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Your step-by-step guide to scraping Amazon product data

Extracting product data from Amazon can be extremely valuable for a range of applications from market research to competitor monitoring. While Amazon does have an Product Advertising API, it comes with rate limits, requires approval, and costs money. An alternative is to scrape Amazon product data directly from the website.

In this comprehensive guide, we‘ll walk through the key steps and considerations for extracting Amazon product information through web scraping.

Overview of scraping Amazon product data

Web scraping involves programmatically extracting data from websites. When done ethically and legally, it enables you to gather large volumes of data from sites like Amazon.

Scraping Amazon can provide all kinds of product information including:

  • Title
  • Product URL
  • Price
  • Images
  • Ratings & reviews
  • Questions & answers
  • Description
  • Bullet point features
  • Variants (size, color etc.)
  • Availability
  • Seller name
  • Category/browse tree

This data can then be exported into a spreadsheet, database, or analytics platform. Scraping Amazon opens up possibilities like:

  • Competitor price monitoring
  • Market research on product trends
  • Discovering best selling items in a niche
  • Sentiment analysis on reviews
  • Inventory monitoring
  • Dropshipping product research
  • Building a product database
  • And much more!

An important first question is whether scraping Amazon is actually legal. The short answer is yes, in most cases.

Web scraping falls into a legal grey area but is generally permissible under these conditions:

  • You scrape publicly accessible data (e.g. not behind a login or paywall)
  • You don‘t violate the target website‘s Terms of Service
  • You don‘t steal intellectual property or copyrighted material
  • You don‘t overload the website‘s servers with an unreasonable number of requests

Amazon‘s Terms of Service do not explicitly prohibit web scraping. As long as you scrape responsibly and adhere to a reasonable scraping etiquette, extracting publicly listed Amazon data is not illegal.

That said, Amazon does have technical measures in place to detect and block scrapers. So extra care needs to be taken to scrape effectively without getting blocked.

Tools for scraping Amazon

There are a few different tools you can use to scrape data from Amazon:

Web scraping libraries like Python‘s BeautifulSoup and Scrapy allow you to write custom scrapers but require programming knowledge.

Browser extensions like Octoparse and ParseHub offer point-and-click GUIs to extract data from Amazon pages into spreadsheets. However, these are limited to single page scrapes.

Off-the-shelf scrapers like the Amazon Product Scraper on Apify enable fully automated scraping of entire Amazon catalogs with no code required. But provide less customization ability.

Commercial web scraping APIs like ScrapeStorm and ScraperAPI handle proxy management and rotation for you, but cost per API call.

For most use cases, an off-the-shelf scraper like Apify or a web scraping library offers the best balance of customization and ease of use when scraping Amazon.

Step 1: Get a list of ASINs or product URLs

ASIN stands for Amazon Standard Identification Number. It is Amazon‘s unique ID given to each product.

To scrape detailed product information, you first need a list of ASINs or product URLs. There are a couple ways to get this:

  • Manually compile – Copy/paste or export ASINs from Amazon category and search pages.

  • Seed sites – Scrape ASINs from sites that list Amazon products like Camelcamelcamel.

  • Parse HTML – Scrape ASINs directly from Amazon category pages.

For large Amazon scraping projects, parsing ASINs directly from Amazon category pages is the best approach. This means scraping the category URLs and extracting ASINs or product links from the HTML.

Most Amazon product URLs follow this structure:

So you can parse the ASIN from the product path, then loop through paginated category pages to build up a base list of URLs/ASINs.

Step 2: Scrape product pages

With a list of ASINs or product URLs in hand, you can loop through each one and extract the desired data from each product page.

Use a tool like Apify or a Python library like BeautifulSoup to parse the HTML and extract product details like:

  • Title
  • Description
  • Bullet point features
  • Pricing
  • Ratings
  • Images
  • Availability
  • Variant options

For pricing, make sure to extract the actual numeric value i.e. 29.99 and not the formatted price like $29.99. This makes it easier to analyze and compare later.

Images can be downloaded to your server or you can save the Amazon S3 URL links in a spreadsheet.

To get reviews, scrape the reviews section or reviews count but avoid scraping reviewer personal information like names.

Step 3: Store the scraped Amazon data

With your scraper extracting the desired information from each product page, you‘ll want to store this data somewhere for further analysis and use.

JSON is a good standard format to save scraped Amazon data. From there it can be loaded into a database or opened in Excel.

For larger datasets, a database like MongoDB is more efficient than loading into spreadsheets.

S3 buckets on cloud platforms like AWS provide affordable storage for scraped datasets that can grow into the terabytes.

Step 4: Clean and structure the data

Raw scraped data inevitably contains inconsistencies, formatting issues, missing values etc.

To create a usable Amazon product database:

  • Remove duplicate entries
  • Standardize pricing into a single numeric format
  • Validate and format fields like ASINs and product URLs
  • Split combined fields like ratings count vs average rating
  • Fill in or remove missing fields

Use Python‘s Pandas library or OpenRefine for data cleaning and transformation scripts.

For easy analysis in Excel, ensure your scraped Amazon product data:

  • Has one product per row
  • Uses separate columns for all attributes (title, rating, price etc.)
  • Removes extra spaces, commas and characters from cells

Well structured data makes it easier to sort, filter and pivot your Amazon dataset to uncover insights.

Step 5: Analyze and monitor the Amazon data

Now the fun part… what can you do with a database of structured Amazon product data?

Price tracking – Chart prices over time to identify discounts and trends.

Competitor monitoring – Check competitors‘ prices and inventory levels.

Amazon SEO – Identify high ranking products in your niche.

Market research – Filter top rated and best selling products by category.

Demand forecasting – Predict sales based on review counts and ratings.

Keyword research – Analyze product titles, features and descriptions.

Regularly re-scraping and updating your Amazon dataset enables all kinds of important ecommerce analytics.

Advanced tips for scraping Amazon effectively

Here are some pro tips for avoiding blocks and extracting data from Amazon efficiently:

  • Limit request rate – Scrape responsibly and avoid bombarding servers.

  • Monitor performance – Check for rate limiting and CAPTCHAs.

  • Use proxies – Rotate different IPs to distribute requests.

  • Randomize user agents – Use a variety of desktop and mobile headers.

  • Retry failed requests – Gracefully handle errors and retries.

  • Parallelize scraping – Open multiple connections to speed up data extraction.

  • Apply filters – Only scrape relevant data to minimize processing.

  • Paginate results – Step through each product listing page.

  • Use caching – Save scraped data temporarily to avoid re-scraping.

Get the most out of your scraper and build robust, efficient workflows for extracting Amazon data.

While scraping Amazon product data is legal in most cases, you should still follow responsible web scraping practices:

  • Respect robots.txt – Avoid scraping pages blocked by robots.txt

  • Check Terms of Service – Confirm your use case is permitted.

  • Limit scrape frequency – Spread out requests over longer durations.

  • Scrape selectively – Don‘t extract more data than necessary.

  • Attribute data – Credit Amazon as the source.

  • Protect data – Store and handle data securely.

It‘s smart to consult experienced legal counsel before any large scale web scraping project. But adhering to reasonable limits and ethics will go a long way in keeping your Amazon scraping above board.


Scraping product listings on Amazon provides access to a goldmine of ecommerce data. Following the steps outlined in this guide will help you successfully extract information from Amazon for research, monitoring, data science applications and more – all without needing the official Amazon Product API.

As always when web scraping, be sure to scrape ethically, monitor performance, and employ techniques like proxies to avoid blocks. With some technical skill and scraping best practices, Amazon‘s catalog is yours for the taking.

Now you have all the tools and knowledge needed to scrape and harness Amazon product data at scale. So go forth and scrape!

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