Nowadays, basing business decisions on evaluating and analyzing data is the most essential factor in leading your market and staying ahead of your competition. Data-driven businesses rely on large scale data collection for their day-to-day operations, as well as market research and customer experience optimization. Once upon a time, it was up to manual examination and copy-and-paste to collect the data needed for research and analysis. Fortunately, automated web scraping was developed.
This blog post is your Definitive Guide to Web Scraping.
We will explain what is web scraping, how it works, explore the most common uses for it, and talk about what is in store for the scraping industry in 2020.
What is Web Scraping?
Web Scraping, aka Data Scraping or Data Extraction, is the collection of large amounts of data from online sources like social media and online shopping websites. An automated scraping software scans and collects information from websites, minimizing efforts and time spent on extracting valuable data.
Web scraping is used to gather scattered data from multiple sources into one place. Locating and aggregating information from different websites the old-fashioned way can take a lot of time and resources. Automated data extraction makes the process a lot more effective, efficient, and affordable.
So, what are the 4 steps for web scraping?
Scraping a website is composed of a series of simple steps, that when executed on a large scale and a parallel manner, have tremendous effect and power.
Step 1: Define what data is needed from the specific data source you are interested in. This can be pricing data, customer feedback, product information or any other information that is displayed in a consistent way across a website.
Step 2: Fetch the HTML of the page you are interested in. The HTML is the raw data that is shown on a webpage. The difficulty of this step is that many of the larger websites apply an anti-bot mechanism that needs to be evaded in order to retrieve a large number of HTML pages. This is done using proxy servers or in other words: changing your IP address for every request.
Another difficulty with not being detected as a bot is sending the correct HTTP headers to the website you are sending your request to. This means that you cannot just HTTP-GET a webpage without opening a real browser and appearing to be a legitimate user.
Step 3: Build the web page scraper: Once you have the HTML of the page, you want to scrape the data, or to parse the data, off of that page. There are common tools that help you extract this data – including Beautiful Soup, Cheerio, Jsdom, or Puppeteer itself if you choose to use it.
Step 4: Summarize the data into something you can use for your business. Some data out there can be used with little or no analysis: bad reviews for your products, current prices, etc. Other forms of data need further analysis including sentiment analysis, statistical analysis, machine learning, etc.
What are the uses of web scraping?
When you understand how to scrape data from a website, you will start to intuitively realize that there are many uses for a web page scraper. No matter which method or tool you use for scraping, the desired end result is always large amounts of highly granular data.
Businesses use web scraping for multiple tasks:
Nowadays, people review and compare products and services online before they make the purchase. They check customer reviews, product ratings, and compare prices.
So, monitoring and optimizing pricing can be vital for your business. Price monitoring, also called price intelligence or competitive price monitoring, is the analysis of the historical and real-time competitor’s prices in order to optimize pricing strategy.
Monitoring your pricing history can help optimize market strategy, and is a major part of quality market research.
Market research is a data-driven evaluation of the potential of a new product or service. The research involves identifying target audiences, collecting market information, and analyzing customer feedback.
The data collected informs pricing, branding, and marketing, and helps businesses gauge how their new product or service will perform when launched. Good market research also helps identify potential consumer markets. So, the more high quality, granular data available, the better.
Get Data for Sentiment Analysis
Sentiment analysis is the process of analyzing a text and interpreting the attitudes behind it. Automated algorithms scan the text and classify statements as positive, negative, and neutral.
Businesses often use sentiment analysis to classify consumer reviews and gain valuable insights about their consumer market and competitors.
Ranking top on Google Search, Amazon, Apple Appstore, Google Play Store, YouTube, etc, is crucial for leading your market. If we look at the example of web scraping google search results in order to understand how your website is ranking as opposed to your competitors – the data is simpler to analyze, the process in which you extract data from the website is simpler, but you should perform this process many more times in order to gather accurate results over time.
Why do you need proxies for web scraping?
When scraping large amounts of data from a website, IP addresses become an important factor in your operation. In order to understand why we need to understand how a website can detect if data is being scraped off it. The easiest way is to mark the IP addresses that are being used and block each address that shows irregular activity. Examples of such can be multiple page requests in a minute (more than 15 pages in 30 seconds is considered a non-human rate), Irregular site navigation, and loading the same resources over and over again.
We would like to find a way to seem like a legitimate user in the website’s point of view, and for that, we need to constantly switch and rotate the IP addresses we are using. The solution to this problem is using a proxy server, and all the best web scraping tools use such servers.
A proxy server is the “middleman” between a web scraping tool and the websites it is scraping. Each request is sent from the original machine to the proxy server, which is then sent to the destination website with the proxy server’s IP address. The response is then directed back to the machine that originated the request.
Using and handling proxy services is a topic for a separate blog post (stay updated), but keep in mind that when using a scraping service, these are usually handled for you.
Is it better to do it inhouse or rely on 3rd parties?
Inhouse, data scraping is the process of hiring software engineers and building up a scraping system. An inhouse data extraction is a viable option many businesses choose to go for. However, it does require skilled individuals with web-scraping knowledge, and substantial infrastructure. Both building and maintaining an in-house web-scraping team is a complex process. That is why many companies eventually turn to web scraping tools.
Using a Web Scraping tool is better than in-house scraping for several reasons, the main one being that not every business has the resources to run a web crawler in-house. By using a data scraping tool, you will save on software, time, and resources required to run web crawling in house. This way, you can spend your time and effort on data analysis and implementation.
However, an independent web scraping tool isn’t the perfect solution. Scraping tools need to be updated regularly and require you to buy the proxies separately. These limitations can create a lot of mess and have extra unexpected costs. If you are interested in learning more about it, check our article In house web scraping V.S web scraping API.
The future of web scraping
After reviewing how web scraping works and how it is implemented, let’s talk about what the future of data extraction looks like.
Here are some interesting developments and predictions for the scraping industry in 2020:
Improved marketing – web scraping provides quality data for marketers to help them improve and enhance the way brands market their products. Data-driven marketing will only continue to grow, and so will the need for efficient and effective web scraping.
More reliable sentiment analysis – effective and accurate sentiment analysis requires quality data, and the more the merrier. Web scraping enables brands to extract information from different sources (like reviews, surveys, and social media), and make it easier for them to analyze and implement.
Content aggregation – With the growing use of advanced content tools and strategies such as SEO, data scraping will only become more and more valuable. Information provided with web scraping will help content writers identify which keywords work, which tags promote better, and which topics trending.
Sophisticated anti-bot detection – The development of highly advanced scraping bots has brought on the introduction of a more sophisticated generation of bot-detection tools. We are set to see more and more AI-based bot detection solutions, using advanced computer learning algorithms to try and keep up with increasingly powerful and efficient bots.
Looking to get started with some quality web scraping?
Find out more about Scrapezone and how we can help you get quality data that’ll push your business to the next level!