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GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
This blog explores various techniques to scrape Google Reviews data efficiently. Implementing these methods allows you to seamlessly export Google reviews to formats like Excel, making the information more accessible and convenient for analysis and other purposes.
There are three different approaches for scraping Google Reviews:
Each method provides flexibility and ease of use, catering to users with varying technical expertise and requirements.
In this section, we will demonstrate how to scrape Google Reviews using either Python or JavaScript by leveraging the browser automation framework called Playwright. This approach offers the advantage of bypassing common scraping blocks that are often implemented to prevent automated data extraction. However, it is important to have a good understanding of the Playwright API to effectively implement this method.
Alternatively, you can build a Google Maps scraper using Python libraries such as Requests, LXML, or Beautiful Soup. However, bypassing anti-scraping mechanisms in this manner can be challenging and is beyond the scope of this article.
Here are the steps to scrape Google Maps data using Playwright:
Step 1: Select your preferred programming language from either Python or JavaScript.
Step 2: Install the Playwright framework for your chosen language.
JavaScript
npm install playwright@latest
Step 3: Utilize the Playwright API to write code that emulates browser behavior and extracts the desired data from Google Maps. Below is an example of how you can achieve this:
Learn how to utilize the Playwright library in Python and JavaScript to scrape reviews of the Burj Khalifa from Google with confidence. Two crucial functions must be utilized for this task:
1. The "run" function: This function uses a Playwright instance to scrape the reviews. It opens the Chromium browser, directs to Google, enters your search query, then clicks on search key, and awaits the results to display on the page. It then calls the "extract_details" function to extract the review details and saves the data in a "google_reviews.json" file.
2. The "extract_data" function: This function works with a Playwright page as input as well as returns a listing of dictionaries having restaurant details like title, rating, address, review count, and phone.
To execute the "run" function, utilize a "async_playwright" context manager of the primary functions. A JSON file having the listing of a Google Maps script would be created. Follow the guidelines and use the information wisely. With this knowledge, you can confidently scrape reviews and gather valuable insights.
Step 4: Execute the code and gather the extracted data using Google Maps.
(Note: The xpaths provided in this tutorial might vary depending on the region from which Google Maps is accessed. Different regions can have dynamically rendered xpaths.)
Once you have written the code using Playwright in Python or JavaScript, you can run the script to initiate the scraping process. The code will automate the browser to navigate to Google Maps, perform a search, and load the reviews.
To collect the scraped data, the extract_data function, which you defined earlier, will extract the review details, such as the title, rating, address, review count, and phone. For further analysis, you can then store this data in a structured format, such as a JSON file.
Please handle any potential errors and implement appropriate waiting times to avoid overloading Google's servers and comply with their terms of service.
The Google Reviews Scraper by Actowiz Solutions offers a user-friendly and convenient method for scraping reviews from Google without the need for coding skills. Here's a step-by-step guide to set up and use the scraper:
1. Sign up or log in to your Actowiz Solutions account.
2. Navigate to the Google Reviews scraper by Actowiz Solutions in the marketplace.
3. Add the scraper to your account. Make sure to verify your email if you haven't already done so.
4. To begin scraping, you'll need to add the Google Reviews URL for a specific business or place. If it's a single query, simply enter it in the designated field and choose the number of pages you want to scrape.
5. You can obtain the Google Reviews URL from either the Google Maps search results page or the regular Google search page.
6. For scraping results for multiple queries, switch to Advanced Mode. In the Input tab, add the Google Reviews URLs to the SearchQuery field and save the settings.
7. To initiate the scraping process, click on the "Gather Data" button.
8. The data scraper will begin collecting data for queries, and you could track its development under Jobs tab.
9. Once the scraping process is complete, you can see or download the data from the same tab.
10. If you wish to export the Google Reviews data to an Excel spreadsheet, click on the "Download Data" option, select "Excel," and open a downloaded file through Microsoft Excel.
The Google Reviews Scraper API by Actowiz Solutions offers a straightforward method for extracting user reviews from Google without the need for extensive technical knowledge. Here's a step-by-step guide to configure and utilize the API:
2. Visit the Google Reviews scraper API by Actowiz Solutions in the marketplace.
3. Click on the "Subscribe" button.
4. Please note that this is a paid API, so you'll need to choose and subscribe to one of the available plans to access and use the API.
5. Once you have subscribed to a plan, proceed to the "Documentation" tab, where you will find all the necessary steps and information to integrate the API into your application.
The Actowiz Solutions Google Reviews Scraper API simplifies the process of obtaining valuable review data from Google, making it accessible to users with limited technical expertise. By following these steps and integrating the API into your application, you can easily retrieve and leverage Google Reviews data for your business needs.
Here are some valuable use cases where scraping Google reviews can provide helpful data:
Business Reputation Management: Google reviews offer crucial insights into customers' perceptions of products and services. By analyzing reviews, businesses can manage their reputation effectively, identify areas for improvement, and gauge customer satisfaction levels.
Competitor Analysis: Reviewing data from competitors can give businesses a comprehensive understanding of the competitive landscape. This information helps inform strategic decisions and allows companies to stay ahead in their industry.
Product Development: Leveraging review data for products or services allows businesses to identify strengths and weaknesses. This knowledge helps refine and tailor offerings to meet customer demands more effectively.
Marketing Strategies: Using review data empowers organizations to craft targeted marketing strategies. Businesses can optimize their messaging by understanding customer sentiments and preferences, leading to more impactful marketing campaigns.
Customer Insights: Review data provides valuable insights into customer experiences and preferences. Organizations can use this information to understand how well their products or services meet customer needs and expectations.
In summary, scraping Google reviews yields valuable data that can drive informed decisions across various aspects of a business, including reputation management, competitor analysis, product development, marketing, and customer insights. It allows businesses to gain a competitive edge by effectively understanding and catering to their customers' needs. For more details, contact Actowiz Solutions now! You can also reach us for all your mobile app scraping, instant data scraper and web scraping service requirements.
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⭐ "Rated 4.9/5 on Google & G2"
🔒 "Your data is secure with us. NDA available."
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Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.
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Move Forward Predict demand, price shifts, and future opportunities across geographies.
Industry:
Coffee / Beverage / D2C
Result
2x Faster
Smarter product targeting
“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”
Operations Manager, Beanly Coffee
✓ Competitive insights from multiple platforms
Real Estate
Real-time RERA insights for 20+ states
“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”
Data Analyst, Aditya Birla Group
✓ Boosted data acquisition speed by 3×
Organic Grocery / FMCG
Improved
competitive benchmarking
“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”
Product Manager, 24Mantra Organic
✓ Real-time SKU-level tracking
Quick Commerce
Inventory Decisions
“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”
Aarav Shah, Senior Data Analyst, Mensa Brands
✓ 28% product availability accuracy
✓ Reduced OOS by 34% in 3 weeks
3x Faster
improvement in operational efficiency
“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”
Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
Faster
Trend Detection
“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”
Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
Enhanced
stock tracking across SKUs
“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”
Growth Analyst, TheBakersDozen.in
✓ Improved rank visibility of top products
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.
✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place
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Discover the top 10 most ordered grocery items during Navratri 2025. Explore popular festive essentials for fasting, cooking, and celebrations.
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This research report explores real-time price monitoring of Amazon and Walmart using web scraping techniques to analyze trends, pricing strategies, and market dynamics.
Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.
Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.
Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations