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The Ultimate Guide to Web Scraping with RPA in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The Ultimate Guide to Web Scraping with RPA in 2024The Ultimate Guide to Web Scraping with RPA in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Web scraping is the process of extracting data from websites to learn what web pages are about. The extracted data is used in multiple applications such as competitor research, public relations, trading, etc.

Web scraping is not an illegal act, however, a web scraper (human or software) may use the extracted data in a way that contradicts the writer’s interest. That is why there is a constant legal and technical struggle between data collectors (e.g. web scrapers) and data owners, such as the legal case between HiQ Labs and LinkedIn. Data owners are creating barriers to ensure that only humans reach the data and as a response, data collectors are using a combination of tech and human resources to overcome these barriers.

Using RPA bots, users can automate web scraping of unprotected websites via drag-and-drop features to eliminate manual data entry and reduce human errors. However, to scrape websites that heavily protect their data and content, users need dedicated web scraping applications in combination with proxy server solutions.

Here we explore how RPA is used for web scraping:

What is RPA?

Robotic process automation (RPA) is a type of software that performs a repetitive task by replicating human interactions with GUI elements. The interest in RPA is rising as the technology matures and vendors provide low/no-code interfaces to build RPA bots. The global RPA market is expected to reach $11B by 2027. RPA is one of the top candidates to automate any repetitive task and a typical rules-based process can be 70%-80% automated.

When done manually, web crawling can be a tedious task with many clicks, scrolls, and copy and paste repetitions, to extract the designated data. That is why it is compelling to use RPA to automate web scraping.

How to automate web scraping with RPA?

RPA bots perform repetitive tasks by replicating GUI processes which a human user would typically do to perform a task. For web scraping, users would:

  • Find the URL they want to scrape
  • Inspect the page to find data relevant to their search
  • Write code (e.g. python) or use an extension to extract the data
  • Export data to a table

RPA bots can be programmed to log into the designated URL, scroll through multiple pages, extract specific data, and transform it into the required format. The bot can also enter the extracted data directly into another application or system for different usages (e.g. send as an email, modify spreadsheet fields, etc.)

What are the benefits of RPA in web scraping?

Web scraping technology provides the following benefits:

  • Eliminate manual data entry errors
  • Extract images and videos
  • Reduce time of data extraction and entry
  • Automatically monitor websites and portals regularly for data changes

Web scraping tools or RPA software allow users to build scraping bots without writing code or scripts for data extraction.


Though RPA is a great general purpose automation tool, specialized web scraping tools can be easier to use for high scale web scraping projects. Bright Data’s Data Collector enables companies to extract mass-scaled accurate data in real-time from any website.

Source: Bright Data

RPA can be the right tool for web scraping especially if more data processing needs to be done on scraped data. Different technologies can be easily integrated into the RPA bots used in scraping. For example, a machine learning API integrated to the scraping bot, could identify companies’ websites from their extracted logos.

What are the challenges facing RPA in web scraping?

Since RPA bots rely on GUI elements to recognize the wanted data, it is difficult to automate web scraping when pages do not display content in a consistent manner. The top challenges facing RPA in web scraping are:

UI Elements

Some UI elements make scraping harder but they are challenges that RPA bots can deal with

“Load more” button

Typically, the bot will scroll down a page, extract the data and export it to the output file. Some web pages, especially product pages, load data in parts and allow users to explore more products via “load more” button. When this is the case, the bot will stop extracting the data by the end of the page instead of exploring more products.

The solution is to create an if-loop within the bot program to click the “load more” GUI element if it exists until no more buttons appear on the web page.

Go to next page

Same as in “show more” button, some content may be loaded in the following page. The solution would also be to create a loop to click on the “next page” GUI element to open the following URL.

Pop up ads

Pop-up elements or ads can hide GUI elements from the bot’s vision and disable it from extracting the underlying data. The solution would be to use an AdBlocker extension for the browser used for web scraping.

Scrape protection systems

Websites like Linkedin use sophisticated tech to protect their website from getting scraped. In such cases, users have a few options:

  • Work with a company that provides the website data in a data-as-a-service manner. In such a model, the supplier handles all the programming and manual verification and provides clean data via an API or CSV download
  • Build your own data pipeline. You can rely on web scraping software or RPA in combination with proxy servers to build bots that act in a manner that is not distinguishable from humans

To learn how to bypass web scraping challenges, read Top 7 Web Scraping Best Practices“.

Further reading

To learn more about web scraping, feel free to read our in-depth articles about:

And if you are interested in using a web scraper for your business, don’t forget to check out our data-driven list of web crawlers.

And we can guide you choose the right tool:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem Dilmegani
Principal Analyst

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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