How to scrape Google Maps Reviews at scale?

Sasha Bouloudnine
July 29, 2022
5 min read


We’ll extract 100 Google Maps reviews of a fancy French Water Park. In 1 minute. With no code. For free!

As easy as it goes:



Reviews on Google Maps are of exceptional value.

Widely used, they do provide strong insights on a business core metrics: quality of service, rough estimation of affluence, explicit verbal feedback on some specific attributes of an establishment.

Consumers trust online reviews 76% more than they do personal suggestions, giving Google Maps data significant influence over purchasing decisions, according to this excellent article from Backlinko.

At an industry-specific level, 94% of American consumers have admitted being affected by internet evaluations of a restaurant they're intending to visit, according to this strong presentation from BinaryFountain.

Local listings have thus become vital for business development and market exposure.

In this tutorial, we’ll see how to extract 100 reviews from a fancy Water Park in Paris — in 1 minute. With no code. For free!

As far as we know, all reviews do deserve a decent collection. All of them.


Reviews are basically a large set of words, usually written by meticulous (or angry) customers. Of course, reviews are explicitly interesting because of the rating score: a clear quantitative metric, which gives the overall value of a place. At a glance.

Though, reviews also convey fascinating qualitative datapoints — which give a true depth to the dataset, and will let you perform accurate place audits. For instance, text, date of publication, language, date of visit, photos, likes, or response of the owner.

How can this rich data be positively used? Here are a couple of hopefully relevant examples:

  1. identify overall positive/negative sentiment
  2. target bad reviews — and identify business pain points/room for service improvement
  3. qualify a list of businesses — and target businesses that could need your help
  4. wipe out fake reviews
  5. assess affluence over time
  6. evaluate your customer base preferences

Of course, it's totally legal to collect any data publicly available on the Internet, included reviews on Google Maps.

More specifically, publicly available information on the Internet is to be considered as publicly accessible data. It means there is not any (intellectual) property assigned to these pieces of information, and it can be gathered — at scale. Automated web scraping is just an (efficient) way to do this. It’s more accurate, highly cost-effective, and it frees your folks from repetitive and boring tasks.

To learn more about this critical topic, we wrote a dedicated blog post, available here:

What about official API?

Google indeed provides a dedicated API, which is accessible here. It’s of course possible to use this service — through, it’s terribly limited, and for 3 main reasons.

First, since 2011, the results count provided is massively limited. For each place concerned, you can collect only up to 5 distinct reviews. No more.

This is what we can read from the official API documentation:


On top, the data points provided are few. Official API is indeed providing text, rating, date of publication, or language. Though, the service is missing a huge set of additional — and decisive — attributes: pictures, likes, total guide status, number of reviews of the user, response of the owner, date of visit. Many elements which let you qualify a review with high accuracy. And are miserably missing from the official provider API.

Endly, using the Google Place API is hard. You’ll need to create an account, pick a dedicated API key, and grab data directly from a piece of code. In other words, it’s only accessible for (motivated) developer. Business owner, marketing manager, sales representative: forget it!

With the dedicated Google Maps Reviews Scraper here, you’ll be able to collect:

  1. up to 2000 reviews per establishment
  2. 18 data points — included pictures, likes and response of the owner
  3. all from cool interface

No code. No pain. Massive gain.

Step-by-Step Guide


First, let’s pick the Google Maps URL of our establishment. Let’s go to Google Maps, type the name of the local business we’re looking for, and copy-paste it:

And here we go! Let’s keep this precious URL, we’ll need it later.,2.274529,17z/data=!3m1!5s0x47e6701cc188b61d:0x133b12c2f8ac9cbc!4m9!1m2!2m1!1saquaboulevard+paris!3m5!1s0x47e6718b1fbcd4fd:0xa7968d889eb5402a!8m2!3d48.8318042!4d2.2762627!15sChNhcXVhYm91bGV2YXJkIHBhcmlzWhUiE2FxdWFib3VsZXZhcmQgcGFyaXOSAQp3YXRlcl9wYXJr


Now let’s connect to our lobstr account, and click on ‘Create a new Cluster’, just in the center of the screen:

With a Free Plan, you’ll benefit 15 minutes per day, which means approx. 1500 reviews per day. Free. Forever!

Then, let’s type ‘google’ — and pick the second Crawler — dedicated to Google Maps Reviews data collection:

We’re now at setup step. A couple of clicks, and we’ll trigger a free data collection at scale! Now, in the ‘Url’ field — let’s replace the already added URL, with the URL we just did copy above.

And let’s simply click on ‘Save’:


Finally, since we only want to collect once, let’s simply click on ‘Save & Extract’

And click on ‘Yes’:

Our run is now launched!

Time to relax — and let professionals at work.



< literally 58 seconds later >

Collection is complete! We did collect exactly 100 reviews, in 58 seconds, which is about… 100 results per minute. And immediately accessible from the RunPage:

You can simply click on the big red ‘Download’ button:

And now opening your file with Numbers, you’ll be able to instantly enjoy an exhaustive set of 100 clean Google Maps Reviews from the URL you did target:

All perfectly scraped, and accessible through a clear and standardized format. Data you can immediately share with your working partners. And leverage at scale.


Let’s sum-up then. With lobstr, you can, in literally 58 seconds: scrape the most valuable reviews on Google Maps, export all of this into a properly formatted .csv file. And leverage it at scale.

With no code. For free.

Happy scraping!



Sasha Bouloudnine

Co-founder @ since 2019. Genuine data avid and lowercase aesthetic observer. Ensure you get the hot data you need.