Are you not totally satisfied with manual data collection? It used to work unless you needed to fetch some data about minor Amazon sellers. But as you scale your business, so does the need for more data. In this case, there is a more effective method to harvest information from the marketplace—web scraping.
Amazon web scraping is about extracting specific pieces of information from Amazon (or any other website) and converting them into a structured format for analysis, storage, or use.
How is web scraping different from other ways to find sellers on Amazon?
First, it’s the depth of data. Traditional methods might limit the amount of information you access. They also may require a lot of time. But with web scraping, you can go as deep as you want, and do it really fast. Along with a list of sellers or basic information about them, you’ll be able to extract seller ratings, reviews, product listings, prices, and more—all in bulk and in a fraction of the time. Just customize your requirements, and you may get information about merchants from, let’s say, a particular niche or those with a certain rating.
Also, you can set up the scraper to run at regular intervals for immediate tracking of merchants. If you’re in a highly competitive niche, that’s a great option, as you’ll always know what’s in the minds of your competitors.
Finally, if you want to get the full picture of what is happening with other merchants, Amazon scraping will be of great help. Unlike some platforms or tools that offer curated or summarized data, you’ll get raw information for truly unbiased analysis.
Steps to obtain sellers’ data from Amazon through scraping
If you’re looking to extract seller information from Amazon, you usually have several primary options: get your in-house development team to handle the scraping project or outsource the data harvesting and processing process from a specialized provider.
There is a shortcut—managed web scraping services from vendors. That’s a great option for those who would like to get accurate and comprehensive information without facing technical challenges.
Set up the scraper
For starters, you’ve got to provide input parameters. These include:
- URLs. Set specific Amazon pages you want to scrape. This can be a general seller page, a specific category page, or even a search results page based on your criteria.
- Keywords. Input relevant keywords to find sellers in a particular niche or those selling specific products. For instance, “organic tea sellers” or “handmade jewelry vendors.”
- Categories. Specify a category to narrow down your search and obtain more targeted results. For example, “Electronics” or “Home & Kitchen.”
Further, you need to configure the scraper for dynamic content and AJAX requests. Why is this important? Because the Amazon platform is not static. So, content changes based on user interactions, be it clicking a button or scrolling down a page. Therefore, traditional scraping methods may miss out on this dynamic content.
Moreover, Amazon may detect and ban scraping activity that exceeds the limits. To avoid this, set delays between requests. This will make your scraping activities feel less like a bot.
Finally, follow robots.txt guidelines. That’s a file to guide how search engines and scrapers interact with the marketplace. While it’s not legally binding, respect these rules to reduce the chances of a ban.
Run the scraper
It’s not enough to hit the “start” button to set the scraper into motion. To get the utmost results, you’ve got to be vigilant and adaptable. Here’s what you need to know.
Track the scraping flow real time. Most scraping tools have a dashboard or at least a sort of interface where you can monitor the progress. If any issues occur, you’ll be able to spot and react to them early.
Also, consider periodically checking what seller data you’ve extracted. In case there are any inconsistencies or something is missing, you’ll promptly identify that.
Keep an eye on metrics like the number of pages scraped per minute, the total data extracted, and the time taken. This will give you insights into the scraper’s efficiency and whether you need to make adjustments.
Handle CAPTCHAs and IP bans
Once Amazon detects unusual activity from an IP address, it might present a CAPTCHA challenge. What is it? Simply put, it’s a sort of test Amazon uses to determine whether the user is a human or a bot. They are simple for humans, but a little bit tricky for computers.
Some advanced scraping tools already have CAPTCHA solvers. So, you’ll automatically handle these challenges. But, unfortunately, they are not always successful, so you’ll need to deal with CAPTCHAs manually.
Additionally, use proxy servers to avoid IP bans. This technique will enable you to route your scraping requests through different IP addresses. So, your activities will look less suspicious, and you’ll reduce the chances of any single IP getting banned.
You should also know that Amazon can detect scraping activity based on the user-agent string of requests. The solution to this is to rotate user agents. What for? This will help you mimic your requests coming from different browsers and devices. So, your data collection activities will look more organic.
Data extraction and preparation
As you’ve successfully launched and maintained the scraper, here comes another step—to extract seller data and prepare it for analysis.
How to extract sellers’ data from Amazon
Configure your scraper to identify and extract data based on specific HTML selectors or XPath. The thing is that Amazon structures its web pages using HTML elements. Each element has unique identifiers—classes or IDs. For example, you may find a seller’s name within a <div> tag with a class of “seller-name.” If you deal with complex structures or when you need to select elements based on specific criteria, not just their type or class, use XPath. It’s XML Path Language that offers a more versatile way to navigate through an XML document.
What you should also know is that Amazon lists its sellers across multiple pages. Especially, if it’s a broad category or a popular search term. So, you’ll need to apply pagination to ensure the scraper doesn’t just stop at the first page but continues to extract data from subsequent pages. So, configure your scraping tool to recognize the ‘Next’ button or the page number links at the bottom of the seller listings.
Finally, you need to configure the scraper to save the extracted data in the desired format. Usually, these are CSV, Excel, or JSON as you can seamlessly import them into data analysis tools. For more advanced or ongoing analysis, consider storing the extracted data directly into a database.
How to clean extracted information about Amazon merchants
You scrape raw data from Amazon. To make sense of the extracted information, it’s better to clean it.
There are always chances of fetching duplicate entries. Especially, if you scrape large datasets. So, use data cleaning tools to spot and remove those duplicates.
Don’t forget about standardizing formats. For instance, you can pull out seller ratings both as “4.5 out of 5” and “90% positive.” When it comes to analysis, it will be quite a challenge to compare the same data in different formats. So, I will recommend you to ensure data consistency.
You may figure out that some entries have missing data points. Decide whether you want to fill these gaps with average values, remove such entries, or keep them as they are.
If you extract reviews or product descriptions along with a merchants’ list, consider removing any HTML tags, white spaces, or special characters.
Web scraping for extracting sellers’ data on Amazon is a powerful tool for businesses that want to stay competitive and profitable.
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