Web scraping is a technique that consists of extracting data from any internet page in an automated way. That is, we convert the information that we can find published on a website into a structured database.
For instance, we want to download the results of last weekend’s sports competitions. It will be a difficult task to do it manually. We software a crawler bot, scrape the reports and copy them directly to a database through site scraping.
Copying and converting data from a web page manually to Excel will be known as data extraction. If we automate this job using bots or robots, it’s web scraping.
Data scraping is the most common use, but we can scrape images, videos, and any type of file.
Despite being a new technique for many companies, the use of data scraping is much more common than you might think. Some authors say that robots and not humans make more than 45% of network traffic.
Data scraping vs. web crawling
Scraping and crawling are not the same. Although we tend to use these terms indiscriminately because most users know the technique from the term scraping, although what they really need is web crawling.
A crawler, or spider, crawls through different web pages imitating human behavior. We see it easier with an example. Let’s say we have a hotel and we want to know the price of the competition in the booking. For this, we will program a crawler that:
- You will enter the main booking page.
- It will carry out a search by city, dates, number of people to stay, etc.
- You will get a list of hotels as a result.
- Copy the URL of each hotel page on the platform
- You will enter each page and download the data we need (price, rating, availability, etc.)
- It will repeat the whole process with all the searches that we need to carry out for different scenarios.
- It will return a structured database with the results.
All the data we get by scraping one or more websites can be stored in a database and made available through API.
Of the entire process that our crawler has carried out, only the part referring to downloading the information would be considered data scraping. The rest is called web crawling. Anyway, in our articles, we use both terms indiscriminately, as we have discussed above.
Advantages of using web crawling and data scraping
With web crawling and data scraping, the processes of finding and collecting information are automated; with this, we achieve:
- Reduce workload.
- Cheap personnel costs.
- Increase the speed of the processes.
- Eliminate human error.
- Handle large amounts of data.
- Getting data in actionable formats.
Use case with applicable strategies in the second-hand sector
Client:
Used car dealer.
State of need:
Companies in the second-hand products sector have a double challenge to maximize profits: on the one hand, make purchases at the best price and, on the other, sell at the most optimal price.
Before meeting us, our client had two employees who spent most of the day looking for used vehicles to increase their fleet in the different portals and set the sale price to the public intuitively.
Goals and objectives:
Automate the collection of information on used vehicles in the different portals twice a day, creating alerts for suitable products according to the dealer’s criteria.
Scraping of used vehicles and data processing:
- Scraper bot programming capable of automatically extracting the database of the entire stock of vehicles offered in all the relevant portals in the sector. (Descriptions, prices, images, mileage, age, etc.)
- Data cleaning and processing
- Database programming for searches by relevant parameters.
- Creation of an automated notification system for new vehicles on and off.
- Programming of automated statistics combined with an algorithm to determine the appropriate pricing according to the type of product, age, mileage, etc.
Competitive advantages achieved:
- Savings in labor hours and costs.
- Elimination of human error.
- 24/7 – Our robots run their data tracking on the portals every day of the week, regardless of whether they are holidays or not.
- Pricing based on data, not guesswork.
- A decrease in the purchase price.
- Sales price increase.
- The consequent increase in profit per operation.
- Increase in the number of sales per period.
Final notes on data scraping & web crawling applied to the second-hand sector
In second-hand markets, being informed in time is a huge competitive advantage. Search automation saves significant hours of work that can be used for many other tasks. Automation and web scraping generate cost savings.
With the data as a service from wscraper.com, everyone can take advantage of data crawling without the need for programming knowledge.
