Web scraping: what is it? and what business challenges does it resolve?

Web scraping refers to the collection of data from different websites. It is a technique used to automatically extract large amounts of data from web pages and save them in a database. The data is collected, processed, and then turned into practical knowledge. That is, web scraping is used by people and companies that want to make use of the large amounts of publicly available web data in order to make decisions based on as much information as possible.

web scraping

Websites are designed to be readable by humans and not by machines, and they have differing page layouts, which makes it difficult to extract their data on a large-scale basis. To achieve the required scale, at present,   web scraping tools,  which automate data extraction tasks and save time and effort, are used in order to avoid  the manual task of copying and pasting.

For this reason, web scraping activity is driven by robots or web crawlers that work in the same way as search engines, that is, by searching and copying. The difference, in this case, is that these robots and web crawlers focus on extracting only specific data from certain websites of interest.

AI involved

Web scraping involves writing a software robot that can automatically collect data from various web pages. The most sophisticated bots use artificial intelligence (AI) to find the appropriate data on a page and copy it into the correct data fields so that it is later processed by an analytics application.

Web scraping software automatically loads, tracks and extracts data from various website pages as required.  It can be tailor-made for a specific website or can be configured to work with any website. In this way, these intelligent, AI-powered data extraction, cleaning, standardization and aggregation tools can significantly reduce the amount of time and resources that organizations need to invest in data collection and preparation.

Certainly, this technique can have different objectives depending on the information that needs to be extracted, its format, or the industry for which it is required.  In order for web scraping to not be considered illegal, some rules must be followed.  For example, data that is not publicly available should not be extracted.

Use cases 

Web scraping is used in e-commerce for competitor tracking and product and price comparisons. Companies can use it to set the optimal price for their products, for example. 

This technique is also used for market research: high-quality data obtained in large volumes can be useful for companies to analyze consumer trends and understand what direction the company should take in the future. They can access insightful web data that has the ability to impact their future decisions. For example, after centralizing the data, the average price of all products that have a certain characteristic can be calculated; and thanks to reviews, the interests of consumers in different regards can be known. Data derived from web scraping will provide knowledge that is needed to prepare a launch strategy.

In the marketing field, web scraping can be used for lead generation. There are companies that use it to find websites with multiple contacts and  thus obtain lists of potential customers.

Other applications

In the real estate sector, this technique is used to collect details of the properties for sale or rent, as well as their costs and terms of sale. The information that is obtained helps investors and agents assess property values, track vacancy rates, estimate rental yields, and understand the direction of the market, both in a general sense and for specific locations. 

In addition, web scraping is often used for news tracking, to analyze supply chains, to better understand the labor market and to assist with talent recruitment.

Brand monitoring  is another use case for this technique:  companies use social media data to understand the sentiment and opinions that their products generate among consumers. In addition, web scraping is used to collect training data for machine learning models.  In short, the use cases are numerous.

At Arbusta we offer various data services that allow organizations to benefit from these advanced techniques.