Scraping is now one of the most popular reading data methods from web pages, for their systematization and further analysis. Scraping tools allow manual and automatic retrieval of new or updated data for successful goal realization. It’s worth noting that we are talking about collecting information from the public domain, not hacking or stealing restricted content.
Many successful companies use data to improve their operations. They are making effective decisions for their business, right down to individualized customer service.
According to Octoparse, the industries requiring web scraping skills are Computer Software (22%) and Information Technology and Services (21%).
Scraping is not only used in Information Technology or Media. It helps in such areas as Oil & Energy, Defense & Space, Hospital & Health Care, Education Management, Electrical/Electronic Manufacturing, and so on.
In this article, we will tell you what goals it is used for and give an example of several industries and how scraping helps them.
Web Scraping Purposes
The main purpose of web scraping is to access and analyze information. It helps to create something useful and completely new, not to cause problems and slow down the servers. So, among the goals are a few more.
Scraping allows a company to research competitors to evaluate its own business, monitor markets, and steer its activities in a certain direction for a certain period. It provides significant benefits in terms of competitor analysis. Companies can make balanced decisions about audience preferences and expand their product line to increase sales and revenue.
The software can obtain data from various data analytics providers, market research firms, and other sources. All of the data is then consolidated into a database for analysis.
Customer base creation
Scraping tools help to collect and organize mailing addresses, contact information from websites and social networks. In this way, it is possible to compile lists of contacts and all the information for a business. For example, data on customers, suppliers, or manufacturers.
But it is worth remembering that it is illegal to collect personal information that is not publicly available.
Pricing policy analysis
Scraping in this case will be useful not only for companies that build their pricing policy based on competitors. But also for those who use online shopping services, keep track of product prices, and look for things in several stores at once.
By gathering information about products and their prices on sites like Amazon, you can keep an eye on your competitors and adapt pricing policies to increase profits.
Online store catalog creation
Such sites have a huge number of items. And it takes a lot of time to compile descriptions for all the goods. This is why scraping helps collect information from sites. Companies often scrap foreign stores and simply translate the information, sometimes using synonyms. That is why the texts are not the same. And the company receives almost ready-made descriptions for product cards.
Data parsing is suitable for both an employer looking for candidates to work for a company and a job seeker looking for a specific position. Scraping can set up data sampling based on the various filters provided and effectively collect information without a routine manual search.
Also, job portals often revise and change data. And when changes occur, web scraping can find them and provide the most accurate and up-to-date data.
In such cases, scraping is used to collect the results of sporting events, infographics on price changes, weather, etc. But not only competitors can collect this kind of information.
For example, a journalist can research whether online retailers really offer Black Friday discounts, or artificially inflate them and pass off the real price as a discount.
Competitor analysis also allows understanding what you’re doing wrong when creating content and how you can improve it. You can see what your competitors are writing about and what content best attracts readers. So you can analyze the format of your articles to take it as a starting point.
Collecting data for machine learning
Scraping helps collect data for testing and training machine learning models. This is a very important step in any project. Professionals often use different methods to obtain datasets. They can use publicly available data as well as data available through APIs or obtain from various databases. The quality of machine learning models depends on the quality of the data used. And when data is not available, scraping can be used to collect it from different websites.
Who Uses Data Collection?
In fact, scraping has multiple applications in every sphere of life, from research for scientific writing to the automotive industry, where data is collected to predict future trends and analyze customer preferences and purchasing ability. Here are some of the industries that use scraping for their needs.
E-commerce companies use scraping to gather information about competitors, adjust or develop an entirely new strategy, or anticipate future consumer needs. Companies also collect data with reviews on products or services to conduct analysis. This way, they learn the shortcomings of their products and can use this data to improve business and gain a competitive advantage.
Media companies use scraping to collect comments, likes or to identify relevant topics. And gathering data from social networks like Facebook, Twitter, and LinkedIn is a complex task that is usually done by experts to provide the necessary data as quickly as possible. Most social sites allow search engine crawlers to retrieve data through their API, thereby retaining control over information about users and their actions.
Real estate scraping collects information from customer profiles, email addresses, phone numbers, and more. It’s handy to collect information on foreclosures, homes, agents, property photos, trends, etc. For example to determine competitive real estate prices based on sales price data or to compare prices of different lots or homes.
Travel companies collect information on popular vacation and travel destinations to analyze the preferences of the target audience. Or to study traveler profiles to see their desire to travel and use this information to compose itineraries. It’s also a great way to analyze price trends for services, to see the demand for hotels at different times of the year, to build travel plans such as museums or restaurants, etc.
Law and Finance
Scraping helps more than just travel or e-commerce. For example, a lawyer can look at past judgments to deal with a case he has not dealt with before.
Or bank analysts can use financial statements to determine a company’s position and monitor market conditions. Scraping can pull financial reports from different sites for analysis, with which companies can make investment decisions.
Scraping programs and tools
Information on the Internet is too voluminous to retrieve manually. That’s why companies use scraping to collect data faster and cheaper. Tools perform a myriad of processes in data extraction, from preventing IP blocking to scraping the target website. Web scrapers and data mining tools make this process simple, fast, and seamless.
But how to find the right scraper? The answer is simple – it all depends. The more you understand why you’re collecting data, the better idea you’ll have about which web scraper is best suited. Now we will show what types of scrapers there are and what tools can be used.
By resource utilization
If the scraper will be used constantly for business tasks, then you need to decide on whose side the algorithm will work: on the implementer’s side or yours. True, to deploy a cloud solution, you will have to hire an expert to install and maintain the software, and allocate server space. Also, the program will eat up the server capacity and will cost a pretty penny.
It is worth taking into account privacy, because some companies do not allow storing data on other servers, and you have to look at the specific service. The collected data can be transmitted directly through the API, and this point can be solved with an additional clause in the agreement.
There are too many nuances when using a scraper on your side, so you should think it over carefully.
By access method
Cloud services do not require installation on the PC. All data is stored remotely on the developers’ servers and does not consume space. Access to the software is via a web interface or API.
However, they are more expensive than desktop solutions and require configuration and maintenance. And not all cloud services guarantee a positive result. You may encounter a complex structure and site technology that the service does not understand. Or security that turns out to be too difficult or inability to interpret the data.
Such programs are installed on the computer. They are used for irregular and non-resource-intensive tasks, and in them, you can adjust data collection parameters visually.
Scrapers like this waste computer resources, work only on the operating system they were written for and there is no guarantee that the program will be able to collect the necessary data.
These parsers create programmers. Without special knowledge, it is impossible to make a parser by yourself. Today the most popular language for creating such programs is Python. Libraries for parsing sites in Python provide an opportunity to create fast and efficient programs, with further integration via API. So, the most common Python frameworks are Scrapy, BeautifulSoup, Grab.
An important feature is that these frameworks are open-source code.
Scrapers in the browser extension form are very easy to use. Requires minimal effort – all you need is a browser to install. Scrapers extract data from the HTML code of pages and unload them into convenient formats – XLSX, CSV, XML, JSON, Google Tables, and more. This is how you can collect prices, product descriptions, reviews, and other data.
Based on tables
These scrapers collect data in Excel and Google Sheets. Suitable for simple tasks where there is no protected data and they are in the standard non-dynamic areas.
Based on Excel
The programs scraping with the subsequent unloading of data into XLS* and CSV formats are implemented with the help of macros (special commands for automating actions in MS Excel).
An example is ParserOk which collects information from sites based on VBA (macros) into Microsoft Excel sheets.
Based on Google Sheets
Importxml imports data from XML, HTML, CSV, TSV, RSS, ATOM XML sources into table cells using Xpath queries. Importhtml has a narrower functionality that imports data from tables and lists placed on the site page.
Customizable scraping solutions
Such services approach the task individually, because scraping works for a specific request. They are best suited for private business tasks, for example, when it is necessary to analyze competitors, collect certain types of data, and do it constantly.
The advantage is that a specially designed solution will gather data even from well-protected sites or data that requires interpretation, such as when something is displayed not as text but as an image.
For instance, we develop scrapers, including custom ones. So if you need expert advice, you’re welcome to contact us.
Global trends are bringing us closer to the time when absolutely all transactions will take place online. Volumes of information today are so large that it is very difficult to process them independently. And it is very important to get data both about current market movements and local news. That’s why all kinds of industries use scraping for their business.
If you have questions about scraping and want to know more, you can contact us for advice!