Google Maps is packed with business and location data, such as addresses, phone numbers, ratings, and reviews. Manually extracting this data, however, is time-consuming and inefficient – especially for companies that need large amounts of data quickly for lead generation, market analysis, or competitive analysis.
What is Google Maps scraping?
Google Maps is not just a system of navigation – it’s also a business, location, and consumer knowledge database. Millions of businesses list their information in Google Maps, providing users with addresses, phone numbers, business hours, and consumer reviews.
For businesses and researchers, this information can be of great use for everything from lead generation to market analysis, to competitive analysis. Web scraping is an efficient and time-saving method of extracting this information at scale. Scraping Google Maps allows businesses to automate data collection, gaining access to key insights that would otherwise require manual searches.
Some of the key Google Maps features that can be extracted include:
- Google My Business / Google Business Profiles: provides information on business names and categories, locations and contact details, and operating hours and services offered.
- Google Map Email Extractor: Many businesses list their emails on their Google Maps profile, making it a valuable source for B2B outreach, sales prospecting, and partnership opportunities.
Some of the types of data that can be scraped from Google Maps include:
- Business names: The first step in building a targeted database for sales, research, or industry analysis.
- Locations and addresses: Help businesses analyze geographic distribution, identify expansion opportunities, and improve logistics and delivery planning.
- Reviews: Provide insight into consumer sentiment, brand reputation, and service quality. Analyzing reviews at scale helps businesses identify trends and areas for improvement.
- Business information: Knowing when businesses are open and what services they offer allows market researchers and aggregators to create valuable industry reports and directories.
- Business contact information: Phone numbers and email addresses can be useful for sales prospecting, outreach campaigns, and partnerships.
- Ratings and star reviews: Enables customer satisfaction analysis, competitor benchmarking, andqualitycontrol assessments.
- Websites: Allow scrapers to collect additional company information.
- Social media links: Some businesses provide links to their Facebook, Instagram, or Twitter/X pages, which can be valuable for social media marketing and competitive analysis.
When geographic data is layered with broader market signals, it often feeds into our data-backed reports used by clients for strategic planning.
Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.
Datamam’s CEO and Founder, Sandro Shubladze, says: “Since there is no easy way to extract Google Maps data in bulk, companies need to extract tools that work automatically. With responsible and efficient means of scraping, companies can take unprocessed Google Maps data and turn it into actionable business intelligence without sacrificing compliance with the requirements of the law.”
Why scrape Google Maps?
Google Maps is a rich source of business and geolocated data that yields useful insights for many different industries. Individuals and businesses can use web scraping to automatically extract data to assist decision-making .
Competitor analysis
Businesses can scrape Google Maps to analyze competitor locations, customer ratings, and services offered. This helps companies identify market gaps, understand customer sentiment towards competitors, and benchmark their own online presence.
For example, a restaurant chain may scrape competitor data to determine which locations have the highest foot traffic and best reviews, helping inform expansion strategies.
Route planning
Logistics and transportation companies use Google Map data extractor to optimize delivery routes, identify high-traffic areas, and enhance fleet efficiency. By scraping business locations and traffic data, they can reduce delivery times and costs, improve last-mile logistics, and identify new service areas.
Real estate and property listing research
Real estate professionals scrape Google Maps to analyze business density and neighborhood growth, track property values based on nearby amenities, and identify emerging commercial hubs.
For instance, a real estate agency may use Google Maps data to track new business openings in a city, helping clients find high-growth investment opportunities.
Brand monitoring
Companies scrape Google Maps reviews and ratings to track their reputation across different locations. This helps them identify customer complaints and satisfaction trends, monitor brand perception across different cities, and compare store performance in various regions.
Customer sentiment analysis
Google Maps reviews provide businesses with first-hand customer feedback. By analyzing this data, companies can improve service quality, identify frequent customer complaints, and develop data-driven marketing strategies.
For example, a hotel chain may use a Google Map data extractor for reviews to analyze guest satisfaction levels across different locations and pinpoint areas needing improvement.
Lead generation
Businesses can extract contact details, websites, and emails from Google Maps listings to generate B2B sales leads. This is particularly useful for marketing agencies looking for local business clients, suppliers identifying potential retail partners, and B2B companies expanding their outreach efforts
A digital marketing firm, for instance, may scrape Google Business Profiles to collect contact information for small businesses needing SEO services.
Logistics and deliveries
E-commerce and delivery companies use Google Maps data to identify optimal warehouse locations, track customer demand in specific areas, and plan efficient delivery routes. A food delivery service, for example, might scrape Google Maps data to track restaurant locations and peak business hours to optimize driver assignments.
Sandro says: “Google Maps is a key resource for business entities that require evaluating market trends, identifying prospects, and making processes more efficient.” “From monitoring competitors’ whereabouts to deciphering consumer sentiment to making logistics more efficient, having structured access to Google Maps is paramount.”
Is it legal to scrape Google Maps?
Scraping data that Google Maps extract does hold some complex legal and ethical challenges. Although it is not generally illegal to extract public information, it is crucial to be familiar with and compliant with Google’s Terms of Service (ToS) and the relevant laws and regulations.
In 2011, Google introduced usage limits to its Maps API, up to a maximum of 25,000 loads per day for free use. Anything more required a premium plan. Later, in December 2024, Google expanded its free use limit, offering up to $3,250 of free use per month starting on March 1, 2025. The action is to allow developers more flexibility without sacrificing fair use.
Despite this recent expansion, Google’s ToS strictly prohibits unauthorized scraping, and not respecting the rule can lead to account suspension or an IP ban.
Beyond ToS violations, users must also consider such legal frameworks as the US Computer Fraud and Abuse Act (CFAA), which prohibits accessing computer systems without permission, and data protection legislation such as the General Data Protection Regulation (GDPR) that places strict controls on collecting and processing personal data.
As a best practice for ethical scraping, users should use official APIs. Google offers APIs that provide access to specific data within defined usage limits. While these APIs may not offer all the data available on Google Maps, they can be used within Google’s guidelines.
It is important to respect rate limits, and avoid sending excessive requests in a short period, which can overload servers and lead to IP bans. Also, personal data should be avoided. Refrain from collecting personal information unless you have explicit consent and a legitimate purpose.
Given the nature of such complications, working in partnership with expert data providers can guarantee adherence to legal and ethical requirements. Specialist providers know the potential pitfalls of data collection and can provide solutions.
For a closer look at how we’ve approached public sector data extraction, check out our government procurement scraping case study.
Sandro says: “Scraping Google Maps is technically and legally challenging, requiring companies to walk a thin line between data requirements and compliance. Publicly available information seems to be easily accessible, yet Google’s Terms of Service explicitly prohibit automated collection without explicit permission.”
How to scrape Google Maps
Scraping Google Maps requires a structured approach and the right tools to efficiently extract location-based data. Here is a step-by-step guide on how to scrape Google Maps using Python and web scraping libraries like BeautifulSoup, lxml, requests, and pandas.
1. Set up and planning
Before scraping, determine what information you need to extract, and in which Google Maps pages you can find it. As a general rule, you can extract business names, addresses, phone numbers, ratings, and reviews legally and ethically.
Google Maps is strict regarding anti-scraping, so it is important to be cautious not to send multiple requests in a short period to get banned via IP. Proxies and rotation of user-agents can be employed to help mitigate detection possibilities.
2. Install the required Python libraries
Python offers several libraries for web scraping, for example:
- requests: Fetches the HTML content of a webpage
- BeautifulSoup: Parses and extracts data from HTML
- lxml: Improves parsing efficiency for large-scale scraping
- pandas: Stores and processes extracted data in structured formats
Install using:
pip install requests beautifulsoup4 lxml pandas3. Send requests to Google Maps
Google Maps URLs often have dynamic content that loads via JavaScript, making direct HTML scraping difficult. However, for basic scraping, we can send an HTTP request to extract raw page data:
import requests
url = 'https://www.google.com/maps/search/cafes+in+New+York'
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36'
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
print('Page retrieved successfully')
page_content = response.text
else:
print(f'Failed to retrieve page, status code: {response.status_code}')If Google blocks the request, consider using proxies or a headless browser like Selenium.
4. Extract data from Google Maps
Once the HTML content is retrieved, use BeautifulSoup to extract useful information such as business names and locations.
Since Google Maps loads most data dynamically, API-based extraction or Selenium may be required for more advanced scraping.
from bs4 import BeautifulSoup
soup = BeautifulSoup(page_content, 'lxml')
# Extract businesses
businesses = soup.find_all('div', {'class': 'business_class'}) # Adjust based on site structure
from bs4 import BeautifulSoup
soup = BeautifulSoup(page_content, 'lxml')
for business in businesses:
print(business.text.strip())5. Parse and store data
After extracting data, store it in a structured format using pandas:
import pandas as pd
business_list = []
for business in businesses:
business_dict = {
'business_name': business.find('a').text.strip(),
'business_url': business.find('a')['href'],
'business_rating': business.find('div', {'class': 'rating'}).text.strip()
}
business_list.append(business_dict)
df = pd.DataFrame(business_list)
df.to_csv('google_maps_data.csv', index=False, encoding='utf-8')
print('Data saved to google_maps_data.csv')6. Implement error handling
To prevent script failures, implement basic error handling:
try:
response = requests.get(url, headers=headers)
response.raise_for_status() # Raise error for bad responses
except requests.exceptions.RequestException as e:
print(f'Error fetching page: {e}')
Sandro says: “Scraping Google Maps requires technical precision and respect for ethical standards. Most companies attempt to do it via direct HTML scraping, but due to JavaScript rendering and bot protection, approaches such as utilizing Google Maps APIs or using headless browse tools work better in most instances.”
While this article focuses on location data, we’ve also applied similar scraping techniques in legal domains see our case study on court dockets scraping for a practical example.
What are the challenges of scraping Google Maps?
Anti-scraping techniques
Google has one of the most aggressive anti-scraping systems, designed to detect and block automated requests. Some of the key challenges include:
- Rate limiting: Google restricts how frequently requests can be made from the same IP address.
- CAPTCHAs: Automated requests may trigger security checks, making it difficult to continue scraping.
- IP blocking: Repeated scraping attempts can lead to temporary or permanent bans.
To mitigate these issues, scrapers should use proxies, rotate IPs, and limit request frequency to avoid detection.
Dynamic content
Google Maps is loaded in JavaScript, making it difficult to extract info using plain web scraping methods. Most of the information such as business details, reviews, and ratings is not in the initial HTML response.
To deal with this, scrapers use headless browsers (for instance, Selenium, Puppeteer) or network request interception mechanisms to get dynamically loaded data.
Maintenance and website changes
Google frequently updates its website structure and algorithms, making previous scraping scripts obsolete overnight. A scraper that worked today may break after a UI change.
To avoid disruptions, businesses should monitor changes in Google Maps’ HTML structure, and use flexible parsing techniques that adapt to modifications. They can also consider employing machine learning models to detect website updates in real time.
Data accuracy issues
Google Maps data is user-generated, which means some of the business names, addresses, and categories could be inconsistent, and some listings could be duplicated, outdated or incorrect. Scraping raw data is not enough – data cleaning and validation are necessary to ensure accuracy.
Sandro says: “Scraping Google Maps is not a question of pulling in data—it’s a question of ensuring accuracy, reliability, and compliance. With Google’s frequent website refreshes and aggressive anti-bot strategies, many businesses find it hard to sustain a working data collection process.”
“This is where Datamam comes in. We provide custom, scalable scrapers that adapt to Google Maps’ changes in real-time. Smart IP rotation, advanced parsing strategies, and around-the-clock surveillance are all included in our method to supply high-quality, structured data without disruption.”
We’ve handled location-based data projects across various industries. You can learn more about our background and team on our About page.
Given the technical and legal challenges of scraping Google Maps, businesses often need customized data extraction solutions. Datamam offers:
- Compliant data scraping strategies that minimize risk
- Automated monitoring and maintenance to keep scrapers functional despite website changes
- Advanced anti-detection techniques to avoid CAPTCHAs and IP bans
- High-quality, structured Google Maps data for market analysis, lead generation, and business intelligence
For more information on how we can assist with your web scraping needs, contact us today!
