How to Scrape Amazon Product Data & Reviews in 2024

With e-commerce sales expected to reach $6.4 trillion by 2024, online marketplaces like Amazon offer a treasure trove of data for retailers, marketers, and businesses. As the largest e-commerce company in the US, accounting for 41% of e-commerce retail sales, Amazon is a particularly rich data source.1 Web scraping services enable users to automatically collect publicly available data from e-commerce websites like product information, pricing, reviews and more. However, scraping data from large, well-protected sites like Amazon raises important legal and ethical considerations.

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In this post, we‘ll explore methods for scraping Amazon product and review data legally and ethically. I‘ll be focusing on data collection services designed specifically to meet Amazon‘s terms of service. Let‘s take a look at which Amazon scraper is the best fit depending on your specific requirements.

Please note this post is for informational purposes only and should not be construed as legal advice. For any data scraping project, I‘d recommend consulting with legal counsel.

Scraping Amazon Product Data: A Step-by-Step Guide

You can extract Amazon product data from product search pages or individual product pages. Search results contain basic info like product rating, pricing, image URL. For basic product attributes, it‘s often more efficient to scrape search pages rather than each product page, reducing the number of requests required. Here are the main steps:

Identify target data: To understand Amazon URL structure, conduct a product search on Amazon.com. The URL contains the search term and page number:

https://www.amazon.com/s?k=[keywords]&page=[page_number]

[keywords] is the search term used
[page_number] is the page number

You can also identify products by their Amazon Standard Identification Number (ASIN). This is a 10-character alphanumeric unique ID located on product pages. However, systematically collecting ASINs without permission can violate Amazon‘s terms. Use Amazon Product Advertising API to legally access product details like URLs.

Set up scraping environment: Install Python if you don‘t already have it. Install libraries like Beautiful Soup, Selenium, and Requests for scraping.

Since Amazon uses HTML and JavaScript, a library like Selenium that handles dynamic content may be needed.

Inspect target page: Right click the target page and select "Inspect" to open developer tools. Identify HTML tags containing your target data. You‘ll only see HTML elements/attributes here. For JavaScript-loaded data, check Network tab for all requests made, including JS files and AJAX requests.
Write script: Request the product page by URL or ASIN. Parse HTML using BeautifulSoup to find your target data.
Handle pagination: Amazon results span multiple pages. Identify and request next page link to continue scraping.

Use a Web Scraper Bot Over RPA

There are several options for scraping sites, including web scraper bots and RPA bots. How can RPA automate web scraping?

Identify target URLs to scrape
Scroll through multiple pages and extract relevant data
Write code (e.g. Python) or use an extension to extract specific data like text, images, video
Transform into required format

However, RPA isn‘t ideal for scraping pages with "load more" buttons. Some sites load data in parts, requiring clicking a button to see more products. The bot will stop extracting data at the end of the initial page load.

What Amazon Data Can You Scrape?

Monitoring competitors: With its vast selection, Amazon offers the largest range of products online. Competitors‘ product listings can be extracted and monitored regularly for availability, vendors, ratings etc.
Product reviews: Scraping and analyzing your product reviews helps uncover customer pain points and behavior. You can also scrape competitors‘ reviews to benchmark product launches or improvements.

For more on review scraping, see our Ultimate Guide to Review Scraping.

Bright Data‘s Data Collector scrapes Amazon product data and reviews automatically. It collects and parses real-time product and category data like descriptions, ASINs, sellers, pricing, features, ratings, and reviews.

Prices: Monitoring competitors‘ pricing is key for optimal pricing and revenue growth. Retailers like Amazon and eBay use dynamic pricing, adjusting prices in response to supply, demand, competition etc. Web scrapers can extract real-time public data from eCommerce sites in a structured, analysis-ready format.

For example, Amazon Personalize allows customized digital stores with personalized product recommendations, direct marketing, and re-ranking. This enables personalized product suggestions and pricing based on purchase history, creating a dynamic pricing environment for Amazon sellers.

The best data collection approach depends on your specific needs. However, for quick, cost-efficient access to data, ready-made datasets provide immediate availability and reduce preparation time. Bright Data offers ready-made Amazon datasets with reviews, prices, products, sellers, and more.

Use Cases for Scraped Amazon Data

Price Comparison

Web scraping allows e-commerce companies to regularly extract relevant Amazon data, like weekly/monthly competitor pricing data. This enables competitive pricing, especially during peak seasons, avoiding lost sales and competitive disadvantage.

However, gathering competitor pricing for hundreds of products is challenging given Amazon‘s dynamic site structure. Here are the main steps for scraping price data:

Access the target site at scheduled times
Track site activity to monitor price changes
Scroll and extract pricing data, outputting to spreadsheets or databases

Oxylabs provides an Amazon scraper API to scrape and parse different Amazon page types like listings, products, and categories. You can extract real-time product data, search results, and public pricing info.

Demand Forecasting

Scrapers can harvest real-time and historical Amazon data to track product interest and estimate demand. Demand forecasting improves supply chain management with real-time demand analysis, ensuring product availability and proper inventory management.

This avoids issues like cash-in-stock, where products go unsold longer than expected. In these cases, companies will list products below competitors to sell inventory, avoidable with accurate demand forecasts.

Improving Product Profile

Businesses can extract product details like pricing, descriptions, ratings, ranks, and reviews from Amazon. Monitoring product reviews helps identify strengths and weaknesses, useful for competitive analysis.

Tracking competitors‘ reviews provides insights into product positioning and market trends. Effective competitor analysis involves:

List major competitors in your market
Gather intel like company size, locations, unique selling proposition
Identify similarities and differences in target customers
Research competitor products:
- What are they selling?
- What pricing models do they use?
- How do they market their products?

Bypassing Amazon Bot Protection

Scraping Amazon poses some key challenges for scrapers:

Bulk scraping: Separate requests are needed for each page when scraping large amounts of data. Too many concurrent connections can overload servers and slow site performance. It‘s important to follow guidelines and keep concurrent connections at reasonable levels.
Rate limiting: Amazon rate limits restrict requests from a single IP address in a time period. Adding delays between requests or using rotating proxies can prevent hitting rate limits.
Anti-bot measures: CAPTCHAs and other anti-scraping mechanisms aim to prevent automated scraping activities like web harvesting. Scrapers may need to incorporate techniques to emulate human behavior, like random wait times and rotating user-agent strings.
JavaScript rendering: Much of Amazon‘s content is dynamically loaded via JavaScript. Some scraping services use headless browsers to handle JavaScript rendering.
IP blockers: To avoid blacklisting, use dynamic vs static IPs. Proxies are another option for easier scraping. Most sites limit scraping with a crawl rate, restricting requests from a single IP. Integrating a proxy server assigns a different IP per request.

Learn more about proxy server types and benefits here.

Complex site structure: Scrapers locate data based on HTML and JavaScript elements. Changes to site content and features also change the underlying structure. Scraping new designs can prove highly complex.

Additional Web Scraping Resources

For help choosing the right web scraping tool, see our data-driven web scraper list. Or reach out to discuss your project!

Chevalier, S. (Jul 10, 2023). “Market share of leading retail e-commerce companies in the United States”. Statista. Retrieved July 15, 2023