How to Scrape Google Maps in 2026: 5 Methods to Extract Business Data & Generate Leads

By the Scrap.io Team — Last Updated: March 2026

Last year I watched a friend of mine — runs a small roofing outfit near Nashville, maybe twelve guys total — spend his entire Monday morning copying business names and phone numbers from Google Maps into an Excel file. Row by row. For two hours straight. He'd been doing this every week for months.

I showed him how Google Maps scraping works. Ten minutes later he had 4,700 contractor listings for the whole state of Tennessee sitting in a CSV on his desktop. He just stared at the screen for a second and said "why didn't anyone tell me about this before?"

Fair question. Google Maps has over 200 million businesses indexed across 220+ countries (Google, 2024). Two billion people hit it every month looking for local services (SQ Magazine, 2025). All that business data — names, phones, websites, hours, reviews — just sitting there. Public. No login. And most people are still copy-pasting it by hand like animals.

This guide covers five different methods to scrape that data, from writing your own Python scripts to using platforms like Scrap.io that handle everything for you (free trial, 100 leads, no credit card drama). I'll also cover what data you can actually pull, where the legal lines are, who's doing this successfully, and why the web scraping market just crossed a billion dollars.

What Is Google Maps Scraping?

Strip away the jargon and it's dead simple. Google Maps data scraping — sometimes called web scraping Google Maps — means you use software to grab business info from Maps listings instead of doing it yourself click by click. Company name, phone, address, website, reviews, hours. All of it, pulled automatically into a spreadsheet you can actually use.

Who does this? Honestly, everyone at this point. Sales teams who need phone numbers and emails by the hundreds. Marketing agencies that build prospect databases filtered by city, industry, star rating. Competitive intelligence folks mapping out how many competitors operate within a 20-mile radius. I even know a real estate investor who scrapes it to find commercial landlords.

And the market backs this up. The web scraping industry crossed $1.03 billion in 2025 and analysts project it'll hit $2 billion by 2030 — that's a 14.2% growth rate year over year (Mordor Intelligence). Separately, a BrowserCat survey found that 65% of enterprises were already feeding scraped web data into AI and machine learning projects in 2024. Not startups. Enterprises.

One important thing I want to say upfront though: scraping public data doesn't mean it's a free-for-all. There are boundaries. I go deep into the legal stuff later in this article.

What Data Can You Extract from Google Maps?

There are basically two layers here, and the difference between them is the difference between "nice spreadsheet" and "actual sales weapon."

Layer one is everything visible directly on a Google Maps listing. You know — business name, phone number, address, star rating, hours. Basic stuff that any scraper can grab.

Layer two is where it gets good. Some tools don't just scrape the listing — they also visit the company's website (the one linked on their Maps profile) and pull additional data. Emails. Social media accounts. What CMS they're using. Whether they're running ads. That's enrichment data, and it's what makes scraped leads actually actionable.

Here's what each layer includes:

Data Field	Where It Comes From
Company name & business type	Maps listing
Phone number	Maps listing
Full address (street, city, ZIP, state, country)	Maps listing
GPS coordinates	Maps listing
Star rating & number of reviews	Maps listing
Operating hours	Maps listing
Photo count	Maps listing
Price range ($–$$$$)	Maps listing
Maps URL & claimed status	Maps listing
Email addresses (up to 5)	Website crawl
Social profiles (Facebook, Instagram, LinkedIn, etc.)	Website crawl
Website tech stack & CMS	Website crawl
Ad pixels (Facebook Ads, Google Ads, LinkedIn)	Website crawl
Meta title, description, keywords	Website crawl
Contact form presence	Website crawl

Think about what that second column means in practice. You scrape a list of 500 restaurants in Chicago. Now you don't just know they exist — you know which ones are on Wix vs WordPress, which ones run Facebook Ads, which have zero email listed anywhere, which haven't even claimed their Google listing yet. That's not a contact list. That's a segmented pipeline.

Want to go deeper on the email extraction side? I wrote a full walkthrough here: How to Find Email Addresses from Google Maps. For phone numbers specifically, there's this separate guide.

Why Google Maps Is the #1 Source for B2B Lead Generation

I've tested a bunch of lead sources over the years. LinkedIn, ZoomInfo, Apollo, manual prospecting. Google Maps beats all of them for local B2B, and it's not even close. Three reasons.

The sheer size of the database. 200 million+ businesses. 4,000+ categories. 220+ countries. And — this is the part people miss — the vast majority of those listings are small and medium-sized businesses. The 8-employee HVAC company. The solo accountant who just opened a second office. The family restaurant that's been around since 1994. That's who you're trying to sell to if you're in B2B services, and they're all on Maps.

The users searching Google Maps are ready to buy. This stat from BrightLocal caught me off guard: 88% of consumers use Google Maps to find local businesses (BrightLocal/SeoProfy, 2026). That alone is wild. But then WiserReview (2025) found that 80% of those local searches turn into a physical visit within a day. These aren't people casually browsing. They're people walking into stores. And businesses with filled-out Google profiles? 7× more clicks than incomplete ones (Google/Search Endurance, 2025). So the data you scrape literally corresponds to high-intent buyer activity.

There's no login wall. This might sound minor but it changes everything. Google Maps is public. You don't need an account, you don't need to connect with anyone, you don't need to worry about your profile getting flagged. Meanwhile, try scraping LinkedIn at scale. You'll spend half your time creating backup accounts because the main one keeps getting suspended. Facebook, Instagram — same circus. Google Maps just... doesn't have that problem.

Put those three together — scale, buyer intent, open access — and you've got the best source for Google Maps lead generation, period.

Top 5 Google Maps Scraping Methods Compared

OK here's where we get practical. Five methods, each with very different trade-offs. I made a comparison table because honestly every time I explain this verbally people's eyes glaze over by method three.

Method	Difficulty	Cost	Scale	Emails?	Best For
No-Code (Scrap.io)	Easy	$$	Country-level	✅	Sales teams, agencies
Python	Hard	Free–$	Custom	❌ (manual)	Developers
Chrome Extensions	Easy	Free	Limited	❌	Quick small jobs
PhantomBuster	Medium	$$$	Medium	❌	Automation users
Octoparse	Medium	$$	Medium	❌	Visual workflow fans

There's also a more detailed DIY vs professional scraper comparison if you want the long version.

Method 1 — No-Code Platforms (Scrap.io)

I'll be honest, this is what I use 90% of the time and I'm biased. But the reason is simple: I don't want to maintain code.

You go to Scrap.io, pick a business category from the 4,000+ listed on Google Maps, pick a location (country, state, city — whatever granularity you need), and it tells you exactly how many results match before you spend a cent. Hit export. CSV or Excel. That's the whole thing.

The data comes back in real time — not from some database that was scraped six months ago and is already half-stale. And it includes enrichment: emails, social media profiles, ad pixels, tech stack, the works. No proxies to manage, no CAPTCHAs, no code.

Method 2 — Python Scraping

If you're a developer and you have specific needs that no off-the-shelf tool covers, Python gives you full control. Selenium, Playwright, BeautifulSoup, Scrapy — the library ecosystem is huge.

Here's a stripped-down example just so you can see what the structure looks like:

from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get("https://www.google.com/maps/search/restaurants+nashville")
# scroll the sidebar, wait for elements to load...
results = driver.find_elements(By.CSS_SELECTOR, ".result-container")
for r in results:
    name = r.find_element(By.CSS_SELECTOR, ".title").text
    # extract phone, address, rating...

Looks elegant in a blog post. In reality? Google changes their Maps HTML every few weeks, so your selectors break constantly. You'll need proxy rotation. You'll need to handle JavaScript rendering. And you'll burn hours debugging things that worked fine yesterday.

GitHub has a ton of open-source google maps scraper repos if you want a head start, but maintenance on those varies wildly. If code isn't your strength at all, skip ahead — there's a reason no-code alternatives took off.

Method 3 — Chrome Extensions

Instant Data Scraper, Data Miner, G Maps Extractor. Free, fast, installed in 10 seconds. For scraping 50 listings for a quick research project, they're genuinely fine.

For anything beyond that? Problems start stacking up. You scrape using your own IP — get blocked and your whole browser is toast. Google Maps maxes out at 120 results per search (that's a platform cap, not a tool issue). And none of these extensions do website enrichment. No emails. No social links. Just the surface-level listing data.

I did a full rundown of the best Chrome extensions for Google Maps scraping if you want specifics.

Method 4 — PhantomBuster

I've used PhantomBuster for LinkedIn automation and it's a legit product. For Google Maps scraping specifically, you feed in a URL, run their pre-built Phantom script, and get a CSV.

My gripe is the pricing model. They charge by execution time. Not by how much useful data you get back. Your Phantom runs for 40 minutes and produces 300 clean leads? Great. It runs for 40 minutes and something goes wrong and you get 15 garbage rows? Same price. That unpredictability gets real annoying when you're trying to budget for client campaigns.

The execution time vs lead-based pricing comparison has the actual math. And there's a broader PhantomBuster alternative overview too.

Method 5 — Octoparse

Visual scraping tool with a drag-and-drop builder. The idea is that non-developers can build scraping workflows without code. And for some websites, that actually works.

For Google Maps specifically, it's hit or miss. You either build your own workflow from scratch — which means understanding XPath, regex, and loop logic — or you use one of their templates, which are easier but rigid and don't always grab the right fields. No email extraction. No social media. Just what's visible on the page.

If you're already invested in the Octoparse ecosystem it might be worth a shot. Otherwise, the Octoparse vs Scrap.io comparison lays out the differences pretty clearly.

Step-by-Step: How to Scrape Google Maps with Scrap.io

Alright, let me just walk through it quickly because the process is genuinely short.

Step 1: search. Open the Search tab. Two dropdowns — category and location. The categories mirror Google Maps directly (4,000+ of them). Location can be as broad as a country or as narrow as a ZIP code. Pick "Plumber" and "Tennessee" and you're ready.

Step 2: filter before you pay. This is honestly my favorite part because most Google Maps scraping tools make you export everything first and then sort through a mess of irrelevant data afterwards. Scrap.io lets you filter beforehand: only businesses with a website, only ones with an email, only claimed listings, minimum rating, minimum reviews, specific social networks, price range, even whether they have a contact form or run ad pixels.

Quick example of why this matters. Say you sell Facebook Ads management services. You can filter for businesses that have a website but do NOT have Facebook ad pixels installed. That's not a lead list — that's a list of companies who literally need what you sell and aren't buying it yet. That level of targeting before you even open the file is kind of unfair.

More details on the advanced filtering options here.

Step 3: export. Scrap.io shows the exact count of matching results before you commit. Choose how many rows, name your file, CSV or Excel, pick your columns. Done in seconds.

Here's a simplified version of what the export looks like:

Company	Type	Phone	Email	City	Rating	FB Ads
Joe's Plumbing	Plumber	(615) 555-0142	[email protected]	Nashville	4.6	No
Elite HVAC	HVAC	(615) 555-0198	[email protected]	Nashville	4.2	Yes
Carter Roofing	Roofing	(615) 555-0233	—	Nashville	3.9	No
GreenLeaf Landscaping	Landscaper	(615) 555-0311	[email protected]	Nashville	4.8	Yes
Quick Fix Electric	Electrician	(615) 555-0087	[email protected]	Nashville	4.4	No

The actual file has 40+ columns — social links, SEO data, ad pixels, GPS coordinates, working hours, everything. I just trimmed this for readability.

From there you can push the data straight into your CRM or go full no-code and build an automated prospecting pipeline using Make.com. But that's a topic for another day.

Google Maps Scraping: Real Use Cases & Results

I always get annoyed when scraping guides stay theoretical. "You can use it for lead gen!" Great, who's actually done that and what happened? Let me share what I've actually seen.

Outscraper's user reviews are publicly available and they tell a clear story. On Capterra and Trustpilot you'll find people who've documented pulling 120,000+ data points in a single year for market research projects. We're not talking about a few power users. Thousands of people scrape Google Maps data every single day through their platform alone. This is mainstream at this point.

There's a reddit thread on r/Entrepreneur that's pretty legendary at this point. Over 120 comments. It ranks on the first page of Google for "Google Maps scraping" — which says something about how much the community resonated with it. People sharing which tools they tried, what broke, what delivered actual leads, how much they spent vs what they made. The recurring pattern? Everyone who started with free Chrome extensions eventually hit the ceiling and switched to something dedicated. And the people who'd been burned by time-based billing models were very vocal about it.

Lead generation agencies have built whole business models around this. I've seen writeups on RapidSeedbox and similar industry blogs describing workflows where agencies scrape Maps listings for a specific city and category, then filter for businesses with websites but no email address listed, and pitch them email marketing or web design services. One workflow I came across was beautifully targeted: restaurants in mid-size US cities, rating between 3.5 and 4.2, website present, no Facebook ads pixel. That's a prospect who has customers but isn't doing digital marketing yet. That's a warm lead.

And then there's Apify — their Google Maps scraper actor has 907+ reviews with a 4.7 average. They process billions of pages monthly. That volume alone tells you the demand for Google Maps data extraction is growing, not shrinking.

For head-to-head tool comparisons: Outscraper vs Scrap.io and Bright Data vs Scrap.io.

The Web Scraping Market in 2026: Scale & Trends

I want to give you the broader context here because some people still think Google Maps scraping is a niche hacker thing. It's not. It's a billion-dollar industry with institutional buyers.

Global web scraping market: $1.03 billion as of 2025, on track for $2 billion by 2030 at a 14.2% CAGR (Mordor Intelligence). The growth comes from three places. Competitive intelligence budgets that keep going up. The explosion in demand for AI/ML training data. And B2B lead generation — the use case you're probably reading this article for.

The Google Maps numbers specifically are kind of absurd when you stack them up. 200 million+ businesses listed (Google/Loopex Digital, 2024). 2 billion monthly active users (SQ Magazine, 2025). 88% of consumers find local businesses through Maps (BrightLocal/SeoProfy, 2026). 80% of local searches convert to an in-store visit within 24 hours (WiserReview, 2025). Complete business profiles get 7× more clicks than empty ones (Google/Search Endurance, 2025). And businesses that link their website from their Google profile see around a 23% lift in organic traffic (SQ Magazine).

On the enterprise side, 65% of companies used scraped web data for AI projects in 2024 (BrowserCat). Not "considered." Not "piloted." Used. The pipeline from scraping to machine learning model is basically standard infrastructure now.

If you want to dig into the economics of Google Maps API costs vs dedicated scraping platforms, that comparison covers it in depth.

Is Google Maps Scraping Legal? Complete Compliance Guide

I get asked this constantly and honestly the answer deserves more nuance than most articles give it. So let me be specific.

Public Data Doctrine

In the US, there's real legal precedent for this. The hiQ Labs v. LinkedIn case went through the Ninth Circuit. LinkedIn argued that scraping their public profiles violated the Computer Fraud and Abuse Act. The court disagreed. The Supreme Court declined to take LinkedIn's appeal. What made that case interesting is that LinkedIn profiles are semi-public — you need an account to see them. Google Maps data is fully public. No account. No login. Nothing between you and the listing. If scraping semi-gated data is legal, scraping fully public data is on even firmer ground.

Google's Terms of Service

OK yes, technically Google's TOS prohibit automated access to their services. I'm not going to pretend otherwise.

But here's the other side of that coin: Google's entire search engine — the thing that made them one of the most valuable companies on Earth — works by automatically scraping other people's websites. Every day. At massive scale. So the "no automated data collection" clause has always been a little... let's say philosophically inconsistent.

In practice, millions of businesses and individuals use Google Maps scraping tools without any legal consequences. The ones who run into trouble are the ones doing reckless stuff — hundreds of thousands of requests per second, deliberately overloading servers. Professional tools don't operate like that. They manage request rates, rotate IP addresses, and stay within reasonable boundaries.

Privacy Regulations by Region

GDPR (EU): The key distinction that most people confuse: GDPR protects personal data. A company name, its business address, its phone number, its website — that's business information, not personal data. If a specific person's name and personal email show up in a scrape, then yes, GDPR handling requirements kick in. But standard business listing data from Google Maps? Not personal data in most interpretations.

CCPA (California): Same principle applied differently. Publicly available business information is carved out of CCPA's scope. You're scraping commercial data, not individual consumer profiles.

CAN-SPAM: This one governs what happens after you scrape, not the scraping itself. If you email a business contact you got from Maps, standard rules apply — unsubscribe link, real sender info, physical address in the email. No different from what you'd do with a business card you picked up at a conference.

Best Practices for Compliant Scraping

Keep it simple. Only scrape data that's publicly visible to any browser visitor. Don't try to get behind any login walls. Use professional tools that handle rate limiting. Skip anything that looks like personal data unless you know the compliance requirements for your jurisdiction. And if you're operating in a gray area or have real money at stake, talk to an actual lawyer — not a blog post.

Full legal breakdown: Is It Allowed to Scrape Google Maps?

Region	Regulation	Key Rule	Business Data	Personal Data
United States	First Amendment / CFAA	Public data broadly protected (hiQ v. LinkedIn)	✅ Allowed	⚠️ Depends on context
European Union	GDPR	Business data ≠ personal data in most cases	✅ Allowed	❌ Strict rules
California	CCPA	Public business info excluded from scope	✅ Allowed	⚠️ Opt-out needed
Global	CAN-SPAM / ePrivacy	Rules apply to email usage, not collection	✅ Collection OK	⚠️ Usage rules

Frequently Asked Questions

How much does Google Maps scraping cost?

All over the map (no pun intended). Python is technically free but your dev's time isn't. Chrome extensions cost nothing but cap at 120 results. PhantomBuster starts around $56/month and bills by execution time regardless of results — which can burn you. Scrap.io charges per lead with a free trial of 100 exports. The cost comparison between billing models is worth reading if budget matters to you.

Can you scrape Google Maps without coding?

That's literally why Scrap.io exists. Two dropdowns (category and location), some filters, an export button. No code, no command line, no API keys. Chrome extensions also work for tiny jobs but with very limited data. Full guide: Scrape Google Maps Without Python.

How do I scrape Google Maps for free?

Free Chrome extensions like Instant Data Scraper will pull basic listing data — names, addresses, ratings. No emails though. Building a Python scraper with open-source libraries is free too but will eat your weekends. Scrap.io gives you 100 google maps scraper free leads on a trial — enough to test a real market segment without committing.

Is there a Google Maps API for scraping?

There's the official Places API. It works but it's limited: 120 results per query, $32–40 per 1,000 requests, and no email or social data whatsoever. For one-off lookups it's fine. For actual google maps data extraction at scale, dedicated scraping platforms are both cheaper and more capable. More on that in the Google Maps API complete guide.

What is the best Google Maps scraper?

Depends what you're optimizing for. Full technical control? Python with Selenium. A quick one-time scrape? Chrome extension. Scale, emails, pre-export filtering, no code? Scrap.io. The comparison table earlier in this article breaks it all down by difficulty, cost, scale, and feature set.

Can you scrape Google Maps reviews?

Review counts and star ratings? Easily. Per-star breakdowns (how many 5-stars vs 1-stars)? Yes, most tools handle that. Individual review text? Possible with some tools, but tread carefully — review content can contain personal info, and that's where you start bumping into TOS boundaries.

Is it possible to scrape an entire country?

With the right google map extractor, absolutely. Scrap.io can pull every single listing for a given category across a country. Every dentist in the UK. Every restaurant in France. Every plumber in the entire United States. That kind of scale is the main dividing line between purpose-built Maps tools and generic scrapers that choke past a few hundred results.

What data can you extract from Google Maps?

Base layer: business name, address, phone, website, hours, star rating, reviews, photos, GPS coordinates. With enrichment: email addresses, social media profiles, website technologies, ad pixels, contact forms, SEO metadata. Scroll up to the data fields table for the complete breakdown.