated French hotels = qualified prospects for your service.

Case 2: Target Users of a Competitor

You sell reservation management software.

Filters: - Category: Restaurants - Detected technology: HubSpot (or Salesforce, or any other competing CRM) - Region: Île-de-France

Result: 340 restaurants already using HubSpot = prospects to contact for migration.

Case 3: Find Businesses Without Web Presence

You offer website creation services.

Filters: - Category: Local services (plumbers, electricians, etc.) - Website: No - Region: All of France

Result: 45,000 plumbers without a website = huge market for your service.

Case 4: Analyze Local Competition

You are launching a restaurant in Lyon.

Filters: - Category: Restaurants - City: Lyon - Price range: Similar to yours - Sort by: Rating (descending)

Result: You see the 20 best restaurants in your segment = benchmark for your menu, pricing, service.

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Advantages of a Pre-Indexed Database vs Real-Time Scraping

1. Legality and Compliance

Mass scraping of Google Maps borders (or crosses) legal limits. Google Maps ToS prohibit automated scraping. A pre-indexed database complies with these conditions.

2. Data Stability

When you scrape, data changes constantly. A restaurant closes, another opens. A pre-indexed database is updated monthly = stable and verified data.

3. Automatic Enrichment

A pre-indexed database enriches data: it extracts emails from websites, detects technologies, retrieves complete reviews. Basic scraping does not do this.

4. No Maintenance

With scraping, you have to rerun your scripts every month, manage blocks, clean duplicates. With a pre-indexed database, everything is automatic.

5. Support and Documentation

A pre-indexed database offers customer support, documentation, updates. DIY scraping, you are on your own.

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Common Pitfalls of Google Maps Scraping

If you still decide to scrape, here are the pitfalls to avoid.

Pitfall 1: Blocking by Google

Google detects scrapers. If you send too many requests too quickly, you get blacklisted. Result: you can’t access Google Maps for days.

Solution: Add random delays (2-10 seconds) between requests, use proxies, respect robots.txt.

Pitfall 2: Incomplete Data

Google Maps displays data progressively. Hours, photos, reviews = dynamic loading. A basic scraper does not capture them.

Solution: Use Selenium (which simulates a browser) instead of raw HTTP requests.

Pitfall 3: Duplicates and Corrupted Data

The same establishment can appear multiple times (with name, address variations). Data can be inconsistent (poorly formatted hours, etc.).

Solution: Rigorously clean after scraping (deduplication, validation, formatting).

Pitfall 4: Limit of 120 Results

Google Maps displays a maximum of 120 results per search. For an entire country, you need to make thousands of requests (one per city).

Solution: That’s why the technical method takes 3-4 days.

Pitfall 5: Obsolete Data

Scraped data becomes obsolete quickly. A restaurant closes, an email changes. You need to rescrape every month.

Solution: Accept that your data will never be 100% up to date.

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When to Use Which Method

Use Technical Scraping If:

• You have Python/scraping skills
• You need very few columns (name, address, phone)
• You have an unlimited time budget
• You accept legal and technical risks
• You have a very specific case not covered by an existing database

Honestly: This is rare.

Use a Pre-Indexed Database If:

• You are looking for a quick and reliable solution
• You need emails, social media, reviews, technologies
• You don’t want to manage maintenance
• You accept to pay (35-179€/month)
• You work with multiple countries

This is the majority of cases.

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Real Cases: Measurable Results

Case 1: SEO Agency Prospecting Plumbers

Before (manual method): - 100 hours to search for 5,000 plumbers on Google Maps - Data: name, address, phone only - Response rate: 2% (emails not found)

After (pre-indexed database): - 30 minutes to export 5,000 plumbers - Data: name, address, phone, email, website, social media - Response rate: 8% (enriched emails, personalized prospecting) - ROI: 400% improvement in response rate

Case 2: Reputation Agency Finding Clients

Before (no data): - Impossible to find poorly rated hotels - Random prospecting

After (filter rating <3 stars): - 5,000 poorly rated hotels identified automatically - Ultra-targeted prospecting - Conversion: 15% of contacted prospects become clients

Case 3: Web Agency Analyzing Competition

Before (manual search): - 50 hours to analyze the top 50 sites in the region - Analysis: technology, design, features

After (pre-indexed database + filters): - 30 minutes to analyze the top 500 sites - Data: automatically detected technologies - Insights: 40% use WordPress, 20% WooCommerce, 10% Shopify

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FAQ: Frequently Asked Questions

Q1: Is it legal to scrape Google Maps?

**Short answer**