The Complete Guide to Product Review Scraping: Amazon, Walmart, Best Buy & Beyond

Introduction

The most significant factor affecting modern shoppers' decisions is online product reviews. Customers who want to purchase anything—from electronics to household items—search for other customers' thoughts and firsthand experiences before buying. Thus, millions of product reviews across the major e-commerce sites (Amazon, Walmart, Best Buy, etc.) create an extensive database of consumer information.

Unfortunately, it is not practical or feasible to manually read through vast numbers of customer reviews, ratings, and metadata to generate actionable insights for businesses, marketers, researchers, and data analysts. Therefore, product review scraping is essential. Through review scraping, organizations can automate the collection of large numbers of customer reviews, ratings, and their respective metadata, turning previously unstructured feedback into structured, valuable data.

This product review scraping guide provides an overview of how review extraction is performed on the major eCommerce platforms (Amazon, Walmart, and Best Buy), the technical and legal considerations related to scraping reviews, and the tools you can use to collect information. The guide goes on to clarify some primary uses of product review scraping for analysis, such as increasing market competitiveness, effectively tracking sentiment for a given product/brand over time, gathering feedback to improve products, and conducting market research.

What is Product Review Scraping?

Product review scraping is the process of collecting automated product reviews that contain customer opinions, ratings, comments, etc., about a product(s) sold through an e-commerce site. The information obtained from product review scraping provides additional intelligence into what consumers are looking for in the marketplace. Businesses use this information to understand their customers' needs, which can help them improve their marketing and competitive positions.

Today, with the rise of social media, consumers have more access to reviews than ever before when making purchasing decisions. By using scraping methods, businesses can gather thousands to millions of reviews almost instantaneously, allowing them to gain insights into product-related trends, product issues, customer satisfaction levels, and how pricing impacts customer purchasing behavior.

Retailers use product review scraping to establish benchmarks for product performance. Manufacturers use product review scraping to inform product design. Marketers use product review scraping to develop more effective messaging for their customer base. Without effective methods for quickly accessing product review data, businesses often make business decisions based upon an inadequate or skewed understanding of their customers.

How Does Product Review Scraping Work in Practice?

Product review scraping uses automated web requests to extract information from HTML. Review scrapers look for specified items such as the reviewer’s name, score, review details, review date, verification status, and how many other people found the review helpful.

Product: first find URLs of the product(s) or the category page(s); from there, a scraper can move through pages by navigating pagination, loading dynamic content, and using rules or selectors to read the page. More sophisticated scrapers can also handle loading JavaScript content, rotating IP addresses (via proxy servers), handling CAPTCHA, and adhering to website rate limits.

When reviews are gathered, the raw data is cleaned and normalized so that there is one copy of each duplicate review, and the text is standardized and consistent in format. The reviews can then be exported from the complete data set in different formats (CSV, JSON, or directly to a Database), so the company can import the data into its data analysis tool(s).

Modern review scraping technology enables companies to obtain current review data on an ongoing basis, rather than just a snapshot at a single point in time.

Read also: The Ultimate Guide to Scraping eCommerce Product Reviews

What Makes Amazon Review Scraping Challenging?

Amazon has an enormous customer base and is therefore one of the most significant sources of online product reviews globally. However, its robust anti-bot technologies designed to deter scraper activity make it very difficult to scrape.

In addition to these strong anti-bot systems, the sheer volume of reviews available on Amazon adds another challenge to scraping them. An electronic device can have tens of thousands of reviews spread throughout hundreds of pages of product review results. Scrapers will also have to paginate through reviews while maintaining session state to avoid being blocked or flagged for scraping Amazon.

In addition, metadata from Amazon reviews is included in several different ways: reviewer star rating "distribution", verified purchase tag, and helpful vote. The scraper must continually adapt its parsing strategy to account for frequent changes in the site design and extract the most essential information from Amazon reviews.

If you can successfully scrape Amazon reviews consistently over time as part of both new and ongoing research and analysis, and you follow sustainable scraping protocols, you will discover Amazon review data to be a handy tool to gain insight into consumer sentiment, relative competitive performance, and global product demand trends into the future.

How is Walmart Review Scraping Different from Amazon Review Scraping?

Collecting Walmart review content requires different technical and strategic approaches compared to collecting reviews for other retailers. This is because Walmart uses APIs to pull most of its reviews via dynamic requests rather than standard HTML.

The API-driven structure can be an advantage for scrapers who know how to intercept and replicate Walmart's API calls to gather review content; however, Walmart implements rate limiting and behavioral tracking, which will detect automated access to its review content.

Another major differentiator between Walmart reviews and Amazon reviews is the presence of Verified Purchase indicators, which are relatively rare in Walmart reviews but provide significant insight into price sensitivity and in-store experience. Furthermore, Walmart consumers have a different demographic profile than Amazon consumers, thereby providing businesses analyzing Walmart review content the opportunity to gain insights into multiple demographics.

In addition to the differences above, another substantial differentiator when scraping review content for a specific product sold on Walmart and Amazon is the ability to compare the performance of the same product on Walmart with that on Amazon. This comparison will enable businesses to quantify differences in consumers' perceptions of pricing, logistics, and customer expectations across both marketplaces.

What Should You Know About Best Buy Review Scraping?

The reviews from Best Buy focus on electronics, appliances, and technology manufacturers. Most reviews provide much more detail about the performance, durability, and use of the latest technology products.

Scraping reviews from Best Buy can be moderately complex because the platform has structured review endpoints and monitors user access patterns when collecting reviews. Hence, a scraper needs to manage both how often it requests reviews and which session headers it uses for those requests.

Another highlight of Best Buy reviews is that they typically include pros and cons for the products reviewed, along with a star rating and recommendation indicators, which can provide a great deal of information to product developers and support competitive analysis for high-value items.

Best Buy also tends to attract more advanced/knowledgeable customers, resulting in stronger signals from customers on product views versus competitors for brands that use advanced technology. Scraping Best Buy reviews will help brands better understand how customers with high levels of competency perceive their products compared to competitors.

What Other Platforms Can You Scrape Reviews From?

Companies other than Walmart and Amazon, such as Google Shopping and eBay, have distinct amounts of review data. With multiple sources of review data from different stores, businesses can generate a more complete and unbiased assessment of a product's overall marketing effectiveness. That is especially true when comparing reviews from marketplace sellers vs. reviews from the sellers' own D2C online stores.

In addition to the domestic marketplaces available through other US e-commerce platforms, companies that operate internationally may also provide access to essential review data about their products based on the local region. Having access to localized reviews allows international companies to identify local cultural differences in how customers speak about the product, how to communicate effectively with consumers using the local language, and what regional standards and specifications consumers expect from specific products.

By developing a data-scraping strategy that incorporates multiple e-commerce marketplaces, companies will reduce their reliance on a single review data provider, yielding a more reliable set of insights into their products' reviews.

Is Product Review Scraping Legal and Ethical?

Product review scraping is lawful depending upon: (1) where the scraping occurs (the jurisdiction), (2) the terms and conditions of service of the website from which the data is being scraped, and (3) how the scraped product review data is used.

Generally speaking, scraping publicly available data is legal in many jurisdictions worldwide; however, if you scrape data in violation of a website's terms and conditions, you may be held to contractual obligations.

Ethical scraping practices include: respecting a website's robots.txt file (if one exists), not placing excessive load on a server, and not scraping any personal or sensitive information. The scraped data should be used only for analysis, not for redistributing the copyrighted content of another individual.

Businesses should always ensure compliance with all applicable data protection laws (including GDPR and CCPA) when scraping product reviews that contain consumers' personal identifiers.

A correctly set up and operated scraping operation can help balance the need to fulfill business requirements, comply with the law, and act ethically, thereby reducing potential liabilities while creating value.

What Tools and Technologies Are Used for Review Scraping?

Review scraping is a complex process that involves several technologies, such as HTTP Clients, Headless Browsers, Proxy Networks, and Parsing Libraries. You can create custom scrapers or use professional data extraction tools to scrape reviews. For example, websites that rely heavily on JavaScript often need a Headless Browser to display their content correctly. They may also use rotating proxies to hide their IP addresses, which helps avoid getting flagged for 'Suspicious Traffic.' If a site uses CAPTCHA, users need a separate service to solve it before accessing the site.

When your company decides to scrape reviews, the process usually includes Extraction, Cleaning, Storage, and Analysis. Many companies integrate the scraped reviews into their Business Intelligence (BI) tools, dashboards, or Machine Learning Models.

How is Scraped Review Data Analyzed and Used?

After the review data is collected, it needs to be evaluated and made actionable through analysis and the application of techniques such as Natural Language Processing (NLP), which can extract sentiment, identify keywords, and the topics associated with the reviews.

Using review data, businesses can make changes to their products and listings and price them competitively, whilst also allowing marketers to provide customers with a better experience by refining their marketing messages to focus on the benefits customers find most valuable.

In addition to improving product offerings and marketing messages, review data can be used in conjunction with pricing and sales data to develop competitive analyses and gain insights into areas where reviews have identified strengths, weaknesses, or gaps in product offerings, and how they may be leveraged for further business success!

What are the Best Practices for Scalable Review Scraping?

Planning & maintaining scalable review scraping is essential. Both the scraper and the monitoring systems should be modular and allow simple updates when the website's structure changes. Failure/error notifications and data anomalies should be flagged early in the process for both systems.

Furthermore, request rates for scraping should be controlled to avoid losing access due to excessive requests, and you should diversify proxy strategies when scraping. In addition, data validation should validate the accuracy & completeness of your scraped data.

Ensure security and compliance are built into your scraping process. It will help you create a complete plan. The presence of proper logging, access controls, and data governance will help reduce the operational risk you face.

If an organization treats review scraping as part of their long-term data strategy, rather than only once in a blue moon, they will achieve greater reliability and a better return on investment.

Conclusion

Scraping product reviews plays an integral role in a company's data-driven approach to its business. With the abundance of product reviews on websites such as Amazon, Walmart, Best Buy, and others, these companies aggregate this information to help businesses understand how their customers view their products.

Successful scraping of product reviews requires a business to use the correct tools and to be aware of the legal and ethical implications associated with scraping and using the information obtained to gain insight into what products are being purchased and how they can sell those products more effectively.

Companies that lack in-house resources or experience to be successful at scraping product reviews often need to use specialized companies to assist with the accurate and compliant collection and analysis of product reviews. With ReviewGators, for example, a business can unlock the full potential of product review data without dealing with operational issues. With the continuous growth of consumer demand and the increasing need for innovation in product and marketing strategy, scraping product reviews will remain an ongoing source of competitive advantage for companies that can act quickly and respond to consumers.