Data is critical for business and internet is a large data source including insights about vendors, products, services, or customers. Businesses still have difficulty automatically collecting data from numerous sources, especially the internet. Web scraping enables businesses to automatically extract public data from websites using web scraping tools.
In this article, we will dive into each critical aspect of web scraping, including what it is, how it works, its use cases and best practices.
What is web scraping?
Web scraping, sometimes called web crawling, is the process of extracting data from websites. The table below presents a comparison of leading web scraping tools. For an in-depth analysis, refer to our comprehensive guide.
|3K free requests
|$5 free for a month
How does web scraper tools / bots work?
The process of scraping a page involves making requests to the page and extracting machine-readable information from it. As seen in figure 2 general web scraping process consists of the following 7 steps :
- Identification of target URLs
- If the website to be crawled uses anti-scraping technologies such as CAPTCHAs, the scraper may need to choose the appropriate proxy server solution to get a new IP address to send its requests from.
- Making requests to these URLs to get HTML code
- Using locators to identify the location of data in HTML code
- Parsing the data string that contains information
- Converting the scraped data into the desired format
- Transferring the scraped data to the data storage of choice
Figure 2: 7 steps of an web scraping process
Bright Data offers its web scraper as a managed cloud service. Users can rely on coding or no-code interfaces to build scrapers that run on the infrastructure provided by their SaaS solution.
Which web crawler should you use?
The right web crawler tool or service depends on various factors, including the type of project, budget, and technical personnel availability. The right-thinking process when choosing a web crawler should be like the below:
We developed a data-driven web scraping vendor evaluation to help you selecting the right web scraper.
Figure 3: Roadmap for choosing the right web scraping tool
Top 10 web scraping applications/use cases
Data Analytics & Data Science
1. Training predictive models: Predictive models require a large volume of data to improve the accuracy of outputs. However, collecting a large volume of data is not easy for businesses with manual processes. Web crawlers help data scientists extract required data instead of doing it manually.
2. Optimizing NLP models: NLP is one of the conversational AI applications. A massive amount of data, especially data collected from the web, is necessary for optimizing NLP models. Web crawlers provide high-quality and current data for NLP model training.
3. Web scraping in real estate: Web scraping in real estate enables companies to extract property and consumer data. Scraped data helps real estate companies:
- analyze the property market.
- optimize their prices according to current market values and customers’ expectations.
- set targeted advertisement.
- analyze market cycles and predict the forecast sales.
Oxylabs’ real estate scraper API allows users to access and gather various types of real estate data, including price history, property listings, and rental rates, bypassing anti-bot measures.
Marketing & sales
4. Price scraping: Companies can leverage crawled data to improve their revenues. Web scrapers automatically extract competitors’ price data from websites. Price scraping enables businesses to:
- understand customers’ purchase behavior.
- set their prices to stay competitive by tracking competitors’ product prices online
- attract their competitors’ customers.
5. Scraping/Monitoring competitors’ product data: Web scrapers help companies extract and monitor products’ reviews, features, and stock availability from suppliers’ product pages. It enables companies to analyze their competitors, generate leads, and monitor their customers.
6. Lead generation: Lead generation helps companies improve their lead generation performances, time and resources. More prospects data is available online for B2B and B2C companies. Web scraping helps companies to collect the most up-to-date contact information of new customers to reach out to, such as social media accounts and emails.
Check out how to generate leads using Instagram search queries such as hashtags and keywords.
7. SEO monitoring: Web scraping helps content creators check primary SEO metrics, such as keywords ranking, dead links, rank on the google search engine, etc. Web crawlers collect publicly available competitor data from targeted websites, including keywords, URLs, customer reviews, etc. Web crawlers enable companies to optimize their content to attract more views.
8. Market sentiment analysis: Using web scrapers in marketing enables companies:
- analyze and track their competitors’ performance on social media
- optimize their influencer marketing activities
- track the actual performance of their ads
9. Improving recruitment processes: Web scrapers help recruiters automatically extract candidates’ data from recruiting websites such as LinkedIn. Recruiters can leverage the extracted data to:
- analyze and compare candidates’ qualifications.
- collect candidates’ contact information such as email addresses, and phone numbers.
- collect salary ranges and adjust their salaries accordingly,
- analyze competitors’ offerings and optimize their job offerings.
Finance & Banking
10. Credit rating: The process of evaluating the credit risk of a borrower’s creditworthiness. Credit scores are calculated for an individual, business, company, or government. Web scrapers extract data about a business’s financial status from company public resources to calculate credit rating scores.
Check out top 18 web scraping applications & use cases to learn more about web scraping use cases.
Top 7 web scraping best practices
Here you can find top 7 web scraping best practices that help you to imply web scraping:
- Use proxy servers: Many large website operators use anti-bot tools that need to be bypassed to crawl a large number of HTML pages. Using proxy servers and making requests through different IP addresses can help overcome these obstacles. If you cannot decide which proxy server type is best for you, read our ultimate guide to proxy server types.
- Use dynamic IP: Changing your IP from static to dynamic can also be useful to avoid being detected as a crawler and getting blocked.
- Make the crawling slower: You should limit the frequency of requests to the same website due to two reasons:
- It is easier to detect crawlers if they make requests faster than humans.
- A website’s server may not respond if it gets too many requests simultaneously. Scheduling crawl times to start at the websites’ off-peak hours and programming the crawler to interact with the page can also help to avoid this issue.
- Comply with GDPR: It is legal and allowed to scrape publicly available data from websites. On the other hand, under GDPR, It is illegal to scrape the personally identifiable information (PII) of an EU resident unless you have their explicit consent to do so.
- Beware of Terms & Conditions: If you are going to scrape data from a website that requires login, you need to agree on terms & conditions to sign up. Some T&C involves companies’ web scraping policies that explicitly state that you aren’t allowed to scrape any data on the website.
- Leverage machine learning: Scraping is turning into a cat & mouse game between content owners and content scrapers with both parties spending billions to overcome measures developed by the other party. We expect both parties to use machine learning to build more advanced systems.
- Consider open source web scraping platforms: Open source is playing a larger role in software development, this area is no different. The popularity of Python is high. We expect open source web scraping libraries such as Selenium, Puppeteer, and Beautiful Soup that work on Python to shape the web crawling processes in the near future.
What are the challenges of web scraping?
- Complex website structures: Most web pages are based on HTML, and web page structures are widely divergent. Therefore when you need to scrape multiple websites, you need to build one scraper for each website.
- Scraper maintenance can be costly: Websites change the design of the page all the time. If the location of data that is intended to be scrapped changes, crawlers are required to be programmed again.
- Anti-scraping tools used by websites: Anti-scraping tools enable web developers to manipulate content shown to bots and humans and also restrict bots from scraping the website. Some anti-scraping methods are IP blocking, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), and honeypot traps.
- Login requirement: Some information you want to extract from the web may require you to log in first. So when the website requires login, the scraper needs to make sure to save cookies that have been sent with the requests, so the website recognizes the crawler is the same person who logged in earlier.
- Slow/ unstable load speed: When websites load content slowly or fail to respond, refreshing the page may help, yet, the scraper may not know how to deal with such a situation.
To learn more about web scraping challenges, check out web scraping: challenges & best practices
For more on web scraping
- Web Scraping tools: Data-driven Benchmarking
- Top 7 Python Web Scraping Libraries & Tools in 2023
- The Ultimate Guide to Efficient Large-Scale Web Scraping 
If you still have questions about the web scraping landscape, feel free to check out the sortable list of web scraping vendors.
You can also contact us:
This article was originally written by former AIMultiple industry analyst Izgi Arda Ozsubasi and reviewed by Cem Dilmegani
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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