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Tomas Laurinavicius
Tomas Laurinavicius

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The Secret to Rapid Scaling: How Scraping Helped These Startups Go From Zero to $1.2+ Trillion

As of today, March 28, 2023, Airbnb, Amazon, and Netflix have a cumulative market cap of $1.2+ trillion thanks to this one unsexy strategy.

To put that in perspective, if their worth was a country's GDP, it would rank 15th in the world (right below Spain).

What’s their secret to rapid growth and market dominance?

It’s data extraction at scale (also known as web scraping). It’s been used by the most explosive startups to acquire users and grow.

Read on to find out what’s web scraping and how you can benefit from using publicly available data for your business.

Web Scraping: The Secret to Scalable Growth

In today’s digital economy, data is the new differentiator.

Having reliable data at your disposal can give your business a competitive edge.

Amazon (Market Cap: $1.01T)

Amazon leverages big data collected from the internet, and their customers’ behavior, to update their product pricing approximately every ten minutes. Their pricing is set according to the general trends in the market, users’ shopping patterns, and business goals—among others.

Amazon sales

By capturing big data, Amazon can smartly offer discounts on best-selling items and, at the same time, earn large profits on less popular products. This data-driven strategy has proven fruitful as they significantly doubled their annual sales from 2018 to 2021.

Netflix (Market Cap: $148.45B)

Netflix experienced similar success. They used web data acquisition to gather data about the preferences of their viewers and potential subscribers.

Netflix churn rate

Unsurprisingly, many of the Netflix Original shows are a hit, helping them maintain a low churn rate of 2.4% from 2019 to 2021.

Airbnb (Market Cap: $74.50B)

In the early days of Airbnb, the company used Craigslist as a source of listings and scraped data from the site to populate its own platform.

Airbnb email

This helped Airbnb rapidly acquire many listings and users.

These examples show that data harvesting is helpful in various businesses, regardless of the industry, type, or size.

Every organization that strives to scale should leverage publicly available data and use it to its advantage.

  • But how?
  • How can organizations collect web data at a large scale, automatically, and within minutes?

The answer is web scraping.

Three major benefits of data harvesting:

  1. Give insight into the market condition
  2. Close observation of competitors
  3. Deep understanding of consumer behavior

What is Web Scraping?

Web scraping is a method for extracting large amounts of data from the internet. This intelligent automated approach gathers everything from prices to product specifications, property listings, and publicly available data.

The results can be presented in structured file formats: XML or JSON.

Put simply, web scraping can be compared to “copy-pasting” content from websites, but it differs in the process and the tools needed to perform the action.

As you can imagine, data scraping requires a web scraper and a few lines of code to function. Some common programming languages and libraries used include Python BeautifulSoup and Python Scrapy.

Furthermore, unlike manual copy-pasting, a web scraper can harvest information from thousands of URLs by queuing requests in bulk.

This scalable solution eliminates any human intervention during the scraping process, saving you time and manual labor.

But Is Web Scraping Legal?

One general concern around web scraping is whether or not it’s legal.

No government has passed laws explicitly legalizing or de-legalizing web scraping thus far (2023). Therefore, we can only make strong assumptions based on case law about web scraping activity (e.g., HiQ vs. LinkedIn) and other data-related regulations.

We know that web scraping itself is legal—but it can be illegal depending on what type of data you scrape and how you scrape it. In general, you can legally scrape the internet as long as:

  • The data is publicly available
  • You don’t scrape private information
  • You don’t scrape copyrighted data
  • You don’t need to create an account and log in to access the website, OR you have read and fully understood the Terms and Conditions (T&Cs)

⚠️ Disclosure: I’m no expert, and the information given is provided for informational purposes only. Please seek legal advice if you’re in doubt about your web scraping project to ensure you’re not scraping the web illegally.

The Standard Sync Web Scraping Process

There are two primary components of a web scraper, the web crawler and the web scraper itself.

Web crawlers

The web crawler works similarly to a search engine bot. It crawls a list of URLs and catalogs the information. Then, it visits all the links it can find within the current and subsequent pages until it hits a specified limit or there are no more links to follow.

Web scrapers

After the web crawler visits the dedicated web pages, the web scraper will collect the data. An integral element of a web scraper called ‘data locators’ will find, select, and collect the targeted data from the HTML file of a website at scale without being blocked.

In simple words, this is how web crawling feeds into sync scraping: once data is crawled, it can be harvested. When the first scraping request is complete, you can begin the next task.

Of course, the purpose of your scraping needs will always determine the type of scraper and method/s you use. Depending on your timeline and the volume of data collection you need, you may face challenges when you try to use a standard sync scraper to complete multiple tasks. Why? Because you’re bound to a limited response (timeouts) and the need to re-submit tasks.

Using an asynchronous scraper service, you can scrape at scale without these problems. It requires less coding and less infrastructure needed to build or maintain on your side. This speedy, modern method allows you to submit a large batch of requests simultaneously—still working to achieve the highest reachable success rate.

Once the job is done, you’ll be notified.

Web scraping process

Source: ScraperAPI white paper.

Web scraping process

  1. The web crawlers visit the given URLs.
  2. The web scrapers request the page’s HTML file, parsing the response to generate a node tree. Most web scrapers will only parse the HTML code on the page, but more advanced web scrapers will also fully render the CSS and JavaScript of the page.
  3. The scraper bots extract the data based on pre-set criteria (name, address, price, etc.) by targeting elements using HTML tags or CSS/Xpath sectors.
  4. After the information is harvested, the scraper bots export the data into a database, spreadsheet, JSON file, or any other structured format, and it’s ready to be repurposed.

Learn Web Scraping: The Next Step

If you want to learn more about web scraping, I suggest starting with the basics and familiarizing yourself with the jargon. This will allow you to quickly search Google and find answers to any specific questions for your use case.

If you don’t know what “parallel requests,” “custom headers,” or “honeypots” are, you’ll have a hard time figuring out how to make things work.

If you’re interested, download this web scraping white paper (it’s free) to learn about:

🤖 Web scraping benefits and processes

💽 Types of data collection and web scrapers

😾 Common challenges (and how to overcome them)

✈️ Industries that use scrapers in their day-to-day tasks

🪄 Tips for using a web scraping API more effectively

Web Scraping: The Basics Explained

👉 Web Scraping: The Basics Explained

Disclosure: I’m a growth consultant at ScraperAPI.

Featured image credit: Visual Capitalist.

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