Across its global marketplaces and encompassing every one of its third-party sellers, it’s estimated that Amazon’s entire inventory includes around 350 million products, covering every category and niche you could possibly imagine.
If you run an ecommerce business, then, you can learn a great deal from how this online goliath operates, with pricing being a particularly abundant area for analysis: understanding how Amazon prices its vast range of products can help online businesses evaluate the state of the market, measure competitor pricing strategies, and remain competitive by continually evaluating and evolving their own pricing models.
With millions upon millions of product prices to pore through, however, collating pricing information from Amazon isn’t a task you’ll want to tackle manually. Fortunately, it’s possible to scrape prices from Amazon — ‘scraping’ simply refers to crawling a website and extracting data from it — using Python. But how?
In this post, we’ll explain the process of using Python to scrape prices from Amazon. But first, let’s define precisely what Amazon price scraping is, and outline the reasons for doing it.
What is Amazon price scraping?
Amazon price scraping is the process of extracting data from Amazon’s website in order to collate pricing information on specific products. Once you’ve scraped Amazon’s data, you can analyze and manipulate it to gain valuable insights related to market trends, competitor pricing strategies, and more.
At its most basic level, Amazon price scraping involves using Python to download product data from Amazon’s API. The process is quick, simple, and free to set up — and once you’ve downloaded the data, you can analyze it using standard spreadsheet software, such as Microsoft Excel.
You can also store all the product information you’ve extracted in a database to make it easier to run comparison scenarios, while you can also schedule daily price scrapes and create alerts that trigger every time a price updates. Find out more about how to scrape Amazon prices without using any code with ScrapingBee’s guide on scraping Amazon prices.
There can be several benefits to scraping Amazon prices — which include:
- Monitoring competitor prices. If you’re running an ecommerce business and regularly monitoring competitor prices, Amazon price scraping can dramatically reduce the time and effort you put into this process. You can scrape pricing data for every product on Amazon in a matter of minutes, then collate and consolidate the data and view it in one single location. From there, you can analyze it, manually flagging any pricing anomalies that you notice.
- Evaluating the market. Beyond monitoring and adjusting your own pricing, Amazon price scraping allows you to understand the broader market and discover the overall pricing trends in your niche. You can identify common price points, and discover the products that are priced beyond what the market can bear. You can then use this insight to inform your pricing strategy, ensuring that you’re charging a fair price while staying competitive.
- Identifying price changes. Amazon price scraping can also be used to identify price changes, either in a single product’s price or across the market as a whole. This can be a great way to predict future market movements. For example, if you discover that a certain product’s price has dropped by around 10% across the board, you can trigger an adjustment to stay competitive. This can be particularly useful in niche markets, where products remain relatively static.
- Automating price adjustments. Given the vast amount of data that price scraping can generate, you may also wish to automate price adjustments. For example, if you discover that a certain product from a particular supplier has seen a significant price drop, you could program a system to trigger a price adjustment on those products in your inventory. This can be an especially useful tool for businesses that sell products in bulk.
- Understanding price elasticity. Amazon price scraping can help you to better understand price elasticity — or, in simpler terms, the relationship between price and demand. This can help you to make more informed pricing decisions and avoid pricing your products too low. As a rule of thumb, if the quantity demanded of a product changes more than the price changes, then the product is considered to be elastic (for example, the price goes up by 5%, but the demand falls by 10%).
How to scrape Amazon prices using Python
To scrape Amazon price data, you’ll need to have a few things ready. First, you’ll need an Amazon Associates account. This account will allow you to access the data feed and collect your competitor’s data for free. If you want to collect your own product data, you’ll need to sign up for Amazon Web Services (AWS) and pay a monthly fee.
If you want to get creative with your Amazon price scraping, you should consider investing in a web scraping tool: these tools are useful because they’re designed to scrape any data feed, which means you can scrape Amazon price data, competitor data, and even your own product data without having to write a single line of code.
Step 1: Gather URLs for the products you want to scrape
You can find the URLs you intend to scrape by logging into your Amazon Associates account and searching for your products. Once you’ve logged into Amazon, you should see a list of products related to your account. Click on the “Products” tab and select the “Search Products” option from the drop-down menu.
Once you’ve selected this option, you should see a search bar at the top of your screen. Type in the name of the product you want to scrape, choose a category from the drop-down menu and click the “Search” button. Once you’ve done this, you should be taken to a product page. You can then copy the URL from your browser and paste it into a text document.
Step 2: Use Python to collect and parse HTML content
Now that you have the URL for the product you want to scrape, it’s time to collect the HTML content. You can do this by using Python’s HTML.parser library. Once you’ve typed this code into your code editor, select “Run” from the drop-down menu and then “Run Module”.
This will run your code and collect the HTML content. You can then copy the content from your computer and paste it into a new text document. Now, you’ll want to parse this HTML content. You can do this by clicking on “Edit”, selecting “Edit With Markdown”, and then typing in the necessary code.
Step 3: Extract the necessary Amazon price data
Now that you’ve collected the HTML content of the product you want to scrape, it’s time to extract the necessary Amazon price data. Type the necessary code below into your code editor, then select “Run” from the drop-down menu followed by “Run Module”. This will run the code you’ve just typed into your code editor and extract the necessary Amazon price data. You can then copy this data from your computer and paste it into a new text document.
Step 4: Save the scraped Amazon price data
Now that you’ve extracted the necessary Amazon price data, you’ll want to save it to your computer. You can do this by clicking on “File”, selecting “New”, and then “New File”. Next, you should type the name of your file and select “Python” as the file type.
Once you’ve done this, you can click on “Edit”, select “Edit With Markdown”, and then type in the code. Once you’ve typed in the code, select “Run” from the drop-down menu and then “Run Module”. This will save the Amazon price data to your computer.
Step 5: Clean and manipulate the scraped data
Now that you’ve collected and saved the Amazon price data, you can begin to clean and manipulate it. You can do this by clicking on “Edit”, selecting “Edit With Markdown”, and then typing the necessary code into your code editor. Now, you should select “Run” from the drop-down menu and then “Run Module”. This will run the code and clean and manipulate the Amazon price data.
You can then copy the data from your computer and paste it into a new text document. You can now manipulate the data to your requirements. You can also perform analysis to gain insights related to market trends, competitor pricing strategies, and other topics.