Technology is continually evolving around us, changing how we work, play, interact with each other, learn, and shop. Machine learning algorithms are getting incredibly precise, and data collection is becoming essential for companies to make any decision.
The most significant data source around us is all publicly available online and generated by users, as UGC content accounts for the vast majority of the world’s data. Therefore, it is understandable that companies and organizations are tapping into it and harvest this immense source of information and making smart decisions.
The more data a company can retrieve, the more accurate it might get, and the faster it can reach the decision-making algorithms. As algorithms have more data to feed them, the more precise the results will be, the higher the potential profit margins and success rates.
Understanding your customer’s opinions, monitoring your competitors, understanding markets and trends, and finding new niches and product opportunities are the most common eCommerce data-related conclusions companies learn from today. Still, the insights gained from web scraping are only limited to one’s imagination.
Nowadays, there are many ways to scrape websites, varying from designing your web scraping tools with an in-house scraping operation, using a web scraping API, and any combination of the two.
What data can be scraped?
Almost all data points one can see when browsing the web can be scraped, analyzed, and turned into informative insights, as long as it is publicly available and presented consistently. All pages have to be aligned similarly. All eCommerce, travel, and finance websites are usually straightforward for web scraping, so are forums and some social media apps.
What are the top five most scraped data points?
This data point is the most common and popular among eCommerce, travel, and services companies. Collecting and comparing prices across different products, sites, and services are the most basic and essential operations for any company that offers online-purchasable products. Comparing prices over time and crossing it with impacting price events like Black Friday can also lead to a substantial profit margin increase.
Understanding your customers and even your competitor’s consumer opinions can substantially increase your customer’s satisfaction. Establishing your product design, pricing, and the overall customer engagement on what they think and share is vital for raising your brand’s image. Consumer reviews can be aggregated from different eCommerce sites like Amazon, Google Shopping, Google Maps, Trip Advisor, Walmart, Target, and many other websites.
Scraping reviews can be used for both sentiment analysis and real-time customer support and engagement. Setting a scheduled scraper to read all of the 1-3 star ratings on a product or service page, for example, can lead to swift responses that can change the customer experience in those cases altogether.
So much can be learned and achieved by profoundly learning about the different characteristics of a product or a series of products. Colors, sizes, descriptions, images, and shipping times all convey a considerable amount of processable information and allows reaching profitable insights for brands and companies. Once a product or a service is deeply analyzed and compared with many other similar offers, one can reach a better understanding of markets and trends.
Organic traffic is all about ranking, location, and keyword combinations. When you continuously compare rankings and search location trends with marketing efforts, the learning process is immensely faster. The feedback is provided by scraping search results – whether on Google Search, Amazon, Apple Store, or Play Store. Scraping SEO data can help companies understand precisely where their efforts are putting them in the ranking competition.
It is often considered the most interesting data point since it allows us to reach endless insights about niches, competitors, and market trends. Scraping product metadata such as review count, product availability, and product page changes can lead to unique insights.
The process of Web Scraping
Web scraping allows us to gain insights by seeing an opportunity with valuable data that is repetitively presented across a website, across multiple pages, different times or both, and consistently fetching that data.
Designing and setting up a web scraper is more straightforward than one might think if you follow the golden five steps process:
Here’s How to Create a Web Scraper in 5 Steps:
Let’s look at setting up a web scraper and breaking it down to 5 simple steps, using a prevalent example of monitoring a product price on Amazon.
Define your use case
How will the fetched data be used, what insights and understandings can you gain from scraping it? Each data point or data points combination can support multiple use cases. This is the place to think out of the box and set the intention of gaining valuable information from the available data on the pages we will scrape. In the example of Amazon product price monitoring, we might use this data to allows our customers to buy a product when it’s the price is below a predefined point.
Choose a consistent data point
The most important part of the scraping operation is that the data point needs to be consistent. Luckily, most websites consider user experience to be a good one when the data presented is consistent.
The main reason for this is that you need to predefine the scraper to fetch a specific data point of a page and set it to bring that data multiple times or across numerous similar pages. In our example, the price of a product on Amazon is a very consistent data point. It is always presented in the same manner with the same location on the page (or, more importantly, the same place in the HTML DOM).
Choose what to scrape and when
Most data points that will allow us to learn the most is that we consistently fetch them multiple times over time. In our example, the Amazon price monitoring can be set to scrape the prices of the interest products every hour. So we should set an hourly schedule for the scraper to run, fetching the current price every time.
Set up a system to receive the data and analyze it
The raw data that is returned from the scraping operation needs to be analyzed and processed. This analysis can be done in the form of an excel sheet, some statistical analysis, or fed into a machine learning algorithm. In our Amazon monitoring service example, the analysis is elementary: we need to write a small piece of code that checks if the price is below the specified point, and sends a notification to the user.
Put the data to use
The most important part of the process is how to use or monetize the data received in our system. This can be insights about the variations, pricing or offer we provide on our eCommerce store, a system or SAAS that is built based on the scraped data and sold as a service or one of the many other options of using and monetizing the data. In our example, we can provide a paid service that notifies clients when the product they want is below a given price point.
It isn’t always necessary to scrape a website since some websites provide an API for retrieving data.
Web Scraping vs. Using a Website’s API
When we are talking about using a website’s API, we are not referring to the usage of a scraping API. Still, to the organic and usually paid API, various websites provide.
When using an API, you are limited to the amount of data you pay for, and the data points the specific website is willing to share with you programmatically. Some websites might decide that pricing information is ok to consume via an API, but shipping information or the product image is not. An API can also be rather pricy since the websites have a monopoly over their data.
When scraping or using a Web Scraping API, you are not limited to amounts, or specific data points and can freely harvest any information you can gain insights or profit.
How is web scraping done?
Web scraping (actually fetching the data) usually involves two combined parts: Web Crawling and Web Scraping. The crawling process is for fetching the links of the pages you want to scrape, and then a scraper is set to get the data points of interest from those pages. It is essential to understand the difference between Web Scraping and Web Crawling.
The 4 main use cases for web scraping
Good web scraping involves a lot of creativity since there are endless ways to get data from the internet, countless data points to inspect, and naturally endless combinations of data points. It is probable to say that every industry and business can gain from web scraping directly, or through a product based upon web scraping.
These are the 4 most common usages today for web scraping, according to our statistics:
Web scraping for eCommerce
Due to its swift pace and is based purely on online customer interaction, the world of eCommerce makes scraping for eCommerce the most popular usage of internet data fetching. There are almost infinite examples of websites and utilization in this category, like scraping Amazon, Google Shopping, Walmart, Target, and BestBuy for product rank changes, reviews, price comparison, competitive data.
Web Scraping for SEO
The world of SEO is all about monitoring and noticing minor changes that can help us understand trends and location changes. Whether it is scraping Google Search, Google Play, Amazon, Apple App Store, YouTube, or so on, this category’s importance is continuous monitoring over time, spotting the little changes, and acting based on those insights.
Web Scraping for Travel
The travel world is very much like the eCommerce world, particularly because it is mainly based on fast-changing online customer experience, price changes and sophisticated algorithms. Monitoring the accommodation prices, car rentals, and travel tickets is a very natural use case in this industry.
Web Scraping for Customer Experience
Learning from your customers and your competitor’s customers can be the difference between the success or failure of a brand, business, or company. Reviews data is so valuable, and companies now understand that analyzing customer sentiments, in any industry, can lead to incredible growth and better brand image. Combining periodic scrapes in monitoring reviews and acting on a bad review in real-time can make a huge difference in your customer’s experience.
The bottom line
Scraping is here to stay, and it doesn’t matter what industry your business is in, the teams that will use data-based decisions will eventually win. The recent changes keep reminding us how much everything is about data, and the companies that can obtain, learn, and understand from the most data points will be able to make the smartest and most accurate choices. I hope I have inspired you to take action, look around, and constantly try and find the best and most creative ways to use the abundance of data surrounding us.