Using Web Scraping Tools For eCommerce Competitor Research
One of the most lucrative industries in the digital space is eCommerce. But it’s also one of the most competitive. That’s where web scraping tools for eCommerce data come in.
So, whether you own an eCommerce store that wants to dominate the competition or a developer looking to innovate, stick around!
In this article, we’ll be covering the basics, how to scrap eCommerce data for competitor analysis, and how to prevent common issues that come with scraping.
What is Web Scraping?
Scraping is the act of extracting data from a website. The data can be used for analysis that decision-makers can use to develop stronger business strategies.
There are several web scraping tools and strategies you can use to scrape data but the majority follow three steps:
- A scraper requests data from your target website, sending a GET request. The website server responds by sending content.
- The scraper scans the HTML code sent by the server and attempts to understand its structure.
- Finally, it gathers data relevant to your business needs and outputs them in a simplified format.
How Can eCommerce Use Web Scrapers?
Web scrapers are versatile tools that help businesses make data-driven decisions. It can be used for a number of use cases in the world of eCommerce such as:
E-commerce Price Monitoring
To succeed in eCommerce, you need to have competitive pricing. Price monitoring offers businesses a way to create a solid pricing strategy.
With the help of web scrapers, you don’t have to manually go through a list of competitors to compare product prices.
Having an automated price monitor also helps you identify key metrics such as price index, margins, and conversion rates.
Price comparators save customers time during the research phase of their purchasing journey. There are several good price comparators out there, each one using web scraping APIs. But, businesses can cut the middle man and integrate price comparators straight into their site.
Data for store-based analytics such as product availability is important. It alerts you to delist products that aren’t available and the shoppers.
Without this, your eCommerce store can have a loss of recurring sales and potentially lose future sales.
Reviews and testimonials are two of the best forms of social proof. Research shows that showing reviews leads to customers viewing your products as more trustworthy.
If your eCommerce site is selling the same products as another store, you can use scrapers to extract data from reviews and showcase them on your own site or other marketing endeavors.
Competitor research is one of the most important use cases for web scraper APIs when it comes to eCommerce. It provides businesses with the necessary data for analytics that can help businesses scale operations.
Here’s how you do it.
How to Scrape eCommerce Data for Competitor Analysis
You can scrape data from most, if not all, eCommerce sites. Among the common types of data being extracted for competitor research are:
- Product Name
- Image URL
- And, the currency being used
But before we can get to scraping…
Prepare the following
We need a headless browser (a web browser lacking a graphical user interface) like HtmlUnit, PhantomJS, or Ghost.
In this example, we’ll be using HtmlUnit to perform the HTTP requests and to extract data from the DOM of this website.
To start, add this dependency to your POM.
We’ll also add the Jackson library to parse and generate our JSON files.
Use Schema Markups
Schema is a semantic vocabulary from schema.org. You can use schema Markups to identify structured data on websites.
Most sites have this as it has several benefits, especially when it comes to search engine optimization (SEO). Using schema is convenient because your scraper can extract specific data. This eliminates the use of specific xPath or CSS selectors.
Sites that have structured data make it easier for bots to understand the context of a page (products, reviews, articles, organizations, and more).
There are three types of schema markups: JSON-LD, RDF-A, and Microdata, the type we used in this example.
To extract the data, let’s create a basic POJO of a Product.
Afterward, we need to head to the URL we want to extract data from and create a basic microdata parser. This allows us to extract the data fields we need.
This parser isn’t optimal as of yet. It can’t even handle multiple offers. But, it does do a good job of giving you an idea of how to extract Schema data from a website.
Finally, we can then output the Product object as a JSON string.
How to Prevent Being Blocked
Now that we can extract the data we need, all that’s left is to make sure we don’t get blocked by the website. Sites such as eCommerce have anti-bot defenses that protect them from heavy automated traffic, spam requests, and more.
There will be cases where your IP would be blocked if you made too many requests. Some sites will block you if you keep sending x amount of concurrent requests per second, hour, or day.
The best way to overcome this is by adding delays to your requests and using proxies in tandem with random user agents. There are several free proxies you can find. But be careful, some of these are from legitimate companies with premium offerings, while others are more sketchy.
As a rule of thumb, use paid services or build your own.
If you want to keep up with the competition—doing competitor research is the way to go. And, among the best ways to conduct competitor research is with the help of web scrapers.
Here are some important details you might’ve missed:
- Web scrapers can help eCommerce businesses monitor prices, compare prices against competitors, report product availability, and even extract reviews.
- Schema Markups can help you identify structured data within websites.
- You can use web scraping APIs to eliminate the need for headless browsers, proxies, or CAPTCHas.