How to Scrape Google Search Data

It’s hard to imagine a successful business that doesn’t gather or use any form of data in 2021. And, when it comes to sources to acquire data from, you can’t get around the wealth of data found on Google search engine result pages.

But gathering Google search results isn’t that simple – you will encounter technical challenges and hurdles along the way. Luckily, there are easy-to-use tools and methods that can automate search result extraction.

In this article, we’ll go through different methods businesses use to scrape Google search results. We’ll also discuss why it’s beneficial for companies to scrape Google and show you how to solve any possible issues you might encounter while scraping Google SERP data.

What Are Google SERPs?

Before we get into the nitty-gritty of Google web scraping, let’s find out what Google SERPs are. 

SERP is an abbreviation for Search Engine Result Page. Basically, it’s the page you get when you type in a search query and press Enter on Google (or other search engines.)

The interface of a Google SERP has changed a lot throughout the years – what used to be just a plain list of search results is now way more complex. Today, Google has a number of different SERP features (also known as Rich Snippets), such as Knowledge Graphs, People Also Ask boxes, reviews, News boxes, and others. So, when it comes to choosing a solution for scraping Google, it’s important that it acquires data from all these features.

Why Businesses Gather Data From Google: Common Use Cases

Google currently holds 86.6% of the global market share of search engines, as measured by the global statistics website Statista. In comparison, the second-largest search engine (Bing) holds a mere 6.7% market share. Google is also (by far) the most visited website in the world.

These statistics tell us that no matter the industry you operate in as a business, your customers and competitors are likely to be on Google. So, there’s definitely valuable data for you to gather. Below are some common scenarios, along with their business personas. Now that you know how businesses can benefit from Google SERP data, let’s learn how to scrape Google SERP data on a large scale.

How To Scrape Google Search Results

When discussing Google search data scraping, a question often arises – does Google offer an official solution for its data acquisition? Unfortunately, Google doesn’t provide an official API for scraping, making it difficult to acquire its data at scale. 

Of course, there’s always an option to gather data manually, but there are two issues with this method. Not only does collecting data manually consume a lot of time, but it also doesn’t ensure accuracy. Google delivers search results to each visitor based on different factors, like their location, device, and browsing history. So, if you gather search results manually, it won’t properly show the big picture.

Hence, you’ve got roughly three ways to acquire Google search data: semi-automated, automated (done yourself), and automated through 3rd party tools.

Option 1: Semi-automated data gathering

Building a scraper requires some coding knowledge and other technical steps (as we’ll see further below). However, depending on the type and amount of data you need, you might be able to use a semi-automated method instead.

A quick and easy solution is to build a (very) basic scraper in Google Sheets. For this option, you don’t need to write any code, only a Google Sheet and a few special formulas are required. This solution is helpful if you need to collect basic information from a list of web pages.

Say you want to gather some basic Google search results (like meta title, meta description, or author’s name) from pages that compete with your own page on Google for a certain keyword.

You can use a custom version of Google Sheets’ IMPORTXML function with an additional argument (called “xpath-query”) to automatically import your desired data directly from the web page’s HTML into your spreadsheet. This formula searches through the page’s HTML to retrieve the element that you want it to look for, such as <meta name=” description” content=”...”> for page’s meta description.

Technical difficulties of this method:

• Having to do some work manually. For basic queries and small-scale Google SERP data gathering, this method can be pretty useful. However, you’ll still need to do the setup manually.
• Limited amount of data. This method is great if you want to extract some basic information; however, if you need large volumes of data, we’d suggest going with the methods described below. 

Option 2: Automated data gathering by building your own scraper

The next option opens a lot of possibilities as it allows you to scrape Google SERP data in an automated way.

A web scraper is a robot that you can program to retrieve vast amounts of data automatically. This robot crawls a URL (or set of URLs) by visiting them, going through all the data on a page, and extracting the data to store it in a database.

The scraper can continue crawling through new pages by following hyperlinks, thus enabling you to gather data from thousands of web pages at once. Following this method, you wouldn’t need to manually feed your robot every page you want it to crawl (contrary to the semi-automated way.) 

Say you want to scrape Google search results for a specific query. You can create a Google result scraper that you only have to feed the Google Search query of choice, and the scraper will do the rest for you.

Building a scraper requires coding knowledge. The most common method is by using the Python programming language, with specific modules like BeautifulSoup and Requests. Most building blocks of a scraper are available through such open-source solutions and do not require any form of payment/fee on your side.

Building a custom, in-house scraper definitely has its perks: for starters, you can build it however you like! You’re fully in control of the development process, so you can ensure the scraper has features you truly need. In other words, a custom tool for scraping Google is likely to be better adjusted to your company’s goals. 

Technical difficulties of this method:

• User-Agent testing. Google tries to distinguish humans from robots by checking the User-Agent of the site visitor. To solve this problem, create a list of User-Agent strings used by real browsers and add this to a Python list. Also, use rotating proxies so the IP address changes with each request.
• Request rate limitation. Google applies limitations on the number of requests a user can place within a certain timeframe. To avoid this, set specific time frames between requests and rotating proxies to change the IP address.
• Robotic behavior. Google analyzes user behavior to predict whether the site visitor is a robot or a human. The solution would be making the bot as human-like as possible (i.e., mimic random actions, like scrolling down, then up). 
• Blacklisted IP address. ​​Once Google notices robotic behavior coming from an IP address, it will blacklist the address. To avoid this, rotate proxies and use high-quality IP addresses. The best types are unshared, residential IP addresses. 
• CAPTCHA test. If Google suspects it’s dealing with a robot, it may request proof of humanity – you’ll have to solve a CAPTCHA test. Solving CAPTCHAs can be difficult for robots, so it’s best that you try avoiding them by combining the above-mentioned methods. 

Option 3: Automated data gathering through 3rd party tools

The third option is to invest in a 3rd party tool to do the work for you. There are thousands of different tools for scraping Google available, each created to fit a specific purpose.

The most common web scraping tools are SEO tools designed for tracking the performance of pages in Google’s SERPs. These tools collect all sorts of page data, from average rankings to the number of words on a page or the number of backlinks a website receives from others. Popular examples of such tools include Ahrefs, SEMrush, and Searchmetrics.

Aside from these SEO tools, there are scrapers to gather all sorts of Google search results. You can find scrapers to gather data from Google Shopping results, Google Maps, Google Scholar, and much more.

Many SEO specialists choose SERP APIs for thorough keyword research. For instance, our Google Search API is a tool designed to extract data from different search types (Google News, Google Images, etc.) and all SERP features, like reviews, People Also Ask boxes, and others. Our Google results scraper can be used for various other purposes, like ad verification, pirated-content detection, stock market forecasting, and more.

Technical difficulties of this method:

• Gets pricey. Third-party tools are not free (like the solutions above). For some of the industry-leading SEO tools like Searchmetrics or SEMrush, you have to pay quite hefty monthly fees to use their services.
• Lack of customization. With a 3rd party scraper, you may not always get the features you want and vice versa – you may be paying for ones you don’t need.

Scraping Methods Summarized

Now that you know more about how you can gather Google search data, it’s time to decide which solution best fits your needs. Going through the pros and cons listed above, you probably already have a favorite option in mind, but let’s quickly recap your possibilities:

Wrapping Up

In the end, the best solution for acquiring data from Google depends on your business’ needs, your personal knowledge, and your budget. If you are comfortable at coding and you have time and resources to do so, building your own scraper can be a great, flexible, and cost-effective solution.

However, in most cases, you will be better off investing in a 3rd party tool instead. It saves you a lot of programming time and effort, and unless you’re a true coding expert, it will help you gather a lot more data than doing it yourself. When choosing a third-party tool, make sure that it provides accurate, real-time data from all Google features and products on a city, country, or coordinate level.

That said, choosing a third party tool is the easiest way to scrape Google SERPs on a large scale.