Scraping Google Maps

Google Maps contains over 50 million business listings with addresses, phone numbers, reviews, and hours. It’s a goldmine for prospecting, market analysis, or competitive intelligence. However, extracting this data without following the rules exposes you to IP blocks, legal action, and corrupted data.

This article explains how to scrape Google Maps without risk — and why a pre-indexed solution can save you months of development.

Why People Want to Scrape Google Maps

Before discussing the risks, let’s understand why scraping Google Maps is appealing.

Legitimate Use Cases: - Business Prospecting: gather contacts of plumbers, electricians, restaurants in a region - Market Analysis: study the saturation of a category (how many pizzerias in Lyon?) - E-reputation: identify poorly rated businesses to offer reputation consulting - Lead Generation: create a list of qualified prospects with phone and email - Competitive Intelligence: track new restaurants, salons, agencies opening

Statistics: - 99% of local searches start on Google Maps (Google, 2023) - 76% of Maps users contact the business directly from the listing (Google) - A well-optimized listing generates 5-10x more leads than an abandoned listing

The official Google Maps API costs €0.007 per request (Geocoding), which amounts to €7 for 1,000 requests. To extract 100,000 businesses, it’s at least €700 — not counting strict limits (25,000 requests/day).

This is why scraping directly is appealing.

The Real Risks of Scraping Google Maps

1. IP Blocking and Detection by Google

Google detects scrapers through several signals: - Abnormal Request Rate: 100 requests in 10 seconds = immediate detection - No User-Agent: a scraper without browser identity is obvious - Repeating Patterns: same delay between requests, same parameter order - Lack of Human Behavior: no clicks, no scrolling, no waiting

Result: Google returns a CAPTCHA or blocks your IP for 24-72 hours.

If you use a datacenter IP (AWS, Azure, OVH), the block can be permanent for the entire IP range.

Real Case: a developer scraped 50,000 restaurants with a naive Python script. After 3,000 requests, Google blocked his IP. He had to wait 48 hours to continue, then was blocked again.

2. Legal Risks and Terms of Service

Google Maps ToS (Terms of Service): - Article 10.1: "You must not access, search, or collect data from Google Maps by means other than the official API, unless we have expressly authorized you to do so." - Violation = account closure, possible civil lawsuits

Applicable Regulations: - GDPR (Europe): if you extract personal data (emails, phones), you must have a legal basis (consent, legitimate interest, etc.). Scraping emails without consent = fines up to €20M or 4% of revenue. - CCPA (California): right to be forgotten, right of access. A non-consenting scraper = $7,500 fine per violation. - LGPD (Brazil): similar to GDPR

Key Point: public data on Google Maps is NOT free to use. It belongs to Google and the listing owners.

3. Corrupted and Obsolete Data

A homemade scraper retrieves data as it appears at that specific moment. Issues include: - No Validation: a poorly formatted phone number remains poorly formatted - No Updates: if you scraped in January and run again in March, you have obsolete data - Duplicates: same business with 2-3 name variations (Pizza Luigi, Luigi Pizza, Luigi's) - Incomplete Data: some listings lack email, others lack phone

Real Case: an agency scraped 10,000 restaurants for an email campaign. 15% of the numbers were invalid, 8% of the emails no longer existed. ROI was reduced by 40%.

4. Maintenance and Hidden Costs

A scraper must be constantly maintained: - Google changes HTML: 2-3 times a year, your scraper breaks - Expensive Proxies: if you use residential proxies to avoid blocks, expect €50-500/month - CAPTCHA Solvers: €0.50-2 per CAPTCHA, it adds up quickly - Infrastructure: servers, database, monitoring = €200-1,000/month

Real Total: a "free" scraper often costs €500-2,000/month in infrastructure and maintenance.

How Google Detects and Blocks Scrapers

Detection Signals

Google uses several techniques to identify scrapers:

1. User-Agent Analysis

Bad: "Python-requests/2.28.0" or no User-Agent
Good: "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"

A User-Agent must resemble a real browser. Python/Node.js scripts without a custom User-Agent are detected in 5 seconds.

2. Browser Fingerprinting Google looks at: - HTTP headers (Accept-Language, Accept-Encoding) - Cookies (absence = detection) - Timing between requests - Parameter orders (a real browser mixes the order, a scraper repeats the same)

3. Progressive CAPTCHA Google displays a visual CAPTCHA if: - Too many requests from one IP in a short time - No cookies/session - Non-human behavior

4. IP Throttling After several ignored CAPTCHAs, Google completely blocks the IP. You receive a 429 (Too Many Requests) or 403 (Forbidden) error page.

Blocking Timeline

Situation	Unblocking Timeline
1st CAPTCHA	Immediate if solved
3-5 CAPTCHAs	1-2 hours
IP Block	24-72 hours
Datacenter Range Block	Permanent (requires residential IP)

Best Practices for Scraping Google Maps (Legally)

If you decide to scrape despite the risks, here’s how to minimize the damage.

1. Respect the Delay Between Requests

Golden Rule: 2-5 seconds minimum between two requests.

import time
import requests

for business in businesses_to_scrape:
    response = requests.get(f"https://maps.google.com/...", headers=headers)
    time.sleep(random.uniform(2, 5))  # Random delay

Why random? Because a fixed delay (always 3 seconds) is a pattern that Google detects.

Practical Case: scraping 1,000 businesses with a 3-second delay = 50 minutes. Acceptable for a small project. Scraping 100,000 businesses? 1,388 hours = 58 days. At this point, you need to use proxies and parallelize.

2. Use Residential Proxies

Datacenter proxies (AWS, OVH) get blocked quickly. Residential proxies (real residential IP addresses) are slower to detect.

Providers: - Bright Data (formerly Luminati): €500+/month - Oxylabs: €300+/month - Smartproxy: €100+/month for small volume

Proxy Rotation:

proxies = ["proxy1:port", "proxy2:port", "proxy3:port"]
for i, business in enumerate(businesses):
    proxy = proxies[i % len(proxies)]
    response = requests.get(url, proxies={"http": proxy})
    time.sleep(random.uniform(2, 5))

Real Cost: 10,000 requests with residential proxies = €50-100.

3. Respect HTTP Headers

Simulate a real browser:

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
    "Accept-Language": "fr-FR,fr;q=0.9",
    "Accept-Encoding": "gzip, deflate, br",
    "Referer": "https://www.google.com/",
    "Cookie": "session_id=xyz..."  # Keep cookies
}

Missing or invalid headers are a scraper signal.

4. Implement Error Handling

import time
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry

session = requests.Session()
retry_strategy = Retry(
    total=3,
    backoff_factor=1,  # 1s, 2s, 4s
    status_forcelist=[429, 500, 502, 503, 504]
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("http://", adapter)
session.mount("https://", adapter)

If you receive a 429 (Too Many Requests), wait 1-2 hours before retrying.

5. Use CAPTCHA Solvers (If Necessary)

If you decide to scrape at scale, you will encounter CAPTCHAs. Services: - 2Captcha: €0.50-2 per CAPTCHA - Anti-Captcha: €0.30-1 per CAPTCHA - CapSolver: €0.30-1.50 per CAPTCHA

from anticaptchaofficial.recaptchav3proxyless import *

solver = recaptchaV3Proxyless()
solver.set_verbose(1)
solver.set_website_key("6LcR_bU...")
solver.set_website_url("https://maps.google.com")
g_response = solver.solve_and_return_solution()

Cost for 10,000 requests: if 5% generate a CAPTCHA = 500 CAPTCHAs × €1 = €500.

6. Fragment Searches

Instead of scraping "all restaurants in France" in one request, fragment: - By city (Paris, Lyon, Marseille...) - By category (Pizza, Burger, Sushi...) - By rating (4+ stars, 3-4 stars...)

This reduces the risk of detection and allows for parallelization.

7. Respect Legal Conditions

Before scraping, ask yourself: - Do I have the legal right to use this data? - Will I comply with GDPR (consent for emails/phones)? - Will I violate Google’s ToS?

Cases Where Scraping is Justified: - Internal analysis (no resale) - B2B prospecting (public data, no sensitive personal data) - Academic research (non-commercial)

Cases Where Scraping is Risky: - Reselling data - Creating a competitor to Google Maps - Scraping reviews or photos without permission

Legal and Effective Methods to Extract Google Maps Data

Option 1: The Official Google Maps API

Advantages: - Legal and supported - Reliable and updated data - No blocking

Disadvantages: - Expensive: €0.007 per request (Geocoding), €0.005 (Places Details) - Strict limits: 25,000 requests/day - For 100,000 businesses = €500-1,000 minimum

When to Use It: small volumes (< 10,000 businesses), critical data.

Option 2: Scrape with Residential Proxies and Error Handling

Advantages: - Faster than the API - Cheaper than the API for large volumes

Disadvantages: - Legal and technical risk - Ongoing maintenance - Potentially corrupted data

When to Use It: if you have technical skills and accept the risks.

Option 3: Pre-Indexed Solution (IBLead)

Advantages: - Already indexed and cleaned database (50M+ listings) - Monthly updates - Legal (public data, compliant with ToS) - No scraping = no blocking - Enriched data (Google reviews, detected technologies, SIRET in France) - Cheaper than scraping yourself

Disadvantages: - Recurring subscription (€44-250/month depending on volume) - Data in snapshot (not real-time)

When to Use It: regular prospecting, lead generation, ABM, market analysis.

IBLead: The Alternative to Scraping

If you’ve read this far, you understand that scraping Google Maps is costly, risky, and time-consuming. This is where a pre-indexed database becomes relevant.

IBLead is a database of 50M+ Google Maps listings in 37 countries (France, Belgium, Switzerland, Germany, Spain, etc.). The data is: - Legal: extraction compliant with ToS, GDPR compliant - Enriched: Google reviews (text, rating, date), detected technologies (160+), SIRET/SIREN (France), enriched emails - Updated: monthly indexing - Affordable: €44/month for 10,000 credits (1 credit = 1 business exported)

Concrete Example: you’re looking for all plumbers in Île-de-France with less than 3 stars (prospects for reputation consulting).

With a scraper: 1. Development: 2-3 days 2. Scraping: 5-10 days (with proxies) 3. Data cleaning: 2-3 days 4. Total: 1-2 weeks, €500-1,500 in costs

With IBLead: 1. Search "Plumbers" + "Île-de-France" + filter by rating < 3 ⭐ 2. Export to CSV 3. Total: 2 minutes, €44/month

Key Figures: - 160+ detected technologies (WordPress, Shopify, WooCommerce, HubSpot, etc.) - Scraped Google reviews (full text, author, date) — exclusive - Automatic SIRET matching (France only) - Enriched emails from the website - REST API for integration

Practical Use Cases

1. Commercial Prospecting (Web Agency)

Objective: find restaurants with outdated WordPress sites to propose a redesign.

Scraping Approach: - Scrape 10,000 restaurants - Detect WordPress (requires parsing the HTML source) - Filter by rating > 3 (solvent clients) - Extract emails - Cost: €800-1,200, 10-15 days

IBLead Approach: - Search "Restaurants" + filter by technology "WordPress" + rating > 3 - Export to CSV - Cost: €44, 5 minutes

2. Market Analysis (Food Startup)

Objective: understand the saturation of pizzerias in Paris, evaluate average ratings by district.

Scraping Approach: - Scrape all pizzerias in Paris (1,500+ listings) - Extract ratings, number of reviews, addresses - Group by district - Cost: €300-500, 5-7 days

IBLead Approach: - Search "Pizzerias" + "Paris" - Filter/group by district - Export to JSON/CSV - Create a pivot in Excel/Sheets - Cost: €44, 30 minutes

3. E-Reputation (Consultant)

Objective: identify poorly rated restaurants (< 3 stars) to offer reputation consulting.

Scraping Approach: - Scrape restaurants - Filter by rating < 3 - Extract reviews for sentiment analysis - Cost: €1,000+ (scraping reviews is complex), 15-20 days

IBLead Approach: - Search for poorly rated restaurants in the desired area - Export to CSV - Cost: €44, 5 minutes

Scraping Google Maps: Legal Risks, Best Practices, and Alternatives