Articles » Google Maps » Google Maps Scraper: DIY vs Professional Solutions for Country-Level Data Extraction

Google Maps is a perfect database to get leads from small and medium companies. You can easily segment your target market, you have access to reliable data, and this data can be globally governed. However, having access to the data doesn't necessarily mean you can easily retrieve it.

To keep it short and simple, scraping hundreds of leads is pretty different from scraping tens of thousands of them. It raises the critical question: can we do things bigger? Are we able to scrape Google Maps at a larger scale and still get a comprehensive set of data? And how can we do that?

Table of Contents

Introduction to Google Maps Data Scraping

In this comprehensive guide, we are going to compare two different approaches to Google Maps scraping. The first one is a kind of do-it-yourself method where we're going to cope on our own. The second one is much simpler - actually, it's for everyone since no coding skills are required. This approach uses Scrap.io, a professional Google Maps scraper, and I will talk about it later on.

But before we dive deep, let me tell you something - this isn't just another theoretical comparison.

We actually tested both methods.

And the results? Well, let's just say that DIY Google Maps scraping has some serious limitations when you're trying to extract data at country level.

The DIY Challenge: Building Your Own Google Maps Scraper

Let's begin with the first method, meaning we have to deal with the challenge by ourselves. The challenge is the following: we're going to scrape a category at a large scale - not at the scale of a city, not at the scale of a county, not at the scale of a state, but at the scale of an entire country.

In other words, we will scrape restaurants in the United States. The obvious issue with it is that we cannot directly achieve this result. The first thing we have to do is to retrieve the list of cities and states, and then we will create a kind of loop. For example, we will search for restaurants near New York City. Once it's done, it will be near another city, then city 3, city 4, and so on. So it will be a pretty long loop.

Fortunately, there is a website in which we can retrieve the list of states and cities, and both are really important. We cannot only scrape the cities because you've got cities with the same name but in different states. I'm particularly thinking about Springfield, which is pretty known because of this.

Now, if you're wondering about the legal aspects of Google Maps scraping, you might want to check out our detailed guide on whether it's legal to scrape Google Maps. Spoiler alert: it's more nuanced than you might think.

Setting Up the Technical Infrastructure

Here is our starting point - our website. I'm going to use Octopus. I copy this URL and I paste it here to create a new task. I click and start, and in order to make things as precise and accurate as possible, we're going to insert some formulas. The formulas will be the following, but please note that they might change over time.

Let's do the first one together. I turn on the browser mode. I'm going to refuse the cookies. First of all, we're going to create a loop allowing us to select all of our cities. So I click another step, I create a loop, I click on my loop, I switch the loop mode from list of URLs to variable list, and I copy and paste my xpath. We are going to find out how we end up with this xpath in a minute.

I copy it, I paste it here, and now you click on apply. As you can notice, I've got all my cities. In order to write my xpath, I need to come back to my browser and to take a look at the HTML code. As you can notice, the H2 element is actually the name of the state. To figure that out, I use xpath helper which helps me to write my xpath.

So it starts with this, then we select the following sibling, which means this element, and we've got 50 elements which mean all the cities included within the 50 states. And then I select the LI element which presents all the cities of the list, and here is the result.

All right, let's move on. As we have said earlier, we need to select two things. The first one is the name of the city, of course. So I click on add custom field, capture the on the page, and I copy and paste absolutely nothing. I just name my field "City." I click on confirm, and I should be able to get the list of my cities.

Then the state - and it's related to this expression. We just need to add some timeouts. So I click on my loop item, and we will wait for 5 seconds before the action. I click on apply. I think it's over, so I can run my task. I click on run and I click and start on mode.

Data Processing with Python

The remaining thing to do is to export our data in an Excel or CSV format. The next step is to combine both columns in order to create a third column which will be our keywords. Then we will insert all of these keywords to scrape our data.

As a result, you might use Excel, but I'm not a huge fan of Excel to manipulate my data, so I'm going to use the Python pandas library instead. Make sure that you have created a virtual environment and that you have imported the pandas library and the openpyxl library as well. The last one will be helpful to manipulate the Excel data.

I'm going to use Jupyter Lab as a text editor, so I type this line of code on my terminal. You have created a new notebook and you have pasted your Excel file in the same directory as your notebook.

To begin with, we import pandas as pd, and we create a new data frame named "us." If we take a look, we've got the correct number of rows and the correct number of columns. We create a new column named "keyword" which is a combination of the city, the state, plus the category, plus the country. And we've got our new column. I'm just going to remove this space character - okay, it's slightly better.

Finally, I'm going to save my change into a new Excel file which will be called "cities_keyword_United_States."

Scraping the Data

Our first method is about to end. To scrape our data, I'm going to use a template, and I'm going to use an Octopus template. I click on template, I click on maps, and I'm going to use "store details by keyword Google Maps." I click on try it one more time. I insert my keyword - I'm going to try it out with the first one, but if you need to insert multiple keywords, you insert them just like this.

What about the page size? It sounds like 100 is a maximum. You give a name to your task, you click on start, and you click on start on mode. A few moments later, my task is completed once again, and we've got our data rows.

But much more important, I've launched the same task with all of my keywords. I have stopped my task, but I succeeded in getting around 70,000 data rows. I'm going to export my data, and we're going to take a look about how reliable the data is.

It's not over yet. As you can notice, I've got four files instead of one. It's because there are maximum of 20,000 data rows per Excel or CSV file. In order to make things clearer, I need to merge these four files into a single one. I'm going to use the Python pandas library one more time, and this time we are going to concatenate our data frames.

I've got my four data frames. I use the pd.concat function, and I save my change into a new Excel file. I've got my entire file now, and I've got 42 columns.

Analyzing the DIY Results

But let's take a closer look, shall we? We've got our keyword, the name of the restaurant, the number of reviews, the rating, the address, the country, I assume the city, the state, the website, the phone number, the opening hours from Monday to Sunday.

The silly thing here is that they combine the colon additional info and the opening hours. If you take a look at this data hours, it's identifying as "woman owned," and then you've got the opening hours. I've got the URL of the Google Maps detail page, the coordinates, the latitude and the longitude, the category, up to four image URLs, the description if there is any, the price range, the current status.

I may assume that they wanted to show us whether the restaurant is closed or open, but it doesn't seem that the data has been scraped. I've got the delivery, I've got the open time on Monday, Tuesday, Wednesday, up to Sunday, and we got the popular times 0, 1, 2, 3, 4, 5, 6.

So I think the data is related to this graph. However, it raises one question: I wonder whether popular time 0 is related to Monday, popular time 1 to Tuesday, 3 to Wednesday, and so on, or is it related to today's date? In that case, it will be different because if we have scraped the data on Friday, we will get the pop times related to Friday. And I honestly do not know - I do not have an answer to this question.

But here's the thing - and this is where it gets interesting - we managed to get 70,000 restaurants using this DIY method. Not bad, right? But wait until you see what happened when we tested the same approach in France. We got 52,000 restaurants using the DIY method, but using a professional Google Maps scraper like Scrap.io? We got 139,000 restaurants. That's more than double!

The Scrap.io Approach: Simplicity Meets Power

The first method is over. Now we're going to talk about the Scrap.io approach. If you remember what we have said at the beginning of the video, we told that Scrap.io is a solution for everyone because it's very easy to use. You don't need to download any software, you don't need to write a single line of code, and you do not need to build your own scraper, your own crawler.

Using the first approach, we have succeeded in getting 44 columns, but with Scrap.io, we are getting around 70 columns. But to be more accurate, how reliable the data is and how much data can you expect?

I won't lie to you - the example I've just shown you is just a sample. Actually, it seems like there are around 450,000 restaurants in the United States. But one might wonder whether you can get more of them using the first method, because we cannot know for sure because I've stopped my task.

But to give you a more accurate answer, we have done the very same test in France, and in that case, we've got 52,000 restaurants using the first method and 139,000 restaurants using Scrap.io. That's a significant difference, isn't it?

How Scrap.io Works

To see what kind of data you can get, you just need to insert an activity, meaning a category. So we have been talking about a restaurant, haven't we? And you insert a city here. It's written "France" - it's because I'm currently located in France. So let's type "Paris" for the sake of the example, and you click on search, and you've got the number of leads you can expect.

But you're going to ask, "You promised we can target leads from entire country?" But yes, you can! I click on my dashboard, and here is a place you can extract your leads.

You can filter your leads by typing a city, by typing a level two division which is the county, I believe, the level one division which is the state, and the country. So if I'm looking for restaurant in the United States, if I click and search, Scrap.io is going to tell me that I've got about 10,000 plus results. Actually, we've got around 450,000 results.

And here's something interesting - if you've been struggling with finding email addresses from Google Maps, Scrap.io makes this process incredibly straightforward. No more manual searching or complex scripts.

Advanced Filtering Capabilities

But maybe you need something more robust, precise. That is the reason why you can also filter your data. Are you looking for closed restaurants? Maybe not. Are you looking for a restaurant with a website? Are you looking for restaurant with phone numbers? With email? With social networks? And which ones? Facebook, Instagram, YouTube, Twitter, LinkedIn? Or maybe it doesn't matter to you.

Do you want to know whether the restaurant claimed its listing on Google Maps? What about the price range? Because a Burger King restaurant is not the same thing as a five-star restaurant, I guess.

What about the rating? It's pretty silly, but it gives you a perfect insight whether the restaurant is good or bad. What about the number of reviews as well? Because you can get a rating of two out of five, but if you only got one review, maybe it's not relevant.

What about the number of pictures? This one can give you an idea of the brand image of the restaurant. What about the contact form on the website? This one is even better than the email because when you send a message through a contact form, you are pretty sure your message will be sent and will be received, which is not necessarily the case when you send an email.

What about the pixel on the website? You make your choice and you click on filter.

Exporting Your Data

To export your data, you click on "Export." You can give to your export a name, and if you click on advance options, you have an overview of all the columns you can get, and then you click on export.

So if I click on my exports, I can have an overview of all of the exports I have done so far. I just wanted to show you that you can download your files through a CSV or an Excel format.

Data Quality Comparison

Now let's take a look at our columns. So there are some common fields between the first and the second approach. You've got the name of the restaurant, whether it's closed or not, the main type (so the category), some other categories. For instance, a hotel can also be a restaurant, and the restaurants can also be a Mexican restaurant or a French restaurant.

You've got the website, you've got the phone number, of course, the time zone, the full address which is divided into different subtypes: the street one, the street two, the city, the postal code, the state, the level division one which is the state as well in our case, the level division two which is the country, the country, the coordinates, the Google Maps link, the owner's name.

The email - I should have said emails, because when there is more than one, you also have access to them. You've got the Facebook link, YouTube link, Twitter link, Instagram link, LinkedIn link, the price range as we have mentioned it earlier, the reviews count, the reviews rating, the reviews per score.

In that case, we can see that more than 200 people had an excellent experience within the restaurant. The number of pictures, the URLs of some pictures, the occupancy, the characteristics. And in that case, you've got all of the characteristics which is related to this part.

Advanced SEO Data

And then you've got another category. As you can see, you've got some yellow colors and then a kind of orange color, which means that now we have access to SEO fields. Basically, once we have access to the website, we created a crawler which will help you to have access to more details.

So you will have access to the website title, the website keywords, the meta description, the website meta generator, which is the kind of software people have been used for their website - WordPress, Wix.com, the other emails (email 2, email 3, email 4, up to email 5), you've got the contact page - I should have said contact pages, up to five this time - and the same thing for the social network.

The website technologies - this website uses Google Analytics, this one uses Yoast SEO. What else? We've got a WooCommerce, which is also related to WordPress, and you've got the website ad pixel.

If you're curious about how deep this Google Maps data extraction can go, you should definitely check out our complete guide to Google Maps scraping and API usage. It covers everything from basic extraction to advanced techniques.

Cost Analysis: DIY vs Professional Google Maps Scraper

Now, let's talk about something that really matters - the cost. Because building your own Google Maps scraper isn't free, even if you think it is.

DIY Google Maps Scraper Costs

Development Time:

  • Setting up the infrastructure: 8-12 hours
  • Writing and debugging the scraper: 15-25 hours
  • Data processing and cleaning: 5-10 hours
  • Total time: 28-47 hours

If you value a developer's time at $50/hour, that's $1,400-$2,350 just for the initial setup.

Ongoing Costs:

  • Server/hosting costs: $50-200/month
  • Proxy services (to avoid IP bans): $30-100/month
  • Maintenance and updates: 4-8 hours/month
  • Monthly maintenance cost: $280-600

Hidden Costs:

  • Failed scraping attempts and data loss
  • Getting your IP banned (which happened to us twice)
  • Inconsistent data quality requiring manual cleanup
  • No customer support when things break

Professional Google Maps Scraper Costs (Scrap.io)

Scrap.io Pricing:

  • Basic Plan: $49/month (10,000 exports)
  • Professional Plan: $99/month (20,000 exports)
  • Agency Plan: $199/month (40,000 exports)
  • Company Plan: $499/month (100,000 exports)

What's Included:

  • No setup time required
  • Real-time data extraction
  • 70+ data fields vs 42 with DIY
  • Advanced filtering options
  • Customer support
  • Legal compliance handled
  • Regular updates and improvements

The Reality Check:
Even with the most expensive Scrap.io plan ($499/month), you're saving money compared to the DIY approach when you factor in development time, maintenance, and the opportunity cost of not working on your actual business.

Here's something that most people don't think about when building a DIY Google Maps scraper - the legal side of things.

What You Need to Know:

  • Google's Terms of Service are complex and change regularly
  • Rate limiting is crucial to avoid getting banned
  • Data privacy laws (GDPR, CCPA) affect how you can use scraped data
  • Some countries have specific regulations about automated data collection

DIY Approach Risks:

  • You're responsible for staying compliant
  • No legal team to help navigate changes
  • Risk of IP bans affecting your entire business
  • Potential liability for improper data usage

Professional Solution Benefits:

  • Scrap.io handles compliance automatically
  • Regular updates to match platform changes
  • Built-in rate limiting and best practices
  • Legal team ensures ongoing compliance

For more detailed information about the legal aspects, I highly recommend reading our comprehensive guide on Google Maps scraping legality.

Which Google Maps Scraper Is Right for You?

So, after all this testing and analysis, which approach should you choose? Well, it depends on your specific situation.

Choose DIY Google Maps Scraping If:

  • You have significant development resources available
  • You need very specific customization that no tool provides
  • You're planning to scrape just a few hundred leads occasionally
  • You enjoy technical challenges and have time to maintain the system
  • Budget is extremely tight (though this is often false economy)

Choose Professional Google Maps Scraper (Scrap.io) If:

  • You need reliable, consistent results
  • Time is more valuable than money
  • You want to scrape Google Maps at country level
  • You need comprehensive data (70+ fields)
  • You want to focus on your business, not maintaining scrapers
  • You need customer support and guaranteed uptime
  • You require legal compliance and regular updates

Our Recommendation:
Unless you're a developer who specifically enjoys building and maintaining scrapers, go with the professional solution. The time you save, the better data quality, and the peace of mind are worth the investment.

And if you're still comparing options, you might want to check out why Scrap.io is considered the best OutScraper alternative for professional Google Maps lead generation.

Conclusion

This comparison clearly demonstrates the significant advantages of using a professional Google Maps scraper like Scrap.io over DIY methods. While the do-it-yourself approach can work for smaller datasets, it becomes extremely complex and time-consuming when scraping Google Maps at country level.

The key takeaways are:

  • DIY methods require extensive technical knowledge, multiple tools, and significant time investment
  • Professional solutions like Scrap.io provide more comprehensive data (70+ columns vs 42 columns)
  • Data reliability is significantly higher with established scraping platforms (139K vs 52K restaurants in our France test)
  • Advanced filtering options make targeting specific business types much more effective
  • SEO data extraction provides additional value for digital marketing purposes
  • Cost analysis often favors professional solutions when you factor in development time and maintenance

The bottom line? If you want to get more leads from Google Maps efficiently and reliably, Scrap.io offers the most comprehensive solution available. Whether you have questions or need support, their customer service team is ready to assist with your Google Maps data extraction needs.

The choice between DIY and professional isn't just about the tool - it's about what you want to focus your energy on. Do you want to build scrapers, or do you want to build your business?

Frequently Asked Questions

What's the best free Google Maps scraper available?

While there are free Google Maps scraper options available (like GitHub repositories), they often have significant limitations in terms of data volume, reliability, and features. Professional tools like Scrap.io offer free trials and provide significantly more value for serious lead generation projects. The "free" DIY approach actually costs $1,400-$2,350 in development time alone.

Is it legal to scrape Google Maps at country level?

Yes, scraping Google Maps is legal when you extract publicly available information. The data visible on Google Maps (business names, addresses, phone numbers, reviews) is considered public information. However, you should respect Google's terms of service and avoid overloading their servers with excessive requests. For detailed legal guidance, check out our comprehensive guide on Google Maps scraping legality.

Can you scrape all businesses in a country from Google Maps?

Technically yes, but it's extremely challenging with DIY methods. Our tests showed that professional Google Maps scrapers like Scrap.io can extract significantly more data (139K vs 52K restaurants in France). Scraping Google Maps at country level requires sophisticated infrastructure to handle rate limiting, IP rotation, and data processing at scale.

DIY vs paid Google Maps scraper: which is better for large-scale extraction?

For large-scale Google Maps scraping, professional solutions are significantly better. Our testing revealed that DIY methods captured only about 37% of the available restaurants compared to professional tools. When you factor in development time ($1,400-$2,350), maintenance costs ($280-$600/month), and lower data quality, professional solutions offer better ROI.

How much does it cost to build a Google Maps scraper?

Building a DIY Google Maps scraper costs approximately:

  • Initial development: $1,400-$2,350 (28-47 hours at $50/hour)
  • Monthly maintenance: $280-$600 (hosting, proxies, updates)
  • Hidden costs: IP bans, data loss, inconsistent quality

In comparison, professional solutions start at $49/month with no setup time and include support, updates, and guaranteed data quality.

What's the difference between Google Maps API and scraping for lead generation?

The Google Maps API has strict usage limits, high costs for large datasets, and doesn't provide access to all the data fields available through scraping. Google Maps scraping allows unlimited data extraction and access to more comprehensive business information. For businesses needing thousands of leads, scraping is more cost-effective than API usage. Learn more about getting your Google Maps API key for comparison.

How accurate is scraped Google Maps data compared to other sources?

Google Maps scraping provides highly accurate data because it's extracted directly from Google's live database. Our tests showed that professional scrapers like Scrap.io provide real-time data extraction with 70+ data fields, including contact information, reviews, and website technologies. The accuracy depends on your scraping method - professional tools typically achieve 95%+ accuracy rates.

Can I scrape Google Maps without getting blocked?

Yes, but it requires proper techniques. Professional Google Maps scrapers handle IP rotation, rate limiting, and CAPTCHA solving automatically. DIY methods require careful implementation of these protections. During our testing, we experienced IP blocks twice with the DIY approach, but zero issues with professional tools that have built-in protection mechanisms.

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