How to Scrape Google Maps Data Without Coding

Google Maps is one of the most useful places to find local business information. Sales teams use it to find prospects. Agencies use it to build local lead lists. Market researchers use it to compare business categories across cities. Founders use it to understand local competition before entering a new market.

But manually copying business names, addresses, websites, phone numbers, ratings, and reviews from Google Maps is slow. That is why many users search for a way to scrape Google Maps data without coding. They want a simple workflow: search for businesses, collect useful fields, export the results, clean the data, and use it in a spreadsheet or CRM.

Before starting, there is one important point. Google’s Maps Platform Terms include restrictions on scraping, exporting, storing, or copying Google Maps content outside its services. Teams should review the applicable terms, use official APIs where required, and avoid collecting restricted, private, or sensitive data.

What Does “Scrape Google Maps Data” Mean?

To scrape Google Maps data means to collect visible business information from Google Maps and turn it into a structured dataset.

A structured dataset is easier to use than raw page content. Instead of copying one business profile at a time, a scraper organizes information into rows and columns, such as business name, address, phone number, website, rating, review count, category, and Google Maps URL.

For non-technical users, “without coding” usually means using a no-code scraper, browser extension, workflow tool, or ready-made data extraction platform. These tools usually ask for a search query, location, or Google Maps URL, then export the collected results into CSV, Excel, or JSON.

Several no-code Google Maps scraping tools position themselves around this exact workflow: search Google Maps, select or extract business data, and export results to CSV or Excel.

What Google Maps Data Can You Collect?

The exact fields depend on the tool, the location, the business profile, and what is publicly visible. Common fields include:

For example, a local SEO agency may search for “dentists in Austin” and filter businesses with low review counts. A sales team may search for “restaurants in Chicago” and collect names, phone numbers, websites, and categories. A market research team may compare coffee shops across several cities.

The goal is not just to collect as much data as possible. The goal is to collect the fields that help answer a specific business question.

No-Code vs API: Which Method Should You Use?

There are two common ways to collect Google Maps-style business data: no-code scraping tools and the official Google Places API.

The Google Places API is part of Google Maps Platform and supports place-related features such as Text Search, Nearby Search, Place Details, and Place Photos. It is best for developers building applications that need official place data, live search, or location features.

The API is powerful, but it usually requires technical setup. Developers need to manage API keys, billing, field masks, requests, responses, storage, and data processing. Google’s Places API documentation also explains that users must specify at least one field mask when requesting place data, which affects what data is returned and how requests are billed.

A no-code scraper is easier when the user simply wants a spreadsheet of business listings. It is better for sales research, local lead generation, competitor mapping, and one-time data exports.

Use the API when you need official data inside an app. Use a no-code workflow when you need a practical dataset for analysis, outreach, or research.

How to Scrape Google Maps Data Without Coding: Step-by-Step

Step 1: Define the exact business question

Start with the outcome, not the tool.

Bad goal: “Scrape businesses from Google Maps.”Better goal: “Collect dental clinics in Austin with websites, phone numbers, ratings, and review counts.”

A clear goal helps you choose better keywords, avoid irrelevant results, and reduce cleanup work later.

Good examples include:

• Restaurants in Seattle with phone numbers and websites
• Gyms in Los Angeles with fewer than 100 reviews
• Plumbers in Denver with high ratings
• Real estate agencies in Phoenix
• Coffee shops in San Diego for competitor research

Step 2: Choose a no-code Google Maps scraper

A no-code scraper should be easy to run, support useful input options, and export structured results. Look for features such as:

• Keyword and location search
• Google Maps URL input
• Business profile extraction
• CSV or Excel export
• JSON export for technical users
• Filters for category, rating, review count, or contact availability
• Clear pricing and usage limits
• Data cleaning or deduplication options

Some tools are browser extensions. Others are cloud-based scraping platforms. Some are better for small manual exports, while others are better for recurring lead generation workflows.

Step 3: Enter your search terms and location

Most no-code tools let you enter a business category and location. For example:

• dentists in Austin
• coffee shops in Brooklyn
• roofing contractors in Denver
• gyms in Miami
• law firms in Chicago

Avoid using too many similar keywords at once. “Dentist,” “dental clinic,” and “dental office” may return overlapping results. If the tool supports deduplication, use it. If not, plan to remove duplicates after export.

Step 4: Run a small test first

Do not start with thousands of records immediately. Run a small sample first and check the output.

Look for:

• Are the business names correct?
• Are phone numbers and websites captured?
• Are addresses complete?
• Are categories useful?
• Are ratings and review counts included?
• Are there many duplicate locations?
• Are closed or irrelevant businesses included?

A small test saves time and prevents messy exports.

Step 5: Export the results

For most users, CSV or Excel is the easiest format. It works well for filtering, sorting, deduplication, and manual review.

JSON is better for developers or teams connecting the data to an internal system. Some tools also provide API access for recurring workflows.

A useful export should include the source URL. This makes it easier to audit the record later and check whether the business information still looks correct.

How to Clean and Use the Exported Data

Scraping is only half the workflow. Cleaning the data is what turns a raw export into a useful list.

Start by removing duplicates. Local businesses may appear under similar names, multiple branches, or repeated search queries. Next, remove irrelevant categories. For example, a search for “marketing agencies” may include consultants, print shops, or unrelated advertising services.

Then review missing fields. A record with no website may still be useful for a web design agency, but less useful for a campaign that requires email enrichment. A record with no phone number may be less useful for call outreach.

Add your own columns, such as:

• City
• Business type
• Priority
• Campaign
• Lead status
• Notes
• Last checked date

For sales use cases, do not rely only on scraped data. Verify important fields before outreach. If email addresses are included or enriched, use an email verification step before sending campaigns.

Common Mistakes to Avoid

The first mistake is collecting too much data. More rows do not always mean better leads. A smaller, cleaner list is usually more useful than a large, messy one.

The second mistake is ignoring terms and compliance. Google’s terms place restrictions on scraping and using Google Maps content outside its services, so teams should review the rules carefully and consider the official API where appropriate.

The third mistake is skipping manual review. Business data changes often. Phone numbers, websites, hours, and business status may become outdated.

The fourth mistake is importing raw exports directly into a CRM. Clean the data first. A messy CRM creates duplicate accounts, poor reporting, and wasted sales time.

The fifth mistake is sending generic outreach. Google Maps data can help identify local businesses, but outreach should still be relevant, personalized, and compliant with email and marketing rules.

Final Thoughts

Scraping Google Maps data without coding is popular because it solves a real problem: local business information is useful, but manual copying is slow. A no-code workflow can help users collect business listings, export structured data, clean the results, and use the dataset for lead generation, sales research, local SEO, or market analysis.

The best workflow starts with a clear goal. Choose the business category and location, run a small test, export the data, remove duplicates, review important fields, and verify contacts before outreach.

For teams that need a simpler path, ready-made data collection tools can reduce manual work and turn public local business information into cleaner CSV, Excel, or JSON outputs. For teams that need official place data inside an application, the Google Places API is usually the better route.