Google Shopping is an invaluable resource for consumers and businesses alike. With Google Shopping, shoppers can easily search for and compare prices across thousands of online stores. For businesses, getting your products listed on Google Shopping gives you exposure to millions of potential new customers.
However, manually collecting and analyzing Google Shopping data is tedious and time-consuming. This is where web scraping comes in. Web scraping allows you to automate the process of extracting data from websites like Google Shopping.
In this comprehensive guide, we‘ll cover everything you need to know to successfully scrape Google Shopping using Python and APIs.
Why Scrape Google Shopping Data?
Here are some of the key reasons businesses scrape Google Shopping data:
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Competitive pricing research – Analyze competitors‘ product listings and pricing strategies. This helps you optimize your own prices.
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Market research – Identify top selling products, analyze consumer demand and search trends.
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Inventory monitoring – Track competitors‘ stock levels and availability.
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Lead generation – Scrape product listings to find business contact information.
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Supplier research – Find new suppliers selling your type of products.
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Price monitoring – Track prices over time to identify sales and promotions.
Having this data enables data-driven business decisions to maximize sales and profits.
Is Web Scraping Google Shopping Legal?
Web scraping public data from Google Shopping is perfectly legal in most countries, provided you follow some basic rules:
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Only scrape data accessible without logging in (public data).
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Don‘t overload servers with an unreasonable number of requests. Follow Google‘s guidelines.
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Don‘t copy substantial portions of content verbatim. Paraphrase text instead.
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Respect robots.txt restrictions.
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Use scraped data only for your own analysis, not for republishing or selling data.
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Be upfront about being a robot, don‘t try to disguise your scraping activities.
As long as you scrape responsibly like this, you should stay out of legal trouble. That said, it‘s always wise to consult a lawyer if you have any concerns regarding the legality of your web scraping project.
Challenges of Scraping Google Shopping
While scraping Google Shopping is legal and valuable, it‘s not without some technical challenges:
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Bot detection – Google is very good at detecting and blocking scrapers. You‘ll need robust proxies or residential IP addresses to avoid getting blocked.
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JavaScript rendering – Google Shopping pages rely heavily on JavaScript. Your scraper must be able to execute JS to load all the data.
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Captchas – Getting hit by captchas can interrupt and slow down your scraper. You‘ll need OCR capabilities or a captcha solving service to handle them.
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Data structure – Google Shopping uses a complex, nested data structure. Your scraper code needs to be robust to parse the data correctly.
These challenges make it highly advisable to use a specialized web scraping API designed for scraping Google services. Doing it yourself from scratch is very difficult.
Google Shopping Page Types
To scrape Google Shopping effectively, you need to understand the different types of pages and data available:
Search Results Page
This page displays the list of products matching a search query, along with summary info like title, price, ratings, etc.
Key data fields on a Google Shopping search results page:
- Product title
- Product image
- Price
- Ratings & reviews count
- Seller name
- Delivery options
Product Details Page
When you click on a particular product listing, you go to the product details page. This page provides in-depth information about that product.
Key data fields on a Google Shopping product details page:
- Full product title
- Product images
- Price ranges
- Seller ratings
- Complete product description
- Product highlights / key features
- Product specifications & technical details
- Product reviews
- Seller information
- Shipping options
Seller Pricing Page
Clicking the "See price history" link on a product page takes you to the pricing page, which displays a comparison of prices offered for that product by all sellers.
Key data fields on the Google Shopping pricing page:
- Product title
- Seller names
- Seller ratings
- Price offered by each seller
- Shipping charges
- Availability status
Understanding the structure of these different page types is crucial for writing effective scraper code. Next we‘ll look at how to actually scrape Google Shopping using Python.
Scraping Google Shopping in Python
For robustly scraping Google Shopping at scale, I recommend using the Oxylabs Google Shopping API. It handles all the challenges like proxies, captchas, and Javascript rendering for you.
However, it‘s also possible to build your own basic scraper using the Python Requests library along with proxies. Here‘s an example Python code snippet to scrape a Google Shopping search results page:
import requests
from bs4 import BeautifulSoup
proxies = {
‘http‘: ‘http://192.168.1.1:8080‘,
‘https‘: ‘http://192.168.1.1:8080‘
}
url = ‘https://shopping.google.com/search?q=laptops‘
headers = {
‘User-Agent‘: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582‘
}
response = requests.get(url, proxies=proxies, headers=headers)
soup = BeautifulSoup(response.text, ‘html.parser‘)
# Extract product data from search results
products = soup.select(‘.sh-dgr__grid-result‘)
for product in products:
title = product.select_one(‘.Xjkr3b‘).text
price = product.select_one(‘.a8Pemb‘).text
print(title, price)This implements a basic scraper to extract the product title and price from Google Shopping results for the search query "laptops".
To make this a production-ready scraper, you would need to:
- Add logic to iterate through multiple pages of results
- Increase the number of product fields extracted
- Add caching, error handling, retrying of failed requests
- Expand search queries beyond just "laptops"
- Store scraped data into a database
- Use multiple rotating proxies and residential IPs to avoid blocks
- Solve captchas manually or with a service like Anti-Captcha
As you can see, building a robust scraper from scratch requires a lot of additional work.
Scraping with the Google Shopping API
The easiest way to scrape Google Shopping is using a purpose-built API like the Oxylabs Google Shopping API.
The Oxylabs API handles all the challenging parts of Google Shopping scraping for you:
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Proxies – Millions of residential proxies to provide high-quality IP addresses that avoid blocks.
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Javascript rendering – Returns fully loaded HTML and structured JSON data.
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Captchas – Automatically solves captchas without any effort on your end.
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Data parsing – Structures data for easy analysis instead of raw HTML.
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Scalability – Designed to handle large scraping volumes without failures.
This allows you to focus on writing the code for your specific business use case, rather than dealing with the scraping infrastructure.
Here is an example of using the Oxylabs Google Shopping API in Python:
import requests
api_key = "YOUR_API_KEY"
# Search results scraping
params = {
"source": "google_shopping_search",
"domain": "com",
"query": "led tvs",
"parse": "true"
}
response = requests.post(
"https://api.oxylabs.io/v1/queries",
json=params,
auth=(api_key, "")
)
data = response.json()["results"][0]["content"]
for product in data["results"]["organic"]:
print(product["title"], product["price_str"])
# Product details scraping
params = {
"source": "google_shopping_product",
"domain": "com",
"query": "7084898117438614660", # Product ID
"parse": "true"
}
response = requests.post(
"https://api.oxylabs.io/v1/queries",
json=params,
auth=(api_key, "")
)
product = response.json()["results"][0]["content"]
print(product["title"])
print(product["description"])
print(product["reviews"]["rating"])This makes it easy to scrape both the search results listing and detailed product page in just a few lines of code.
The Oxylabs API also provides great documentation and customer support, making it seamless to integrate Google Shopping scraping into your business applications.
What to Do with Scraped Data
Once you‘ve successfully scraped Google Shopping data, what should you actually do with it? Here are some ideas:
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Price monitoring – Load pricing data into a database and create dynamic dashboards to view price history and trends over time. Set up alerts for key thresholds.
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Competitive analysis – Compile scraped data into a comparison table of product titles, descriptions, prices, reviews and other metrics for you vs competitors.
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Market research – Analyze search trends and product demand based on search volume and review data. Identify gaps for potential new products.
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Lead generation – Enrich your marketing and sales databases by extracting seller contact information from listings.
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Inventory tracking – Build a system to monitor competitor inventory levels and notify you of shortages.
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Demand forecasting – Use historical Google Shopping data along with other signals to predict future product demand.
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Real-time alerts – Get notified immediately via email or SMS when key products go on sale or have other attribute changes.
The possibilities are endless! Properly utilizing the scraped data will enable smart, data-driven business strategies.
Conclusion
Scraping product and pricing data from Google Shopping can give you a competitive edge, but requires overcoming some important challenges around bot detection, JavaScript rendering, captchas, and more.
The most effective approach is to use a robust web scraping API like Oxylabs that handles these complexities for you and makes the data easy to collect. With scraped Google Shopping data powering your business analytics, you can unlock significant new revenue opportunities and data-driven decision making capabilities.
